Calreticulin is an ER protein that gets translocated to the cellular membrane after RT. Calreticulin is essential for efficient uptake of dying tumor cells by antigen-presenting cells [182].


Chemokines are a frequent part of DAMPs. Chemokines, such as C-X-C motif (CXC) ligand 16, 10, and 19 (CXCL16, CXCL10, and CXCL9), are known for being released after radiation exposure; they trigger differentiation of immune cells, and stimulate immune modulators such as Interferons, Tumor necrosis factor α, and Interleukin 1β [162, 183–185].


In summary, radiation increases the immunogenicity of tumors by simultaneously promoting the release of tumor antigens, neoantigens, and DAMPs. This radiation-induced immunogenicity may counteract the progression of tumors that have escaped immune surveillance, especially if an abscopal effect can be triggered.


To reproduce the radiation-induced abscopal effect has been challenging as its underlying biological mechanisms are not fully understood. However, some crucial cellular signaling pathways have been identified alongside the IR doses that appear to be fundamental for their occurrence.
The accumulation of cytosolic double-stranded DNA fragments (dsDNA) is usually a sign of microbial infection and alerts the host’s innate immune system to initiate a defense response. Interestingly, the accumulation of dsDNA after RT has been shown to be an essential step in the abscopal response. Mitochondrial DNA and dsDNA are recognized by cyclic GMP-AMP synthase (cGAS), which produces messengers that bind and activate the transmembrane protein Stimulator of interferon genes (STING), triggering a robust innate immune response [186]. In particular, activation of the type I interferon signaling pathway (IFN-1) and production of Interferon β cytokines by cancer cells trigger the accumulation of basic leucine zipper ATF-like transcription factor 3 (Batf3)-dependent DCs, which are required for the maturation of anti-tumor CD8+ T cells.

The irradiation dose and fractionation schemes are crucial for the induction of abscopal effects. With increasing radiation dose, more dsDNA fragments accumulate in the cytoplasm of cancer cells. However, the increased radiation dose of over 12 Gy also triggers the activation of three prime repair exonuclease 1 (Trex1), which degrades dsDNA and prevents an immune response. Interestingly, Interferon β production can be enhanced with repeated radiation doses that do not trigger Trex1 [186]. Radiation regimens that have been shown to be effective in preclinical and clinical studies are 3 × 8 and 5 × 6 Gy.

Transforming growth factor beta (TGF-ß) is a potent immunosuppressive cytokine in established tumors that impairs the antigen presentation function of DCs, prevents T cell priming and thus inhibit the abscopal effect [164].

TGF-β expression increases in irradiated tissues. However, the abscopal effect can be restored if TGF-β neutralizing antibodies are administered during RT. Current research is investigating if the use of dual-function drugs that simultaneously target TGF-β and PD -L1 in combination with RT can be a good strategy to overcome tumor immune escape [187].


The accumulation of cytosolic double-stranded DNA fragments (dsDNA) is usually a sign of microbial infection and alerts the host’s innate immune system to initiate a defense response. Interestingly, the accumulation of dsDNA after RT has been shown to be an essential step in the abscopal response. Mitochondrial DNA and dsDNA are recognized by cyclic GMP-AMP synthase (cGAS), which produces messengers that bind and activate the transmembrane protein Stimulator of interferon genes (STING), triggering a robust innate immune response [186]. In particular, activation of the type I interferon signaling pathway (IFN-1) and production of Interferon β cytokines by cancer cells trigger the accumulation of basic leucine zipper ATF-like transcription factor 3 (Batf3)-dependent DCs, which are required for the maturation of anti-tumor CD8+ T cells.


The irradiation dose and fractionation schemes are crucial for the induction of abscopal effects. With increasing radiation dose, more dsDNA fragments accumulate in the cytoplasm of cancer cells. However, the increased radiation dose of over 12 Gy also triggers the activation of three prime repair exonuclease 1 (Trex1), which degrades dsDNA and prevents an immune response. Interestingly, Interferon β production can be enhanced with repeated radiation doses that do not trigger Trex1 [186]. Radiation regimens that have been shown to be effective in preclinical and clinical studies are 3 × 8 and 5 × 6 Gy.


Transforming growth factor beta (TGF-ß) is a potent immunosuppressive cytokine in established tumors that impairs the antigen presentation function of DCs, prevents T cell priming and thus inhibit the abscopal effect [164].


TGF-β expression increases in irradiated tissues. However, the abscopal effect can be restored if TGF-β neutralizing antibodies are administered during RT. Current research is investigating if the use of dual-function drugs that simultaneously target TGF-β and PD -L1 in combination with RT can be a good strategy to overcome tumor immune escape [187].


The tolerance of normal tissues to RT is the limiting factor in delivering a radiation dose high enough to eradicate tumor cells [188, 189]. The dose delivered to the target structure is limited by the dose constraints to the organs at risk (OAR), which are in close proximity to the tumor. In the case of external RT, standard recommendations for OAR doses have been issued based on the volumes irradiated and are regularly updated to reflect new practices.


Despite ongoing technical and technological advances, the planning target volume (PTV) necessarily contains a volume of normal tissue for several reasons. First, the target volume, which receives the prescribed dose, is always larger than the actual tumor volume. Indeed, this volume must take into account the visible tumor volume (gross tumor volume, GTV), the possible microscopic extension of the tumor (clinical target volume, CTV), the movements of the different volumes (internal target volume, ITV), small errors in patient set-up and technical precision, all together making up the PTV. Secondly, the tumor contains normal tissues such as soft tissues and blood vessels, which receive the entire prescribed dose. Finally, especially for external beam RT, the radiation beam inevitably passes through normal tissue, depositing doses that may be clinically relevant. Effective RT is therefore necessarily associated with a risk of adverse effects. Side effects may occur during RT or a few weeks after treatment (acute effects). In the long term, late effects, which may occur months or years after RT, are the most critical because they are chronic, disabling, painful, and most often irreversible.


Limiting adverse events to reduce morbidity and optimize the therapeutic index of RT remains a priority in the fight against cancer. To this end, NTCP modeling is widely used in treatment planning as a tool to differentiate treatment plans [11]. In future, cellular and animal models of radiotoxicity aimed at understanding the sequence of molecular and cellular events that drive the pathogenesis of early and late normal tissue radiation injury should enable the development of tools to reduce the impact of RT on normal tissues [190].


Acute effects of radiation are observed in days to weeks after exposure.

The time scale of clinical manifestation of most acute responses is independent of the radiation dose and related to the proliferative rate of injured cells.

Acute effects are transient and reversible.

Typical acute responding normal tissues are: intestine, mucosa, skin, hair follicles, bone marrow.


Acute effects of radiation are observed in days to weeks after exposure.


The time scale of clinical manifestation of most acute responses is independent of the radiation dose and related to the proliferative rate of injured cells.


Typical acute responding normal tissues are: intestine, mucosa, skin, hair follicles, bone marrow.


Acute responses are primarily observed in tissues with rapid cell renewal where cell division is required to maintain the function of the organ. In these renewing tissues, physiological cell loss occurs constantly from the post-mitotic tissue compartments while actively cycling cell populations in the germinal parts of the tissue proliferate to replenish them. The radiation response is related to death of critical cell populations such as the stem cells in the crypts of the small intestines, in the bone marrow, or in the basal layer of the skin. When sufficiently large numbers of critical cell populations are affected, cell production capacity is no longer able to compensate for the physiologically occurring cell loss, leading to hypoplasia and cell depletion. For this reason, the time scale of clinical manifestation of most acute responses is independent of dose and instead reflects the rate of loss of functional cells and the demand of proliferation of the supporting stem cells of the different tissues (Box 5.13).


In general, acute responses are transient, but sensitive to the overall radiation treatment time. Recovery may occur by rapid repopulation from the surviving stem cell compartment or by recruitment of stem cells from neighboring sites (non-irradiated/damaged areas). Hence, the latent period of manifestation of tissue reactions is specific, depending on the cell type and its proliferation rate (Table 5.12) as well as on its intrinsic sensitivity to radiation and capacity to repair damaged DNA.Table 5.12
Tissue and target cell-specific latency times

Tissue

Target cells

Latency times (after single exposure)

Epidermis

Basal cells

3–4 weeks

GI-tract

Crypt cells

<1 week

Bone marrow

Hematological stem cells and precursor cells

0.5–2 weeks

Testes

Spermatogonial stem cells

2–4 weeks


Apart from the number of sterilized cells, several other non-lethal mechanisms like proliferative impairment and disturbances in molecular cell signaling play a role in the acute tissue reaction to radiation. For example, the release of 5-hydroxytryptamine by mast cells have been shown to be responsible for several early clinical reactions such as erythema following irradiation of the skin and nausea/vomiting following irradiation of the intestines. Immunological reactions associated with local and systemic release of cytokines have also been demonstrated. In the hematopoietic system, apoptosis plays an important role in acute normal tissue response. Apoptotic death directly following irradiation, particularly of the lymphoid lineage, causes additional cell loss on top of the physiological cellular loss rate of circulating cells and explains earlier onset particularly for the lymphoid lineage. Furthermore, the typical tissue or organ architecture has a principal role in the response to irradiation. Threshold irradiation doses (Table 5.13) are often dependent on the irradiated normal tissue volume.Table 5.13
Threshold radiation doses for a selected group of tissues. Estimate of threshold doses for an approximate 1% incidence of morbidity for early and late tissue reacting human tissues and organs following acute exposure to radiation (modified after Table 4.4 from [191])

Organ/tissue

Threshold dose (Gy)

Biological effect

Latency period

Testis

~0.10

Temporary sterility

3–9 weeks

Testis

~6

Permanent sterility

3 weeks

Ovaries

~3

Permanent sterility

<1 week

Bone marrow

~0.5

Depression of hematopoiesis

3–7 days

Skin (large areas)

<3–6

Main phase of skin reddening

1–4 weeks

Skin (large areas)

5–10

Skin burns

2–3 weeks

Skin

~4

Temporary hair loss

2–3 weeks

Skin (large areas)

10

Late atrophy

>1 year

Skin (large areas)

10

Telangiectasia at 5 years

>1 year

Eye

~0.1 per 5 yearsa

Cataract (visual impairment)

>20 years

Brain

0.1–0.2

Cognitive defects infants <18 months

Several years

Carotid artery

~0.5

Cardiovascular disease

>10 years

Kidney

~7–8

Renal failure

>1 year

a An equivalent dose limit for the lens of the eye of 20 mGy/year is recommended for workers. The dose should be spread over defined 5 years periods, with no single year exceeding 50 mGy [192]


The tissue architecture is generally organized in the so-called functional subunits (FSUs). Some tissues are built of anatomically demarcated FSUs, like nephrons in the kidney, liver- and lung lobules. These types of organs—with a parallel arrangement of FSUs show large reserve capacity. Non-radiation exposed volumes of the organ can take over the function of the damaged tissue. Other tissue types like the spinal cord and mucosa do not show clear anatomical demarcation in FSUs. In such serial arranged FSUs tissues, radiation injury to a small tissue volume can result in function loss of a larger volume or even the whole organ. The threshold radiation dose can be defined as the safe, tolerated, dose below which no tissue-specific reaction occurs. This particular dose is difficult to determine [191]. In general, it is the estimated dose that is required to cause a typical tissue reaction in 1% of the exposed individuals (ED1) relative to non-irradiated controls. To be noticed is that radiation doses <ED1 could also induce biological effects above baseline levels in non-irradiated, age-matched individuals, i.e., above natural incidence. Table 5.13 lists threshold doses and latency times for tissue reactions to a single radiation exposure for a selected number of healthy tissues and organs.


