| --- |
| license: cc-by-3.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - pet |
| - ct |
| - mri |
| - sarcoma |
| - oncology |
| - extremity |
| - tumor-segmentation |
| - rtstruct |
| - dicom |
| - tcia |
| - multimodal |
| pretty_name: Soft-tissue Sarcoma (joint FDG-PET/CT and MRI) |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: preview |
| path: data/preview-* |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: patient_id |
| dtype: string |
| - name: role |
| dtype: string |
| - name: frame |
| dtype: string |
| - name: sequence |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: is_gold_tier |
| dtype: bool |
| - name: is_propagated |
| dtype: bool |
| - name: is_derived_crop |
| dtype: bool |
| - name: orientation |
| dtype: string |
| - name: n_slices |
| dtype: int32 |
| - name: slice_index |
| dtype: int32 |
| - name: mass_slice_frac |
| dtype: float32 |
| - name: mass_px |
| dtype: int32 |
| - name: edema_px |
| dtype: int32 |
| - name: has_gtv_mass |
| dtype: bool |
| - name: has_gtv_edema |
| dtype: bool |
| - name: uses_alias_roi_names |
| dtype: bool |
| - name: roi_names |
| dtype: string |
| - name: image_series_uid |
| dtype: string |
| - name: rtstruct_uid |
| dtype: string |
| - name: lung_mets |
| dtype: int32 |
| - name: grade |
| dtype: string |
| - name: site |
| dtype: string |
| - name: histology |
| dtype: string |
| - name: mskcc_type |
| dtype: string |
| - name: mri_to_pet_days |
| dtype: int32 |
| splits: |
| - name: preview |
| num_bytes: 29167123 |
| num_examples: 306 |
| download_size: 29149250 |
| dataset_size: 29167123 |
| --- |
| |
| # Soft-tissue-Sarcoma (STS) |
|
|
| A TCIA collection of **51 patients with histologically proven soft-tissue sarcoma |
| of the extremities**, each imaged with **joint pre-treatment FDG-PET/CT and MRI** |
| and contoured by an expert radiation oncologist. Collected at McGill University |
| Health Centre (Montreal) and published with Vallières et al., *Phys Med Biol* 2015. |
|
|
| The original study built a radiomics model predicting **lung metastases** from |
| joint PET/MRI texture features; 19 of the 51 patients developed lung metastases. |
|
|
| ## Dataset Details |
|
|
| | Field | Value | |
| |---|---| |
| | Modalities | FDG-PET, CT, MRI (T1 + T2FS-or-STIR) + DICOM RTSTRUCT contours | |
| | Body part | `EXTREMITY` (all 612 series) — 28/51 thigh, remainder other limb/girdle sites | |
| | Task | 3D tumour segmentation (single foreground structure) | |
| | Patients | 51 — `STS_001` … `STS_051`, contiguous, no gaps | |
| | Studies | 102 — exactly 2 per patient (one MRI session, one PET/CT session) | |
| | Series | 612 = 51 CT + 51 PT + 204 MR + 306 RTSTRUCT (**exactly 12 per patient**) | |
| | Images | 38,283 DICOM | |
| | Size | 9.19 GiB (TCIA quotes 9.87 GB) | |
| | Scanners | PET/CT: GE Discovery ST. MRI: GE / Philips / Siemens / Varian (multi-vendor) | |
| | License | **CC BY 3.0 Unported** — commercial use permitted | |
| | DOI | `10.7937/K9/TCIA.2015.7GO2GSKS` | |
| | Registration | None — fully public | |
|
|
| **There is no official train/val/test split** and no named subsets. Splitting is |
| left to the consumer. Natural strata that do exist: lung metastases 19 / 32, |
| T2FS 26 / STIR 25, `GTV_Edema` present 32 / absent 19. |
|
|
| ## Per-patient structure — 6 image series + 6 RTSTRUCT |
|
|
| Every one of the 51 patients has exactly the same 12 series, in **two frames of |
| reference**: |
|
|
| | Frame | Image series | Paired RTSTRUCT | n | |
| |---|---|---|---| |
| | **Native MRI** | MR T1 | `RTstruct_T1` | 51 | |
| | **Native MRI** | MR T2FS *or* STIR | `RTstruct_T2FS` / `RTstruct_STIR` | 26 / 25 | |
| | PET/CT | CT | `RTstruct_CT` | 51 | |
| | PET/CT | PT (FDG-PET) | `RTstruct_PET` | 51 | |
| | PET/CT (crop) | `Aligned_T1toPET_BOX` | `RTstruct_AlignedT1toPET` | 51 | |
| | PET/CT (crop) | `Aligned_{T2FS,STIR}toPET_BOX` | `RTstruct_Aligned{T2FS,STIR}toPET` | 26 / 25 | |
|
|
| T2FS and STIR are **mutually exclusive and partition all 51 patients**. The paper |
| treats STIR as a fallback under the "T2FS" umbrella: *"When T2-weighted |
| fat-saturated scans were not available, STIR scans were used (n = 25)."* |
|
|