Late effects of radiation occur months, years, or decades after radiation therapy.

Late effects are progressive and irreversible.

The latent phase of chronic reactions is inversely proportional to the radiation dose.

The late effects are based on an interactive response of parenchymal cells, vascular endothelium and fibroblasts, with a contribution from immune cells, especially macrophages, as well as other cells.

Tissues and organs are affected by atrophy, fibrosis, and/or necrosis, which can severely impair their functions and lead to a loss of function.


Late effects of radiation occur months, years, or decades after radiation therapy.


The latent phase of chronic reactions is inversely proportional to the radiation dose.


The late effects are based on an interactive response of parenchymal cells, vascular endothelium and fibroblasts, with a contribution from immune cells, especially macrophages, as well as other cells.


Tissues and organs are affected by atrophy, fibrosis, and/or necrosis, which can severely impair their functions and lead to a loss of function.


The classification that distinguishes between early and late effects is based exclusively on the time of first diagnosis of pathological changes, with a threshold arbitrarily set at 3 months after the start of RT [5, 193]. Unfortunately, there is no general biological rationale for this threshold, whereas a classification of early and late effects is clinically relevant and essential for comparing studies. Nevertheless, there are specific biological characteristics of early and late effects that distinguish them (Tables 5.13 and 5.14). As discussed in Sect. 5.13.2, the early symptoms most often appear in highly proliferative tissues, such as the intestinal mucosa, epidermis, and bone marrow. In these tissues, irradiation causes a progressive but potentially reversible depletion of cells by preventing their physiological renewal, associated with a direct or indirect radiation-induced inflammatory reaction. In contrast, late side effects can affect all tissues. The pathogenesis leads to fibrosis, atrophy, vascular damage as well as other detrimental side effects on normal tissues such as hormonal deficiencies, infertility, and second malignancies (discussed in Chap. 7). Late side effects are chronic, progressive, in most cases irreversible and their severity tends to increase over time (Fig. 5.24 and Box 5.14).Table 5.14
Typical characteristics of acute and late responding normal tissues and organs. Please note that early as well as late effects could occur in the same tissue or organ

Early reactions

Late reactions

Cell turnover of target cells

High

Low

Latency

<90 days

>90 days

Time factor

Shorter time

No influence of time

>Damage

Clinical course

Transient

Irreversible

Fractionation sensitivity

Low

High

α/β ratio

~10 Gy

~3 Gy

Examples:

Mucositis dermatitis bone marrow depletion (tumor response)

Myelopathy

Intestinal fibrosis

Telangiectasia

A diagram of the late or chronic normal tissue responses from months, years, to decades for repair and remodeling cycles and chronic inflammation with 3 stages from vascular and parenchymal damage to dysfunction or loss of function.

Fig. 5.24
Radiation-induced chronic damage to healthy tissues. Late damage develops within months to decades post RT and may concern all normal tissues. Successive cycles of tissue remodeling and repair, together with chronic inflammation induce vascular and parenchymal damage leading to tissue atrophy/fibrosis/necrosis compromising organ function


A diagram of the late or chronic normal tissue responses from months, years, to decades for repair and remodeling cycles and chronic inflammation with 3 stages from vascular and parenchymal damage to dysfunction or loss of function.


The mechanisms that lead to these effects are more complex than for acute effects. They involve organ-specific changes in parenchymal cells, including cell death and alterations in cellular metabolism. Consequential late effects from severe acute reactions may contribute to chronic damage due to unresolved regeneration of rapidly proliferating tissues which contributes additional damage to connective tissue and endothelium [194].


The pathogenesis leading to radiation-induced fibrosis is a result of fibroblast differentiation into myofibroblasts, proliferation of surviving fibroblasts and extracellular matrix and collagen deposition. Seen as wound healing that goes wrong, this pathological process plays a key role in the development and expression of most late effects. Atrophy is caused by the loss of fibroblasts and collagen reabsorption. Examples of fibrotic-atrophic response include hardening and shrinkage of an irradiated breast, or strictures and malabsorption of irradiated small intestine. Vascular damage is caused by either small vessel dilation or constriction as well as losses of the vascular endothelial cells from small blood vessels and capillaries. Vascular damage results, for instance, in skin telangiectasias, bleeding, ischemia with intestinal perforation and fistula formation. The immune system also contributes significantly to the tissue response through the involvement of macrophages and mast cells, which interact with other cells in the irradiated tissue and other organs through the release of cytokines and growth factors [195–198]. More generally, the response of a tissue is mediated by different types of cells such as inflammatory, stromal, endothelial, and parenchymal cells that actively communicate through the release of cytokines, chemokines and growth factors, and/or the activation of molecular pathways downstream of these messengers. Altogether, these effects lead progressively to parenchymal damage and potentially to loss of organ function in the irradiated volume.


The tissues of cancer survivors treated with RT still bear the traces of RT to varying degrees. Although patients are usually asymptomatic, all irradiated patients, especially those with late effects, have a common histological feature of radiation-induced fibrosis and atrophy. Fibrosis predominates in the breast, skin, small intestine, lungs, kidneys, and liver, while atrophy and necrosis predominate in the later stages after RT alone or in combination with surgery and local trauma to bone (osteoradionecrosis of the mandible, ORN), nerve, or brain. The clinical severity correlates with the extent of the underlying pathophysiological process, which is usually invisible and often depends on the level of parenchymal cell loss.


It is often mentioned that 5–10% of patients, and sometimes up to 20% for the treatment of pelvic malignancies, including prostate, rectal and cervical cancer, develop late side effects. However, some authors believe that the rates of patients with late side effects may be greatly underestimated [193]. For example, in the case of abdominal or pelvic cancers, more than half of patients would suffer from some form of chronic bowel dysfunction [199]. Most of the effects observed today were caused several years ago by RT techniques that are less used today (2D- and 3D-CRT), and which are progressively being replaced by more precise and more efficient techniques and technologies (IMRT, SBRT and their derivatives, hadrontherapy, and maybe FLASH RT in the future). It is therefore likely that the landscape of side effects will be completely different in a few years.


As cancer detection and management continue to improve, there is an increase in the number of long-term cancer survivors in the more economically developed countries. For example, in the USA, the 5-year survival rate has increased from 49% in the period 1975–1977 to 67% in the period 2010–2016 (American Cancer Society, Cancer Facts and Figures, Atlanta, Georgia, 2021). On the other hand, given that about half of patients are treated with RT, it can be estimated that several tens of thousands of patients will develop side effects each year in this country. Worldwide, with more than 19 million new cancer cases in 2020 (and about 30 million expected in 2040), and an estimated 5-year prevalence of more than 50 million [200], the number of patients developing side effects affecting their quality of life is expected to be in the millions each year. Beyond the fact that the late side effects of RT still limit the effectiveness of this treatment, these figures reveal a real public health concern facing the public authorities and the medical profession.


Radionecrosis is a late toxicity phenomenon with the occurrence depending on radiation dose, the tissue affected and a number of site-specific risk factors; as treatment options are scant, preventive measures should be facilitated by providing the treatment team with the forecasted 3D RT isodose curves (Box 5.15).


Radiation accidents and therapy have shown that, in principle, all human tissues can suffer from necrosis as a late toxicity as result of progressive ischemia of irradiated tissues in the context of chronic inflammation. Pathologic samples show necrosis with fibrinous exudates and dystrophic changes of the vessels in the exposed tissues. In daily practice, radionecrosis most commonly involves bone (head and neck (jaws-mastoïd-temporal bone-larynx-cartilage)/femoral head), breast, CNS, bowel, skin and rarely ribs or sclera (Sect. 5.14.3). Even when standard dosage schedules are followed, serious radiation complications would occur in 5–10% of long-term survivors. Yet, general incidence rates on most tissues are difficult to present as most studies have specific settings and constraints resulting in large heterogeneity of data. In the following section, osteonecrosis of the jaws and brain radionecrosis will be discussed in greater detail.


Osteoradionecrosis (ORN) of the jaw is a late complication of RT in the treatment of head and neck cancer. It is defined as exposed irradiated bone that fails to heal over a period of 3 months without any evidence of persisting, recurrent tumor or metastatic disease. ORN occurs in bone that was exposed to a radiation total dose exceeding 60 Gy. However, in the presence of concomitant risk factors, lesions can develop in bone exposed to a lower dose, usually above 50 Gy. The overall susceptibility ratio between mandible and maxilla is for the development of ORN 24/1, with the posterior areas of the mandible most at risk and the upper jaw rarely affected.


The overall incidence of ORN in IMRT patients is reported to vary between 5.1% and 12.4% [201] with excess figures (up to 25.5%) [202] in the presence of risk factors and higher figures with longer follow-up. ORN usually develops during the first 3 to 24 months after RT; however, the real risk for ORN lasts a lifetime and can occur at any time following RT.


The pathophysiology of ORN is still uncovered. In essence, the viability of the irradiated bone is lost due to ischemic necrosis in the irradiated atrophic tissue without sufficient capability of repair, leading to secondary soft tissue breakdown and exposure of bone. Pathological fractures following ORN typically form no callus formation [203], illustrating the absence of periosteal healing. The presence of Actinomyces in necrotic bone is best detected with a PCR-based method and its role needs further investigation.


ORN may remain asymptomatic for a prolonged period but signs and symptoms may also occur before the development of bony exposure. Presenting clinical features include pain, tooth mobility, mucosal swelling, erythema, ulceration, malocclusion, dysphagia, trismus, paresthesia, or even anesthesia of the associated branch of the trigeminal nerve.


Different classifications of ORN exist, usually based on following criteria: extent of the lesion [204], symptoms [204, 205] and response to hyperbaric oxygen therapy. The extent of lesions can vary and range from a non-healing extraction site to exposure and necrosis of large sections of the jaw. Late stage ORN often present with fistula from the oral mucosa or skin, complete devitalization of bone, pathological fractures, and even life-threatening complications.


Panoramic radiographs are mostly used for diagnosis, follow-up, and monitoring patients who are at risk of osteonecrosis. However, only at a loss of 30–50% in bone density injury will be visible on X-ray. CBCT, CT, and MRI allow to analyze the jaws more extensively and to better assess the extent of injuries and are also very helpful in differentiating osteonecrosis from other causes of osteolysis.


Although ORN may occur spontaneously [206], most ORN develop after dental surgery (extractions of teeth, dento-alveolar surgery, dental implant placement).


The most important risk factors for ORN are dose >50–60 Gy and post-RT dento-alveolar surgery in the high-risk zone. Other factors are tumor size, proximity of the tumor to bone, age >60 years, diabetes mellitus, poor oral hygiene, concomitant chemotherapy, active smoking, excessive alcohol consumption, and chronic use of corticosteroids [207]. Many of these published risk factors still need confirmation with robust data and study designs.


Pre-treatment dental screening aims to reduce the risk of developing ORN after RT by eliminating all teeth with an elevated risk in an area of bone that will get exposed to a high dose of IR. It is therefore mandatory to provide the dentist and/or oral surgical team with the forecasted 3D RT isodose curves (Fig. 5.25) to allow a differential approach for teeth within and outside the high ORN risk perimeter [208].

Four three-dimensional images of the removal of high-risk teeth in I R exposed bone in 40 grey, 50 grey, and 60 grey.

Fig. 5.25
3D RT isodose curves


Four three-dimensional images of the removal of high-risk teeth in I R exposed bone in 40 grey, 50 grey, and 60 grey.


In the areas with a high risk of developing ORN (>50 Gy), an extraction is done whenever teeth represent a risk for future need for extraction of a risk for future infection. Teeth which will be extracted as part of the surgical resection approach can be left in situ. In other areas of the jaws, the extraction therapy will depend on regular extraction guidelines, the clinical experience of the supervising surgeon considering the level of oral hygiene of the patient and the expected future limitation of mouth opening. In the upper jaw, due to the far lower incidence of ORN, most clinicians opt for regular extraction guidelines.


Depending on the extent of the affected area (both soft tissues and bone), the symptoms, the existence of a pathological fracture the treatment will vary from a conservative to a surgical approach. In early stages, conservative measures such as antibiotics, debridement, and irrigation will be preferred while surgical resection and reconstruction (reconstruction plates, free vascularized osteomyocutaneous flaps) are reserved for more advanced cases. Whenever resection of ORN is needed, 3D RT isodose curves should be allowed to be included in the virtual planning of the procedure.


HBO as adjunctive therapy to conventional treatment has not been proven to yield consistently significantly favorable results compared to conventional treatment alone. Therapeutic regimens composed of Pentoxifylline and Tocopherol combined have been shown to have a synergistic effect in treating small areas of ORN with visual and symptomatic resolution of the condition. Clonodrate, a first-generation non-aminobisphosphonate, has been described as effective when combined with pentoxifylline and tocopherol for refractory ORN (PENTOCLO-protocol).


Lifestyle changes should accompany both conservative and surgical procedures: proper oral hygiene, smoking and alcohol cessation, healthy and adequate nutrition intake, well-fitting dentures [209]. Dento-alveolar surgical procedures in a highly radiated mandible should be avoided if possible and whenever needed following principles should be kept in mind: minimal periosteal degloving, antibiotic coverage, local anesthesia without epinephrine.


Brain radionecrosis (RN) is an irreversible late radiation-induced tissue complication that can occur after irradiation of brain parenchyma inducing a vascular lesion of the white matter, developing in the irradiation field, secondary to chronic inflammation of the brain parenchyma, with a tendency for spontaneous extension [210]. Its pathophysiology is not yet clear. Brain RN induces hypocellular zones of necrosis and fibrinous exudates with degenerative or dystrophic changes in the vasculature, with telangiectasia, hyaline thickening of vessels, and fibrinoid necrosis including intravascular thrombosis responsible for an increase of vascular permeability. The occurrence and severity are correlated with dose-volume parameters [211]. An actuarial incidence of brain RN up to 34% two years after stereotactic radiotherapy (SRT) was recently reported—symptomatic and sometimes lethal or severely debilitating in 10–17% of the patients [212]. Approximately 80% of cases occur within 3 years from the completion of RT.


The symptoms of RN are those of a non-specific intracerebral expansive process. A seizure is inaugural in half of the cases, signs of intracranial hypertension and a progressive deficit syndrome (sensory, motor, or aphasia) are frequently present. The semiology often reproduces the initial signs of the primary tumor. In pituitary tumors, lesions preferentially affect the chiasma and the optic nerves causing severe visual disturbances; damage to the temporal, frontal, and hypothalamus lobes is often associated, causing cognitive impairment.


The main differential diagnosis is tumor progression due to very similar clinical and radiological characteristics.


The gold standard for the diagnosis with certainty is the pathological analysis. On histological analysis, 50% of lesions are pure RN, the remaining 50% associated with radionecrosis and tumor cells without predicting their viability.


There is not yet a validated imaging technique that distinguishes the two entities though advanced imaging techniques such as DTI (ADC and fractional anisotropy ratios), perfusion MR imaging (CBV, rPH, and relative PSR), MR spectroscopy, and amino acid PET hold promise [213]. The MRI shows a persistent central hypointense and an enlargement of a pre-existing enhancement in T1 gadolinium associated with a hypersignal in T2 with an appearance of “Swiss cheese” or “soap bubble.” Perfusion MRI, spectro-MRI and PET amino acid imaging may provide additional arguments. Other avenues are showing interest in the differential diagnostic strategy—notably radiomics.


When this documentation is not possible, the decision-making process is guided by clinical and imaging criteria collected over a significant period of follow-up. Such criteria were proposed by the Association of Neuro-Oncologists of French Expression (ANOCEF) [214]. The treatment options of brain RN include steroids, bevacizumab, surgical resection, and hyperbaric oxygen.


Cellular depletion by radiation-induced death is not the only one responsible for initiation and progression of lesions.

Radiation-induced effects on the vascular endothelium drive the propagation of the inflammatory response and chronic effects.

Molecular and cellular damage after exposure to IR impacts cellular homeostasis and potentially leads to chronic organ dysfunction.

The notion of a continuum of effects, orchestrated by all the compartments and chronic cytokine cascades, opens up fields of therapeutic approaches.


Cellular depletion by radiation-induced death is not the only one responsible for initiation and progression of lesions.


Radiation-induced effects on the vascular endothelium drive the propagation of the inflammatory response and chronic effects.


Molecular and cellular damage after exposure to IR impacts cellular homeostasis and potentially leads to chronic organ dysfunction.


The notion of a continuum of effects, orchestrated by all the compartments and chronic cytokine cascades, opens up fields of therapeutic approaches.


Improving the quality of life of patients by reducing sequelae of cancer treatment is one of the main future challenges. Beyond the dose itself to the organs at risk, the probability of occurrence of side effects is related to a multitude of factors: the nature of the radiation (photons, electrons, charged particles), the volume irradiated, the fractionation, the spread, the dose rate, but also the nature of the exposed tissue (hierarchical versus flexible tissue, in parallel or in series organization) or the individual susceptibility of the patient. Furthermore, in a simplified manner, acute toxicity is mainly observed in rapidly proliferating tissues (skin, gastrointestinal tract, and hematopoietic system) and late effects are observed in slower proliferating tissues (central nervous system, kidney, heart) [215]. Historically, tissue response to radiation has long been explained by the target cell concept which suggests that the severity of tissue effects is mainly due to the depletion of cells in a target compartment by radiation-induced death resulting in a functional impairment of the organ. This hypothesis can be considered for early effects but is more questionable for late effects. Cellular depletion by radiation-induced death is an important element of the tissue response, but it is not the only one responsible for the initiation and progression of lesions. Molecular and cellular damage after exposure to IR will disrupt cellular homeostasis and potentially lead to chronic organ dysfunction. It is now agreed that the tissue response to IR is the result of the activation and integrated involvement of all the compartments that make up the tissue (Fig. 5.26 and Box 5.16).

A diagram involves the coagulation system activation, endothelial cells activation, inflammatory response, recruitment of circulating cells, and D N A repair cell death or survival progresses that lead to fibrosis necrosis.

Fig. 5.26
Irradiation and progression of radiation-induced normal tissues damage. Tissue damage results from several acute events such as cell loss and endothelial cells activation. Damage progression includes a continuum of effects orchestrated in time and space leading to tissue fibrosis/necrosis and organ dysfunction


A diagram involves the coagulation system activation, endothelial cells activation, inflammatory response, recruitment of circulating cells, and D N A repair cell death or survival progresses that lead to fibrosis necrosis.