| ## Ground truth — one hand-drawn tier, five propagated |
|
|
| There is a **single annotator** (an expert radiation oncologist), so there is no |
| inter-rater ambiguity. What varies is *drawn* versus *derived*: |
|
|
| - **Gold / primary:** `RTstruct_T2FS` (26) and `RTstruct_STIR` (25) — contours |
| were **manually drawn slice-by-slice on the T2FS/STIR scans**. This is the tier |
| the paper's features were extracted from. |
| - **Propagated:** the other four tiers (`RTstruct_T1`, `RTstruct_CT`, |
| `RTstruct_PET`, `RTstruct_Aligned*toPET`) are **MIM Software rigid-registration |
| propagations** of those contours onto the other grids. |
|
|
| ⚠️ **`ROIGenerationAlgorithm = MANUAL` on all 306 objects, including the |
| propagated ones** — the DICOM tag does *not* distinguish drawn from derived. Use |
| the tier table above, not the tag. |
|
|
| ⚠️ **The PET/CT-frame masks cross a large time gap.** The MRI and PET/CT sessions |
| are a **median 21 days apart** (mean 20.5; range 0–63 days; only **3 same-day**; |
| 32/51 more than 14 days; 10/51 more than 30 days; the PET/CT session **never** |
| precedes the MRI). The PET/CT-frame contours are rigid propagations across that |
| much repositioning, so they are geometrically approximate. Neubauer et al. |
| (MICCAI 2020) found it necessary to have a nuclear medicine physician |
| **re-delineate on PET** rather than trust them. |
|
|
| ### Two ROIs, and they are nested |
|
|
| | ROI | Patients | Meaning | |
| |---|---|---| |
| | `GTV_Mass` | **51 (all)** | tumour mass, **excluding** peritumoural edema — the paper's reference target | |
| | `GTV_Edema` | **32** | tumour **including** peritumoural edema — secondary, used in the paper only to probe segmentation-uncertainty | |
|
|
| ⚠️ **`GTV_Edema` is a nested superset of `GTV_Mass`**, not a disjoint class. Across |
| all 32 patients the in-plane area ratio is ≥ 1.031 (median 1.29, max 8.74), and |
| the edema slice set is a superset of the mass slice set for 31/32 — the sole |
| exception, `STS_021`, has exactly one mass slice with no edema contour. |
|
|
| **Build these as two independent binary targets, never as a `{0,1,2}` label map.** |
| A label map assigns "edema" only to the rim annulus `GTV_Edema \ GTV_Mass`, which |
| is not a structure anyone annotated and scores meaninglessly. |
|
|
| ## Loader gotchas — verified against all 612 series and all 306 RTSTRUCT objects |
|
|
| These are easy to get silently wrong. `series_to_patient.json` and `pairs.json` |
| (shipped at repo root) pre-resolve all of them. |
|
|
| 1. **`SeriesDescription` is typo-ridden.** RTSTRUCT carries `RTStruct_CT`, |
| `RTStruct_PET`, `RTstruct_AlignedT!toPET` (**bang instead of a 1**), |
| `RTstruct_Aligned_T1toPET` / `_STIRtoPET` (stray underscore), plus stray `_BOX` |
| suffixes; the MR side has `Alligned_T1toPET_BOX` (**double L**). After |
| case-insensitive normalisation the counts land exactly on |
| 51/51/51/26/25/51/26/25 = 306. |
|
|
| 2. **Native-MR T1-vs-T2FS/STIR is NOT readable from the description.** The 102 |
| *native* MR series carry free-text clinical protocol names — `AX STIR`, |
| `Axial FSE/T2 Fatsat`, `KNEE *AXT2SP`, `eT1W_TSE_ax SENSE W PICT-PLUS`, |
| `2. AXIAL T1 BOTH LEGS - RESEARCH` … Resolve the sequence by following the |
| normalised RTSTRUCT back through `ReferencedFrameOfReferenceSequence` to the |
| series it references. `pairs.json` has already done this. |
|
|
| 3. **The other 102 MR series are MIM-derived crops**, distinguishable *only* by |
| `Manufacturer` containing `MIM Software` (e.g. `SIEMENS / MIM Software`). The |
| `Aligned_*_BOX` volumes are **tumour-centred crops**, not full FOV (e.g. |
| 153×123×60), though self-consistent — 0% out-of-bounds contour vertices. |
|
|
| 4. **ROI order is not stable.** 12 of the 192 two-ROI objects list `GTV_Edema` |
| first. Map by `ROIName`, never by `ROINumber` or sequence index. |
|
|
| 5. **`STS_046` renames its ROIs.** On its PET/CT-frame RTSTRUCTs the names are |
| `GTV_Research` (= mass) and `GTV_Res+edema` (= edema). Hard-matching the |
| literal string `"GTV_Mass"` yields a **silently empty mask** for that patient. |
| Match with an alias set. |
|
|