The notion of a continuum of effects, orchestrated by all the compartments and chronic cytokine cascades, opens up fields of investigation into various therapeutic approaches [190]. The contemporary view involves several cell types and molecular mechanisms, which together form an orchestrated response, and contribute to the initiation, progression, and chronicity of radiation-induced injury. A better understanding of these event kinetics should allow the identification of molecular and cellular targets, associated functions, and relevant times for therapeutic action. Radiation-induced effects on the vascular endothelium and epithelial barriers are important for the propagation of the inflammatory response and the recruitment of immune cells. The concept that the microvasculature plays a central role in the radiation toxicity of many tissues is emerging and demonstrated now. Irradiation leads to endothelial cell apoptosis, increased vascular permeability, and acquisition of a pro-inflammatory and pro-coagulant phenotype. Moreover, tissue-specific deletion in the endothelium of key molecular actors impacts the severity of acute and normal tissue injury [216].


Rapidly after exposure to IR, damage to the endothelium and epithelial cells leads to the release of damager signals (such as DAMPS) and the activation of adhesion molecules. This reaction allows the recruitment of a large panel of immune cells to the damaged site, which are able to repair the tissue but which, in the case of chronic inflammation, can strongly also participate in the installation of fibrosis [196]. For example, macrophages are rapidly recruited after irradiation and are a heterogeneous immune cell population with multiple pro- or anti-inflammatory as well as pro- or anti-fibrosis functions. The recruitment dynamics of macrophages, as well as their phenotypic orientation impacted by their microenvironment over time, are increasingly shown to play an essential role in the evolution of radiation-induced injury [195]. Radiation-induced immune effects are propagated by a large panel of cytokines including interferon-γ (IFNγ), Interleukin-1β (IL-1β), Interleukin-6 (IL-6), CC-chemokine ligand 2 (CCL2), tumor necrosis factor (TNF), and transforming growth factor-β (TGFβ). Interestingly, beyond their roles in the inflammatory response, some of these cytokines also play essential roles in several other processes contributing to the evolution of radiation-induced lesions. TGFβ induces the differentiation of fibroblasts into myo-fibroblasts with a consequent increase in the extracellular matrix. In addition, in association with other cytokines such as IL-1β, TGFβ promotes endothelial-mesenchymal (endoMT) [217] and EMT, two key processes also demonstrated in radiation-induced lesions to healthy tissues [218]. Finally, it has recently been shown in several studies that senescence also contributes to the pathogenesis of radiation-induced injury to healthy tissue. Senescence is a durable cell-cycle arrest with a persistent pro-inflammatory Senescence-Associated Secretory Phenotype (SASP) characterized by the secretion of multiple growth factors and cytokines, the senescence-messaging secretome (SMS) [219]. Premature senescence can be produced by a large panel of DNA-damaging agents and genotoxic stress including IR. In several preclinical models of radiation-induced lung injury, it has been shown that many types of cells bear senescence marks such as pneumocytes, macrophages, and endothelial cells [220, 221]. Interestingly, senolytic agents that selectively can kill senescent cells limit radiation-induced lung injury provided the evidence that senescence participates to the pathogenesis and that senolytic drugs could be a good strategy to reduced late normal tissue damages [222].


Recent research clearly shows that normal tissue injury is a dynamic and progressive process. The main challenge in the future will be to perfectly decipher this dynamic of events for each organ and its own characteristics. This will allow to propose new molecular and functional tools to predict, prevent, and treat damage to healthy tissues after irradiation.


The evaluation of treatment plans in the treatment planning system is based on dose-volume histogram analysis for PTV and critical organs. Final plan quality evaluation should be based on plan complexity, plan robustness, and dose distribution analysis including dose-volume control. Constraints for any dose-volume relationship should be connected to the radiobiological outcome.


In 2010, a series of articles were published in the International Journal of Radiation Oncology Biology Physics as a meta-analysis of published dose–response observations for different critical organs. The project called Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) aimed to review meaningful data published in the previous 18 years for common critical organs in terms of dose/volume values connected to radiobiological effects [223]. This endeavor was a challenge because it involved the amalgamation of different analytic methodologies, calculation methods, endpoints, and grading schemes, which were used in different studies to address the relationship between dosimetric parameters and the clinical outcomes of normal tissues.


QUANTEC consists of two introductory papers about the overview and history with some scientific issues related to the QUANTEC effort and about the suggestions on how to rationally incorporate the QUANTEC metrics/models into clinical practice. The core of the QUANTEC project is described in 16 articles for different organs at risk or complications:
Bladder

Brain

Brainstem

Esophagus

Hearing loss

Heart

Kidney

Larynx and pharynx

Liver

Lung

Optic nerves and chiasm

Penile bulb

Rectum

Salivary gland

Spinal cord

Stomach and small bowel


For each organ, there are associated sections describing: clinical significance, endpoints, challenges in defining volumes, review of dose/volume data, factors affecting risk, mathematical/biological models, special situations, recommended dose/volume limits, future toxicity studies, and toxicity scoring.


The QUANTEC reviews provide focused summaries of the dose/volume/outcome information for many organs, but these were usually obtained for 3D conformal RT or other techniques that have in many cases already been replaced by more modern techniques, such as VMAT or SRT. It should be emphasized that dose/volume constraints and other information in QUANTEC are expected to be updated in the future for relevant techniques. The data is not intended to be extrapolated to pediatric patients. Pediatric Normal Tissue Effects in the Clinic (PENTEC) is a recent initiative to review tolerance constraints for children, who may have different tolerance to that of adults [224].


For hypofractionated RT such as SRS or SBRT, relatively small target volumes receive hypofractionated RT schedules, typically in 1–5 fractions. As an extension to QUANTEC and other previous guides to tissue tolerance, high dose per fraction, Hypofractionated Treatment Effects in the Clinic (HyTEC) was published as a series of articles [225]. This project served to provide guidance on dose/volume constraints for hypofractionated regimens for 7 normal tissues as well as 9 disease sites (TCP). Interestingly, the possibility of so-called “new radiobiology” of hypofractionation is also alluded to—the possibility that large fractions may induce enhanced radiobiological effects in tumors by additional vascular targeting and anti-tumor immune responses [150]. Radiobiological aspects of hypofractionation are discussed in detail in Chap. 5.


Overall, the recommendations of QUANTEC, PENTEC, HyTEC, and other constraint guidelines should be used judiciously as a guide and should not replace clinical judgment.


Normal tissue complication probability (NTCP) describes the probability of organ/structure complication related to radiation treatment specified by physical and clinical factors in radiation oncology.

There are various approaches to NTCP modeling which usually are based on different statistical distributions.

The predictive power of the NTCP model is dependent on the parameters of models, which can include dosimetry as well as other clinical and treatment conditions.


Normal tissue complication probability (NTCP) describes the probability of organ/structure complication related to radiation treatment specified by physical and clinical factors in radiation oncology.


There are various approaches to NTCP modeling which usually are based on different statistical distributions.


The predictive power of the NTCP model is dependent on the parameters of models, which can include dosimetry as well as other clinical and treatment conditions.


The normal tissue complication probability (NTCP) is the probability that for a given dose distribution organ or structure complication can be expected. These complications can be multiple for one organ or structure and usually are called as endpoints in the models. NTCP is aimed at quantification of dependence of tolerance dose for a certain radiation effect on the size of treated volume. The NTCP models are supposed to be predictive and to be used to estimate the complication risk for organs at risk (OARs) after RT. OARs can be used to individualize the tumor dose for a given acceptable NTCP (Box 5.17).


NCTP models are used to describe dose–response curve shape for particular endpoint for an organ at risk, which is usually sigmoidal. These models are usually connected to Dose-Volume Histogram (DVH) of the applied treatment plan; therefore the models are sometimes called DVH-reduction models. More complex approach [226] moves towards spatial dose distribution in the patient and not dose-volume reduction only. When voxel-based evidence on organ radiosensitivity was acknowledged and attempts were made to develop a probabilistic atlas for NTCP in radiation oncology. However, there are other clinical factors that influence complications, such as chemotherapy, fraction size, pre-existing medical conditions, and comorbidities. The predictive strength for models can be enhanced with considering other important clinical and medical features for the patient. This information is expected to provide a boost for further deployment of biological models in the clinical treatment planning process.


Common NTCP models as described by [227] are:1.
Lyman-Kutcher-Burman (LKB) model (Gaussian)
 2.
Parallel architecture model (Logit)
 3.
Weibull model (Weibull)
 4.
Critical element model (Poisson)
 5.
Relative seriality model (Poisson)
 6.
Critical volume model (Binomial)
 7.
Inverse tumor model (Poisson)


Models are based on different statistical distributions (in parentheses). The first four models are using cell-survival-based response, while others are phenomenological. However, each model may be expressed in terms of the parameters D50 (dose that is associated with the 50% response probability) and γ50 (gradient of the dose-response curve at the level of the 50% response probability). The steepness of the NTCP curve can be expressed in the models by parameter m. It is inversely proportional to the steepness of the dose response.


Commonly used model is the Lyman-Kutcher-Burman (LKB) model. This model assumes that the tolerance dose increases inversely as power of n of the partial volume irradiated. Examples of NTCP curves obtained for the LKB model are presented in Fig. 5.27.

Two line graphs represent N T C P in relative units versus dose in grey. The plotted line in graph A rises before plateauing with N T C P = 0.5000, D 50 = 50.0, and m = 0.50. In graph B, the plotted line is in an upward trend with N T C P = 0.4338, D 50 = 60.0, and m = 1.00.

Fig. 5.27
NTCP curves calculated from Lyman-Kutcher-Burman model for two parameters combinations. Parameter m is inversely proportional to the steepness of the curve. (a) NTCP curve calculated by LKB model for D50 = 50 Gy and m = 0.50. For a dose of 50 Gy, the value of NTCP is 0.50. (b) NTCP curve calculated by LKB model for D50 = 60 Gy and m = 1. For a dose of 50 Gy, the value of NTCP is 0.43


Two line graphs represent N T C P in relative units versus dose in grey. The plotted line in graph A rises before plateauing with N T C P = 0.5000, D 50 = 50.0, and m = 0.50. In graph B, the plotted line is in an upward trend with N T C P = 0.4338, D 50 = 60.0, and m = 1.00.


Serial (critical element models) and parallel (critical volume models) are also common models. These mechanistic models are based on tissue architecture. It is assumed that organs consist of functional subunits, which can be organized in chains for serial organs or independently for parallel organs. Damage of one functional subunit impairs the function of the whole organ, while the function of a parallel organ is more dependent on the irradiated volume.


NTCP models are usually incorporated into in-house developed software in RT centers. Currently, they are also available in commercial treatment planning systems. Parameters for different NTCP models adopted from literature must be used with caution when the probability estimation is applied as a decision criterion for the treatment plan. NTCP can be used also for comparisons between different treatment plans or RT modalities. In these cases, NTCP is used as a relative value in the plan evaluation process and this approach is safer. However, the software should always allow the user to update model parameters. There should be detailed documentation for the models available. It is obvious that the value of NTCP is strongly dependent on the parameters of the model, and therefore should be used with caution.


Stem cells have been described as undifferentiated cells which are found in most adult mammalian tissues. Stem cells are divided into two principal groups: embryonic and adult stem cells. Embryonic cells, which have a pluripotency phenotype have the blastocyst inner cell mass as their origin. It means that they can be differentiated into all cells from the three main germ layers (endo-, meso-, and ectoderm). On the other hand, adult stem cells can be differentiated into cell types according to their origin tissue, thus they are multipotent. Under physiological conditions, adult stem cells are slow growing with a long G0 cell-cycle phase. The main function of such stem cells is to maintain tissue homeostasis including continuous regeneration and associated constant number of cells. The way they are divided is as follows: from the origin stem cell arises one daughter cell with stem cell properties and one progenitor cell with a higher proliferative capacity [228]. Below the normal stem cells in different tissues are described alongside cancer stem cells and their IR response or resistance together underlying molecular mechanisms.


In the healthy human tissue, there are multiple cell populations with different stem cell phenotypic characteristics and radiation sensitivity. These different stem cell niches are discussed below based on the tissue localization.


Stem cells of the bone marrow are divided into two groups: hematopoietic and mesenchymal stem cells. From the hematopoietic stem cells arise leukocytes, erythrocytes, and thrombocytes and from the mesenchymal stem cells adipocytes, chondrocytes, myocytes, and osteocytes are generated [229]. One function of mesenchymal stem cells is to establish the hematopoietic stem cell niche [230]. When it comes to IR toxicity, the progenitors from the hematopoietic stem cells are more sensitive than the origin, more primitive, stem cells. Such a difference is linearly dose dependent in the progenitor cells and is one of the factors causing the development of one of the early radiation effects, the hematopoietic syndrome [231]. Importantly, both of these stem cell populations are responsible for the repopulating of the damaged bone-marrow homeostasis after IR exposure.


The neural stem cell pool can be divided according to the localization. Hence, we recognize the subventricular and the subgranular group [232]. Even within these two subtypes are heterogenous. Thus, one can distinguish four main types of cells from the subventricular niche: activated neural stem cells, dormant neural stem cells, progenitor cells, and quiescent neural stem cells [233]. Generally, neural stem cells can differentiate into multiple neuronal- and glial cell types. The most IR sensitive populations are activated neural stem cells and progenitor cells because IR induces their cell death, for example, apoptosis. Such effects lead to a reduced population of new neurons. To prevent the negative effects of IR on the neural stem cells different protective strategies have been tested, for example, administration of lithium [234, 235] or the natural polyphenol resveratrol [236]. It has been shown that lithium pre-treatment can reduce DNA damage and increase microglial activation [234, 235]. Resveratrol, on the other hand, has a neuroprotective effect, because it can reduce oxidative stress [236].


Several types of the stem cells exist in the skin which can differentiate into more than two dozen cell types, including epidermal-, keratinocyte-, and melanocyte stem cells [237]. The keratinocytes progenitors are the most IR sensitive ones, if damaged they are eliminated thereby contributing to the high epidermal sensitivity to IR. In contrast, more primitive keratinocyte stem cells possess active repair mechanisms and increased cell survival, but their rapid and faultier repair contribute to the genomic instability. Interestingly, while the keratinocyte stem cells favor repair of DNA damage, the melanocyte stem cells are not involved in tissue regeneration after IR damage [231].


Gastrointestinal syndrome, a known acute toxicity response to IR, promoted the first exploration of intestinal stem cells in radiation biology studies, which included the exposure of mice to doses greater than 14 Gy, inducing death after 7 to 12 days due to small intestine damage. This high sensitivity has been attributed to the fast cell turnover in the intestinal mucosa which, in mice, completely renews the epithelium every 5 days [231]. These studies allowed the characterization of intestinal regeneration, revealing the presence of a stem cell population near the bottom of the intestinal crypt. These actively cycling cells are highly sensitive to IR, undergoing apoptosis in response to doses as low as 1 Gy although this sensitivity seems to be dependent upon their position within the crypt [238]. Another stem cell subpopulation, known as crypt base columnar cells and characterized by the expression of Lgr5 (Leucine-Rich Repeat Containing G Protein-Coupled Receptor 5), are less radiosensitive than the previously described cells yet more sensitive than small intestine progenitor cells. Radiation toxicity can occur at low doses; however, crypt loss is only observed after exposure to higher radiation doses. This may be because crypts only disappear after total loss of the stem cell population, which only happens at doses greater than 8 Gy. This radiosensitivity could be caused by the accumulation of DNA damage or pro-apoptotic proteins after genotoxic stress, i.e., p53, ATM, and PUMA. The difference in radiosensitivity between the small intestine and the colon could be due to a more efficient p53 signaling, DNA repair and G2-phase checkpoint delay in the latter. The high expression level of the anti-apoptotic protein Bcl-2 in colon progenitors could be another reason. Paradoxically, the risk of developing cancer after exposure to IR is lower in the small intestine, suggesting the interplay between cell resistance and lower genomic stability. It should be noted that most of this knowledge is based on studies in mice; therefore, human models are still required in order to understand the intestinal stem cell radiation biology [231] (Box 5.18).


Stem cells are divided into two principal groups, embryonic and adult stem cells, respectively. Some examples are bone marrow-, neural, skin, and intestinal stem cells.

The main function of stem cells is to maintain the tissue homeostasis.

In the normal human tissue, there are multiple cell populations with different stem cell phenotypic characteristics and different IR sensitivity.


Stem cells are divided into two principal groups, embryonic and adult stem cells, respectively. Some examples are bone marrow-, neural, skin, and intestinal stem cells.


The main function of stem cells is to maintain the tissue homeostasis.


In the normal human tissue, there are multiple cell populations with different stem cell phenotypic characteristics and different IR sensitivity.


Tumor heterogeneity is found among patients with the same histological diagnosis as well as within each patient’s tumor as a result of genetic or phenotypic variations [31]. Further, the tissue of origin also influences the inter-tumoral heterogeneity because some of the driving signaling networks (e.g., those that maintain genomic integrity) may vary. Moreover, tumor progression, treatment sensitivity including towards RT and tumour aggressiveness are largely influenced by the origin of the carcinogenic transformation as well as the TME. Tumor heterogeneity plays a significant role in cancer cell survival, thus setting a significant challenge in the development of effective cancer treatment, or in the prevention of tumor progression and metastasis [239].


Recent studies describe two models, i.e., clonal evolution and cancer stem cells (CSCs) which in part can explain tumor heterogeneity as well as alterations during progression of malignancy. The clonal evolution model shares the idea that all cells can accumulate genetic mutations; therefore, any cell has tumorigenic potential [240, 241]. On the other hand, the CSCs’ model describes a hierarchy system in which tumor growth and progression can be maintained with a small proportion of cancer cells displaying stem-like characteristics, such as self-renewal. These stem-like cancer cells can drive tumorigenesis and differentiation, which can to some extent explain tumor cell heterogeneity [240, 242, 243]. It is believed that CSCs originate from the malignant transformation of normal stem cells or progenitor cells. Thus, CSCs possess key properties such as self-renewing and differentiation capacity, thereby being able to produce a phenotypically variable progeny [244]. Due to these characteristics, CSCs are thought to be important for tumor formation, recurrence, and resistance. Indeed, experimental data from xenograft studies in mice where different tumor cells with diverse CSCs characteristics have been engrafted and formed tumors, have demonstrated that CSCs are involved in tumor growth and metastasis and that they are resistant to a multitude of cancer treatments including RT [245–251]. It has been noted that only a few surviving CSCs in a heterogeneous tumor are enough to cause local tumor relapse after RT but also to promote metastasis [243, 247]. It is difficult to calculate the frequency of CSCs within a tumor as it is dependent on the type of malignancy. Further, the identification of CSCs is challenging as specific markers are not entirely clear. This is in part due to the high intra- and inter-tumor heterogeneity as well as by tumor plasticity, and variable genotypes and phenotypes. Regardless, a few markers, i.e., CD44, CD98, CD90, CD44+/CD24−, and CD133, are robust enough to be used in the identification of CSCs (Table 5.15) in breast cancer, small-cell lung cancer (SCLC), esophageal cancer, larynx, head and neck cancer, non-small cell lung cancer (NSCLC), etc. Interestingly, these markers are also associated with response to RT as illustrated in NSCLC and glioblastoma [245, 246, 248–251].Table 5.15
Example of cancer stem cells (CSCs) Markers

Type of tumor

CSC markers

References

Breast cancer

CD44+/CD24, CD44−/CD24−/low ALDH1

Coppes and Dubrovska [247], Filipova et al. [243]

Esophageal cancer

CD44
CD133
CD90

Smit et al. [252], Yang et al. [253]

Glioma

CD133

Bao et al. [245]

Head and neck cancer

CD98

Coppes and Dubrovska [247], Digomann et al. [254] Martens-de Kemp et al. [255]

Larynx

CD44
CD133

Yang et al. [253]

Small cell lung cancer

CD133

Sarvi et al. [256]

Non-small cell lung cancer

CD133

Bertolini et al. [246], Lundholm et al. [250], Moro et al. [251], Shien et al. [257]


The variable phenotype of CSCs has a strong association with cells of origin thus making the comparison between different tumors complicated. In addition, the number of CSCs within a tumor is relatively small; therefore, the use of CSC markers is a poor predictor for treatment response [258]. Regardless, the characterization of CSCs remains important, as it may provide essential information in the development of more efficient treatment strategies and the prevention of tumor relapse as well as metastasis [240, 247–249, 251, 259].


A successful RT treatment largely depends on the elimination of cells with tumorigenic capacity, i.e., the number of clonogenic cells, which in part are CSCs, seeking to inactivate these permanently and to take control of tumor growth. Should a single CSC survive, the possibility of tumor relapse is tangible, with the consequent concern that this new tumor may now be RT resistant. In part RT resistance of tumors depends on the number of CSCs within the tumor mass, with greater numbers often being responsible for the failure of therapy [240, 260]. Some of the signaling networks involved are shown in Fig. 5.28.

A diagram displays the tumor environment leads to pro-inflammatory cytokines hypoxia, and the apoptosis goes through low R O S level. The pro-inflammatory cytokines hypoxia and the low R O S level both forward to radio resistance.

Fig. 5.28
Ionizing radiation and factors associated with cancer stem cells and tumor microenvironment contribute to tumor resistance to IR. (Adapted from [240, 260])


A diagram displays the tumor environment leads to pro-inflammatory cytokines hypoxia, and the apoptosis goes through low R O S level. The pro-inflammatory cytokines hypoxia and the low R O S level both forward to radio resistance.


CSCSs are also reported to have activated DDR signaling and increased DNA repair capacity, for example, ATM, Chk1/2, and NHEJ [245, 250], increased cell death resistance as well as upregulation of the signaling pathways involved in cell survival and proliferation, such as HIF-1α, WNT (Wingless-related integration site), NOTCH, or Hedgehog [247]. Moreover, it was recently shown that tumors with a higher count of CSCs had an impaired local control of the tumor and lower effect of RT than tumors with less CSCs [240, 261]. Several studies have suggested that RT sensitization is linked to the same signaling pathways involved in the preservation of CSCs cells, for example, WNT, NOTCH, or Hedgehog signaling pathways; therefore, these pathways have been indicated as possible targets for CSC-targeted therapies [247, 253]. Other strategies propose the inhibition of the DNA damage response (ATM, Chk1/2), the promotion of apoptosis, and the inhibition of epigenetic-related proteins, for example, histone deacetylase (HDAC), Enhancer of zeste homolog (EZH2). [247]. However, at present very few of these strategies have been clinically tested, due to complex cellular characteristics of CSCs. Nevertheless, further CSCs exploration and their signaling networks could reveal new potential therapeutic targets (Box 5.19).


Tumor heterogeneity is explained by theory of clonal evolution and/or existence of CSCs.

Clonal evolution presumes accumulation of genetic mutation(s) while CSCs model describes a hierarchy system of tumor growth and progression maintained with only by a small subpopulation of CSCs.

Few specific markers are at hand for characterization of CSCs due to high inter- and intra-tumor heterogeneity, tumor plasticity, and variable genotypes and phenotypes.

Tumors with a higher count of CSCs show lower efficacy of RT and impaired local tumor control.

Multiple signaling cascades controlling DDR signaling, cell death, EMT, and hypoxia are reported to be altered in CSCs of tumors offering a putative way for RT sensitization for the future.


Tumor heterogeneity is explained by theory of clonal evolution and/or existence of CSCs.


Clonal evolution presumes accumulation of genetic mutation(s) while CSCs model describes a hierarchy system of tumor growth and progression maintained with only by a small subpopulation of CSCs.


Few specific markers are at hand for characterization of CSCs due to high inter- and intra-tumor heterogeneity, tumor plasticity, and variable genotypes and phenotypes.


Tumors with a higher count of CSCs show lower efficacy of RT and impaired local tumor control.


Multiple signaling cascades controlling DDR signaling, cell death, EMT, and hypoxia are reported to be altered in CSCs of tumors offering a putative way for RT sensitization for the future.


The microbiota is composed of many microorganisms such as bacteria (most represented and studied), viruses, fungi, and archaea.

The microbiota plays a key physiological role in maintaining the gut health and well-being of the host.

Composition and abundance of the microbiota can be modified by various stresses and a stable alternative state of the microbiota can lead to pathologies.

Reduction of the fecal microbiota diversity and composition after RT are consistently associated with intestinal toxicity.

The microbiota can modify the tumor response effectiveness of RT.

The microbiota can be a therapeutic target for personalized medicine that might be used to increase patient’s quality of life during or after RT.


The microbiota is composed of many microorganisms such as bacteria (most represented and studied), viruses, fungi, and archaea.


The microbiota plays a key physiological role in maintaining the gut health and well-being of the host.


Composition and abundance of the microbiota can be modified by various stresses and a stable alternative state of the microbiota can lead to pathologies.


Reduction of the fecal microbiota diversity and composition after RT are consistently associated with intestinal toxicity.


The microbiota can be a therapeutic target for personalized medicine that might be used to increase patient’s quality of life during or after RT.


The human body has around 500 billion cells including microorganisms such as bacteria, viruses, fungi, and archaea, on the surface of organs in contact with the outside. All of these microorganisms, hosted by the body, represent the human microbiota. Several microbiotas exist in an organism: in the digestive tract system (from mouth to anus), in the respiratory system, in the urogenital tract, and on the skin. Nevertheless, the larger community of microorganisms resides in the digestive system. The intestinal microbiota can be considered as an organ, given that it has specific functions of its own. Indeed, the intestinal microbiota makes it possible to maintain intestinal homeostasis by transforming nutrients that cannot be digested by the intestine into essential metabolites, by maintaining an effective epithelial barrier avoiding intestinal colonization by pathogens and also by participating to the development as well as the function of the immune system.


Among microorganisms, the most prevalent and studied ones are bacteria. The human GI tract is colonized by more than 2000 different individual bacteria species. Proportional representation and genus level distribution are dependent on the organ localization and vary with diet, age, and geographical localization of the host. As other microorganisms within the microbiota, bacteria have an important role in maintaining the health and well-being of the host. In the healthy gastrointestinal tract, Firmicutes and Bacteroidetes represent the predominant phylum in the microbiota (up to 80%). Other phylums which are less represented in the microbiota are Actinobacteria (3%), Proteobacteria (1%), Verrucomicrobia, and Fusobacteria (less than 1%).


In a physiological environment, the microbiota is in a state considered stable and healthy, also called eubiosis, where very little modifications take place in its composition. The relationship between host and microbiota is therefore beneficial for both entities. However, multifactorial events can cause transitory disturbances in the microbiota state and therefore population reorganization. Because of the resilience ability of the microbiota, such modifications are often transient. The microbiota then has the capability to return to its basic stable healthy state. However, the microbiota can also tend towards another stable state, called alternated state or dysbiosis, which becomes deleterious for the host [262]. Dysbiosis is defined as a condition where there is an excessive presence of pathogenic microorganisms, a defect in the communities of beneficial microorganisms and a loss of ecosystem structure, i.e., decrease in richness and diversity of microorganism species and increase of the low-grade inflammation, the intestinal permeability, and the oxidative stress. The dysbiotic state of the microbiota questions the scientific and medical community about its involvement in the development of certain pathologies like inflammatory bowel disease (IBD), metabolic disease (obesity or diabetes), neurological pathology (Alzheimer, autism, or Parkinson) but also cancers. Recently, dysbiosis was also reported in patients treated by RT (Fig. 5.29).

A block diagram shows microbiota changing state due to disturbances. The original stable state changes to a transient state after resilience. It is followed by an alternative stable state called dysbiosis.

Fig. 5.29
From microbiota healthy state to dysbiosis and pathologies: case of RT effects


A block diagram shows microbiota changing state due to disturbances. The original stable state changes to a transient state after resilience. It is followed by an alternative stable state called dysbiosis.


Currently, at least eight prospective clinical studies assessed the effect of RT combined [263–266] or not [267–270] with other anti-tumors treatments, like chemotherapy, on the gut microbiota dysbiosis. The results of these studies have recently been presented in Byeongsang Oh’s review in 2021 [271]. Fecal microbiota changes by pelvic cancers (gynecological, colorectal/rectal, prostate, lymph node, and anal cancers) and/or after RT are briefly summarized below. Prior to RT, patients suffering from pelvic cancers have a loss of their fecal microbial diversity [266, 269]. The clinical studies also performed taxonomic analyses at the phyla level in feces from cancer patients. Results highlight variations of the relative bacteria abundance with an increase of the Firmicutes [269] and the Actinobacteria [266] and decrease of the Bacteroidetes [269] and the Fusobacteria [266]. Low fecal bacterial diversity has also been described in patients during and after pelvic RT [266, 267, 269]. Pelvic RT gradually reshapes microbiota bacterial composition in such cancer patients. Indeed, the prospective clinical studies demonstrate that during pelvic RT, the fecal relative abundance of the phylum Bacteroidetes tends to decrease and conversely that of the Fusobacteria significantly increases [266]. After the completion of pelvic RT, fecal relative abundance of the phyla Firmicutes is reduced [266, 269] and that of the phylum Bacteroidetes, Fusobacteria [266], Proteobacteria [270], and Actinobacteria [267] are enhanced.


The prospective clinical studies described above, also show that the reduction of the fecal microbial diversity during and after pelvic RT, are consistently associated with radiation toxicity and therefore with intestinal complications, i.e., enteropathy, enteritis, and diarrhea [266–269]. Indeed, as suggested by a personal view of Andreyev’s team published in The Lancet Oncology in 2014 [272], clinical data seem to indicate that microbiota through its composition change might be an actor involved in radiation-induced intestinal toxicity. In 2018, Gerassy-Vainberg et al. [273] published preclinical data supporting this assumption. Indeed, results show that, rectal irradiation by brachytherapy leads to microbial dysbiosis in chronic post-exposure phase (6 weeks). Irradiated microbiota transplantation in germ-free mice transmits susceptibility to radiation damages at least in part through the production by epithelial cells of Interleukin-1 (IL-1). Nevertheless, in 2020, data published in Science by Guo Hua et al. show that gut microbiome-metabolome network plays a crucial role in substantial protection against radiation-induced toxicity though host defense regulation [274]. Indeed, a small proportion of mice named “elite-survivors” can survive a high dose of total body irradiation and that these individuals also reduce their susceptibility to radiation-induced digestive toxicity and damages. The families Lachnospiraceae and Enterococcaceae, together with downstream metabolites represented by propionate and tryptophan pathway members, contribute substantially to radioprotection. Even if a role of the gut microbiota is suggested on RT-induced tissue toxicity or protection, very little data exists concerning its involvement and the underlying mechanisms. Wang et al. showed that the reduction of the fecal microbial diversity is even more pronounced in patients with pelvic cancers who later progressed to RT-induced side effects (fatigue, diarrhea) [269]. Mitra et al. propose that compositional characteristics of microbiota in cancer patients could also be relevant to be predictive of end-of-anti-cancer treatment bowel toxicity [265].


RT efficiency, regarding anti-tumor effects, passes in part through the induction of immunogenic cell death in which CD8+ cytotoxic T cell, CD11b+ myeloid cells and dendritic cells all have been described as major actors [275]. There is growing evidence of the existence of bidirectional effects of RT and microbiome composition. Indeed, RT-induced reduction of gut microbiota diversity, richness, and composition could be followed by the host immune response alteration which in turn could lead to an effectiveness change of the anticancer treatment themselves. In 2021, data published by Shiao et al. in the Cancer Cell, robustly demonstrated that, within the gut microbiota, commensal bacteria and fungi differentially regulate tumor response to RT [276]. Indeed, commensal bacteria are required for efficient immune anti-tumor effect (activation of T cells) of RT. Currently, no study has identified bacterial subjects involved in RT efficiency. By contrast, Shiao et al. demonstrated that commensal fungi regulate the immunosuppressive microenvironment of tumor (with combined effects on T cells and macrophages) after RT leading to a reduction of treatment efficiency. They highlighted a role of Saccharomycetales orders and specific Candida Albicans genera in fungal effect after RT.


Fecal microbial signature in patients with pelvic cancers may be a tool for RT risk assessment and/or efficiency. In order to robustly demonstrate this assumption, further experiments and clinical trials should be performed. The use of high-throughput data generation by multi-omics approaches (e.g., microbiota shotgun sequencing or metabolomics analyses) and mathematical models will give an added value compared to previous studies. After RT, a better understanding of individual states of different microorganism populations within the microbiota or more largely within the intestinal ecosystem could help to guide personalized medicine. Indeed, prophylactic or curative treatments like rich fiber diet, probiotic or fecal microbiota transplantation could prevent or reduce RT-induced toxicity and/or improve radiotherapy efficiency on tumor control.


In radiation research, different techniques are used to measure complete molecular- or genomic profiles of organisms, resulting in various collected data types. Omics, a general term for specified measurements and studied biological fields, include genomics, proteomics, transcriptomics, metabolomics, phenomics, lipidomics, and many more. The suffix “omics” indicates interest in all molecules or genes of a specified type and their interactions rather than individual observations. For example, in genomics studies the genome, a set of all genes expressed in the cell, tissue, or organism, and their relationships with each other and with the environment is analyzed. So other omics-based platforms such as proteomics and transcriptomics study proteome (all proteins) and transcriptome (all types of transcripts like mRNAs and miRNAs), respectively. Omics data analysis helps to understand the influence of molecules and genes on a phenotype. The measurements can be obtained with techniques like microarrays, high-throughput sequencing technologies (Illumina, Oxford Nanopore, etc.), mass spectrometry (MS) (including MS imaging), flow-, and mass cytometry. The amount of collected data may vary from few records to millions, and the measurements may be taken for one point in time or more. With increased throughput, a large amount of data is generated, which requires advanced methods to perform a comprehensive analysis. By combining the domain expertise and knowledge of mathematics and statistics, data science extracts insights from big data.


Different statistical approaches are applied depending on the experimental design, type of data, sample size, number of replicates, number of time points, and so on. The first step of any analysis is data preprocessing that includes data cleaning and normalization. Omics data may have missing values, duplicated observations or outliers which need to be handled. Outliers are observations that deviate from other observations due to equipment failure or recording errors. An outlier can be corrected or removed from the analysis. There are many outlier detection methods that can detect one or more anomalies, like Chauvenet’s criterion, Grubbs’ criterion, Dixon’s procedure, Tukey’s or Huberta’s method. Which one to choose depends mainly on the data distribution and sample size. Observations with missing values can be ignored, removed or the values may be imputed with mean, median, mode, or constant value. Missing data imputation can also be carried out with, for example, the Nearest Neighbor algorithm, which finds k nearest observations to the observation with missing values and the aggregate of these measurements, as mean/median value, is used to impute the missing one. Before the analysis, data should be normalized to guarantee their numerical scale similarity across different experiments. The standardization techniques, asz-, t-score transformation, or local re-scaling, are often applied to partially correct the batch effect or reference instability.


For a comparative study, depending on the data distribution, a variety of statistical tests can be applied. Usually, if data contain one or two experimental groups, one-step testing is performed. Three or more groups or measurements collected from several time points (time series) require a two-step procedure to determine the difference profile—the omnibus type test (from ANOVA family, for example) followed by pairwise comparisons. The test hypothesis is verified with a p-value, the probability that the test statistics would take a value at least as extreme as observed, assuming the null hypothesis is true. The lower the p-value, the stronger the evidence against the null hypothesis, and if the value is equal or smaller than the assumed significance level α, it confirmed the presence of the effect studied.


Due to the number of data collected, the omics analyses usually require more than one hypothesis to be verified, which leads to the problem of multiple testing. In the case of a single test performed, the first type of error is controlled by significance level, but in the case of multiple testing, the number of false-positive results has to be maintained for the whole test family. This can be done with the use of Bonferroni correction, Simes-Hochberg procedure, Dunn-Šidák, Holm, or Hommel methods to control family-wise-error (FWER) or Benjamini and Hochberg procedure or Storey’s algorithm when focusing on false-discovery-rate (FDR). However, the p-value depends on sample size, and if the sample is sufficiently large, the statistical test will almost always indicate a significant difference. Therefore, in big data, it is recommended to calculate effect size together with the p-value. Effect size is a quantitative measure of the strength of a phenomenon calculated based on data and is independent of the sample size. A lot of different measures of effect size exist, and they can be divided into two categories: for indicating differences between groups (e.g., risk difference, risk ratio, odds ratio, Cohen’s d, Glass’s delta, Hedges’ g, the probability of superiority, ω2) and estimating measure of similarity between variables (e.g., the correlation coefficient r, R2, Spearman’s ρ, Kendall’s τ, φ coefficient, Cramer’s V, Cohen’s f, η2).


It is possible to integrate data and results from different experiments to get a unified view to them, in situations when the same experiment is performed on a different set of data or the same data is used in a different experiment (different method) but concerning the same characteristic (null hypothesis). The p-value integration can be carried out, among others, with Fisher product, Lancaster, Stouffer method, or weighted z-transformation. The adaptive rank truncated product method can also be applied at the pathway analysis level.


If measurements were taken for multiple characteristics to estimate the relationship and its strength between them (between a dependent variable and independent variables), a regression analysis can be conducted. Regression is a statistical method that tries to fit a model (function) to the data. The model has different forms, the most common one is a linear function, which is a line that closely fits the data according to a specific criterion. In other words, the dependent variable is a linear combination of the model parameters.


In radiation oncology, data science (incorporating the disciplines of computer science and statistics) attempts to provide clinical insight and clinical decision support using structured or unstructured clinical data that incorporates multiple variables descriptive of patient cohorts [277]. Daily clinical workflows produce a vast array of data comprising of electronic health records, treatment information, genomic data, multimodal imaging, and patient outcomes [278]. Inconsistencies in annotations of medical records presents a problem when utilizing unstructured data [279]. Here machine learning and artificial intelligence are key to detecting patterns within these vast data matrices [280, 281], providing opportunities for the development of diagnostic and prognostic tools. Increasingly clinical data and -omics data on the intrinsic biological characteristics of the patient are being integrated to derive models predictive of outcome metrics such as cancer survival and treatment response [282]. As the volume of clinical data available increases, innovative methods to process and interpret the data is required, translating the information into useable knowledge.


Machine learning (ML) and artificial intelligence (AI) approaches are capable of both identifying intrinsic patterns within data (termed unsupervised techniques) and developing models linking matrices of clinical data to identifying factors such as diagnostic or prognostic criteria (termed supervised techniques) [283]. For the latter type of model, the standard approach is to randomly separate the available data from patients into a training set which is presented to a machine learning algorithm capable of identifying important variables that link the patient data to the target variable (which is itself representative of the diagnostic or prognostic criterion). Subsequently, the generalizability of the learnt algorithm to new data is interrogated by presenting the algorithm with the unseen test data which remains from the available dataset after separation of the training data.


Typical applications of these approaches include prediction of toxicity to radiation therapy with dosimetric factors in head and neck cancer [284] to prediction of survival in pancreatic cancer [282]. A key advantage of machine learning in predicting therapy outcomes over conventional models such as NTCP and TCP which use dosimetric data [285] is the application of additional clinical and biochemical data [284, 286]. Many models demonstrate high performance owing to validation taking place using data which bears a close relationship to the training dataset. This is a particular concern when translating algorithms to a clinical setting, as models may not be evaluated on an external dataset [287, 288], and as such their generalizability is in question. In general, clinical translation of AI-based technologies requires generalizable, robust models which are validated in prospective, randomized clinical trials, and this represents a key challenge to their adoption [289].


Recently, deep learning-based algorithms have been employed, which perform automated image segmentation [290, 291] without the requirement for feature engineering, though the potential for overfitting and a lack of generalizability can persist with such approaches. Deep learning (DL) is a widely researched area in radiobiology and radiation oncology with models being developed for tasks such as modeling outcomes using dose-volume metrics, radiomic feature discovery, image and tumor segmentation, and treatment outcome prediction.


One area where machine learning approaches have seen substantial application is in the development of methodologies utilizing medical imaging data such as CT, MRI, and PET for predictive modeling, particularly of radiotherapeutic outcome. In this instance, the field has been termed radiomics [292, 293]. Here the lack of standardized image acquisition protocols (e.g., slice thickness and tube current in CT) can affect the quality and reliability of the radiomic features extracted by automated algorithms [248, 249, 294, 295]. Often manual clinical delineation of regions of interest of prognostic value prior to modeling (which is termed “feature engineering”) is utilized though this can introduce interobserver variability [296].


The success of radiomics in this context is in the development of “interpretable” machine learning or AI algorithms for a range of applications. Features may be extracted from 2D and 3D imaging data, reducing them to multiple features describing tumor intensity, shape, and texture [297]. These may subsequently be utilized to quantify tumor heterogeneity, where tumors with high heterogeneity have been shown to demonstrate resistance to treatment [298]. Once the features have been extracted machine learning or statistical learning can be applied to match feature patterns to “ground truth” data. Radiomic approaches have been applied to multiple imaging formats including CT, MRI, PET, and ultrasound [299–302]. These approaches have been successfully applied to segmentation and detection problems such as the differentiation of prostate cancers with Gleeson grade 6 and 7 [303], the discrimination of breast cancer subtypes [304] and in TNM staging [305]. Potential clinical applications of radiomics-based classification extend to the detection of lung nodules providing a prognostic and diagnostic aid to the clinician [306]. In terms of personalization of treatment, these approaches have been used to identify prostate cancer patients at risk of biochemical recurrence post-treatment from MRI-based imaging [307], to distinguish between HPV-positive and HPV-negative head and neck cancers [308] and to monitor the response to RT [309]. Similarly, ML models are also being examined in radiation genomics, or radiogenomics, which explores tumor and normal tissue response to radiation at a genomic level [310]. Linking imaging characteristics with genomic data has the potential to aid clinical cancer research and improve decision-making capabilities and personalize therapy [310–312]. Various deep learning methodologies are currently used to generate new knowledge from radiogenomic data including convolutional neural networks (CNNs) [313] and deep neural networks (DNNs) [314].


However, as highlighted earlier, significant challenges remain regarding the clinical interpretability of radiomic and radiogenomic analyses and models, image acquisition standardization and data storage in the era of “big data” [292, 312, 315, 316]. Randomized controlled trials will remain the gold standard in evaluating diagnostic and prognostic interventions that are AI or ML based will be evaluated in oncology [317, 318], where data science approaches can also provide complementary information [319, 320].


Several imaging modalities exist in radiology and nuclear medicine with different physical, acquisition, and reconstruction principles which have strengths and weaknesses but all of them are indispensable for differential diagnosis.
Ultrasound sonography (USG) uses high-frequency mechanical waves to differentiate tissues based on various reflectivity on the tissue edges. USG is an affordable and inexpensive modality with minimal burden for the patient.

Roentgenography (X-ray imaging, RX) uses electromagnetic waves with energy in the X-ray range most often between 40 and 120 keV. RX is a fast and inexpensive modality with small radiation exposure. If one collects thousands of 2D RX images from different angles, one can use computer algorithms for creating 3D images and one is talking about CT. Advantages of CT are in special resolution, high contrast, and 3D information; however, at the cost of high radiation exposure and higher price than RX.

MRI also uses also electromagnetic waves as well as RX of CT but with an energy in the radio range most often between 240 and 500 neV which cannot ionize biological tissue (no radiation exposure) which is together with high tissue contrast advantage of MRI. The disadvantages are longer examination time (15–45 min), price, availability, and several contraindications.


Ultrasound sonography (USG) uses high-frequency mechanical waves to differentiate tissues based on various reflectivity on the tissue edges. USG is an affordable and inexpensive modality with minimal burden for the patient.


Roentgenography (X-ray imaging, RX) uses electromagnetic waves with energy in the X-ray range most often between 40 and 120 keV. RX is a fast and inexpensive modality with small radiation exposure. If one collects thousands of 2D RX images from different angles, one can use computer algorithms for creating 3D images and one is talking about CT. Advantages of CT are in special resolution, high contrast, and 3D information; however, at the cost of high radiation exposure and higher price than RX.


MRI also uses also electromagnetic waves as well as RX of CT but with an energy in the radio range most often between 240 and 500 neV which cannot ionize biological tissue (no radiation exposure) which is together with high tissue contrast advantage of MRI. The disadvantages are longer examination time (15–45 min), price, availability, and several contraindications.


Imaging methods in nuclear medicine are characterized by lower spatial resolution than the radiological methods mentioned above, but they contain very specific functional information. Small amount of a specific radiopharmaceutical is injected into the patient and then emitted gamma photons are detected. Depending on the isotope used, one speaks of positron emission tomography (PET, beta+ tracer) or single photon emission computed tomography (SPECT, gamma tracer). Both methods can be combined into hybrid modalities mostly with CT for obtaining anatomical information.


In the first step, it is necessary to find the pathological area(s) and make a basic description. The process that identifies such areas or, in general, region of interest (ROI) is named segmentation or delineation and can be done manually or with the support of ML algorithms. Manual segmentation is a time consuming and demanding task, with relatively low level of reproducibility so it is beneficial to use semi- or fully automatic methods.


Sometimes it is helpful to segment the pathology based on different histological tissue types, for example, necrotic part, active tumor, or edema but also precise anatomical localization, diameter or volume, shape, intensity, and changes compared to the previous examination.


After all variables are collected, the radiologist must decide which kinds of pathologies it could be. Occasionally, the radiological diagnoses are not unequivocal, for example, the pathology looks like high grade gliomas but with non-negligible probability it could be metastasis of some other primary tumor and the treatment of such different entities are completely different. Each hospital produces thousands of images per day, and it is obvious that usage of the same kind of computer algorithms or AI can save time, increase reproducibility and precision of diagnosis.


The concept of radiomics appeared in 2012 [321]. While the traditional analysis of imaging is based on the visual interpretation of simple features—such as tumor size, general shape, contrast uptake, or signal intensity—radiomics processes any type of imaging computationally and translates into complex quantitative data. Radiomics is based on qualitative and quantitative analyses, combining numerical data from medical imaging with clinical and biological characteristics to obtain predictive and/or prognostic information about patients. Indeed, the study of cellular interaction within tissues and intrinsic characteristics of medical imaging reflect the physiology and pathophysiology of the affected organ. The radiomics approach is: (1) Noninvasive; (2) Allows an evaluation of the studied tissues in their globality, thus characterizing their spatial heterogeneity; (3) Represents an easy way to follow the patient over time, allowing understanding the changes throughout the history of the disease and the therapeutic sequence. A typical radiomics workflow follows five steps as illustrated in Fig. 5.30.

A workflow diagram begins with data selection, followed by imaging and segmentation, feature extraction and selection, modeling, and then to reporting results.

Fig. 5.30
Typical radiomics workflow. The different steps are: (1) Data selection: choosing the image to analyze, the imaging protocol to use and the correlated outcome. (2) Imaging and segmentation with (semi-) automatic methods to improve reproducibility. (3) Feature extraction and selection with appropriate algorithms. (4) Modeling using available machine learning models. (5) Reporting results. (Adopted from Keek et al. [322] with permission)


A workflow diagram begins with data selection, followed by imaging and segmentation, feature extraction and selection, modeling, and then to reporting results.


Combining radiomics features with deep learning features or semantic features may further improve prognostic performance. The changes over time may also be integrated (delta-radiomics). Several studies have proven the effectiveness of using these features independently in predictive modeling. As was mentioned above, radiologists make basic descriptions of pathology like volume, shape, etc. but the number of these descriptors are limited by the radiologist’s time and his/her eyes. But there exist tens or hundreds of different descriptors which can describe pathology and a method which analyses all these descriptive parameters is called Radiomics and these parameters are called features.


One can divide radiomics features into several classes like: (1) First order; (2) Two and three (2D/3D) Shape and; (3) Grey level class (e.g., Size zone, Neighboring tone, Run length, Co-occurrence, Dependency).


The first order features (more than 15) characterized distribution of voxels intensities so they can be commonly known histogram parameters like median, mean, or several quartiles. But they also include mathematically sophisticated parameters like energy, entropy, mean absolute deviation, root mean squared, skewness, or kurtosis (“Sharpness of the peak”).


Shape features (2D or 3D, more than 20) are intensity-independent parameters which are extracted from segmented binary mask image or triangle mash. For example, 2D features can be mesh surface, perimeter, sphericity, maximum 2D diameter or elongation. As 3D shape features, one can mention, for example, mesh volume, surface area to volume ratio, compactness, or flatness.


The biggest features class (which can be subdivided) with more than 50 features is grey level class. For example, grey level size zone features are trying to quantify connected voxels in an image which share the same intensity and one can extract features like grey level non-uniformity, size-zone non-uniformity, grey level variance or zone entropy, etc. Neighboring grey tone features quantify differences between intensity of voxel and average intensity of neighbors’ voxels within defined distance and one can extract features like coarseness, contrast, complexity, or strength.


Radiomics create a model to predict clinical outcomes based on extracted features. Not all features have to be used, selection of features are done before modeling because lots of them are correlated to each other or can be unstable across a dataset. Clinical outcome which radiomics model can be diagnosis (benign or malignant, subtype or stage), treatment evaluation, or prognosis (survival coefficients).


These days there exist hundreds of papers which evaluate the usefulness of radiomics in clinical practice mostly on CT data, but MRI and PET are becoming more common. Radiomics can be used in diagnosis as well as treatment evaluation of different oncological diseases like brain tumors, breast, lung-, prostate-, or colorectal cancer. Radiomics are being applied in the field of oncology in different settings to help decision-making such as:
Differentiation between human papillomavirus-positive and human papillomavirus-negative oropharyngeal tumors on contrast-enhanced CT [323].

Prediction of tumor aggressiveness in prostate cancer [324].

Assistance to automatic segmentation and sub-target volume definition in prostate cancer [325].

Prediction of treatment response and outcome in head and neck and lung cancer (with combination of genomic features) [326–328], rectum [329], esophageal [330], or prostate cancer [331].

Prediction of toxicity in head and neck (xerostomia) [332] and lung (pneumonitis) [333] cancers.

Differential diagnosis between recurrence and RT-induced radionecrosis in brain [334].


Differentiation between human papillomavirus-positive and human papillomavirus-negative oropharyngeal tumors on contrast-enhanced CT [323].


Assistance to automatic segmentation and sub-target volume definition in prostate cancer [325].


Prediction of treatment response and outcome in head and neck and lung cancer (with combination of genomic features) [326–328], rectum [329], esophageal [330], or prostate cancer [331].


Prediction of toxicity in head and neck (xerostomia) [332] and lung (pneumonitis) [333] cancers.