| 6. **Never group contours by z — the gold scans are not all axial.** Of the 51 |
| annotated T2FS/STIR scans, **45 are axial, 4 coronal and 2 sagittal** (for the |
| 32 edema patients specifically: 27 / 4 / 1), and contours are slightly oblique. |
| Use `ReferencedSOPInstanceUID` — it is present on every contour, and every |
| referenced SOP exists in its series. |
|
|
| 7. **Foreground is sparse on the PET/CT grid, but not on the gold tier.** |
| `GTV_Mass` covers a median of only **13.6%** of CT slices (min 5.6%, max 36.3%; |
| 14/51 patients under 10%), so a `debug_n_samples: 1` smoke test against the |
| PET/CT frame will look degenerate through no fault of the loader. The **gold |
| T2FS/STIR tier is not sparse** — median **50.0%** slice coverage (min 20.0%, |
| max 90.0%, none under 15%), so single-sample debug runs on the default target |
| behave normally. |
|
|
| 8. **Frames of reference.** T1 and T2FS/STIR share one `FrameOfReferenceUID` (same |
| MRI session), so T1↔T2FS propagation is geometrically trivial; the PET/CT frame |
| is separate. |
|
|
| 9. **Dates are shifted** for de-identification (intervals preserved), so |
| `StudyDate` values are not real. 84 of the 612 series carry |
| `DateReleased = 2020-05-21` — a partial resubmission over the 2015 release. |
|
|
| ## Cross-dataset overlap |
|
|
| - ⚠️ **SAROS shares 6 patients**: `STS_001`, `STS_007`, `STS_008`, `STS_026`, |
| `STS_040`, `STS_044` → SAROS `case_815`, `case_636`, `case_678`, `case_714`, |
| `case_703`, `case_753`. The cross-reference lives in TCIA's |
| [Segmentation-Info CSV](https://www.cancerimagingarchive.net/wp-content/uploads/Segmentation-Info_09-29-2023.csv) |
| under `tcia_case_id`. SAROS labels **body regions**, not tumour, so the risk is |
| CT-image contamination rather than tumour-label leakage. |
| - ⚠️ **MSTT-199 uses all 51 of these patients as its external test set** (reported |
| Dice 0.79 U-Net / 0.80 LiteMedSAM). Do not train on STS and evaluate on MSTT-199. |
| - **No overlap** with MSD (no sarcoma task), the BraTS family, MedSAM's training |
| corpus, SA-Med2D-20M, autoPET, HECKTOR (same PI and lab, different patients and |
| disease), QIN-SARCOMA (OHSU, DCE-MRI only, no segmentations), or **TCGA-SARC** |
| (disjoint cohort — TCGA-SARC has no McGill/Montreal site, and its TCIA arm is |
| 5 `TCGA-QQ-*` patients). |
| - ⚠️ `INFOclinical_STS.xlsx` has **no cross-reference ID column**. Its |
| `MSKCC type` column is a *histological classification scheme* |
| (Liposarcoma / Leiomyosarcoma / MFH / Other) — **not** a link to MSKCC or TCGA. |
|
|
| ## Provenance |
|
|
| Official, author-deposited TCIA collection. All 51 patients from the paper are |
| present — **no count mismatch**. Third-party re-hosts exist and **all of them |
| misstate the license or the content**; prefer this mirror or TCIA directly: |
|
|
| | Re-host | Problem | |
| |---|---| |
| | Kaggle `4quant/soft-tissue-sarcoma` | HDF5 at 5 mm isotropic, **PET/CT only — no MRI**; ~4% of the original | |
| | `reasat/sts-reg` | NIfTI, T1→T2 registered; claims **CC0** (understates CC BY 3.0) | |
| | `husnainrasool/tcia-sarcoma-ds` | verbatim re-upload but claims **MIT** (wrong license class) | |
| | hyper.ai listing | labels it **Non-Commercial** — incorrect; CC BY 3.0 permits commercial use | |
|
|
| **Faithful-naming disclosures for this mirror:** |
| - 25 of the 51 "T2FS" scans are actually **STIR** — the paper's own umbrella term, |
| not a mirroring error. |
| - **Half the MR series (102/204) are derived** MIM registered crops, not raw |
| acquisitions. This is a raw + derived mix, exactly as TCIA publishes it. |
| - Body part is tagged `EXTREMITY` throughout, but roughly 10/51 primary sites are |
| girdle/trunk (buttock, pelvis, groin, parascapular) rather than true limb. |
|
|
| ## Structure |
|
|
| ``` |
| images/<PatientID>/<SeriesInstanceUID>/*.dcm # CT, PT, MR — 306 series |
| segmentations/<PatientID>/<SeriesInstanceUID>/*.dcm # RTSTRUCT — 306 objects |
| clinical/INFOclinical_STS.xlsx # demographics + outcome vector |
| clinical/Soft-tissue-Sarcoma-nbia-digest.xlsx # official NBIA series digest |
| series_to_patient.json # all 612 series, normalised `role` |
| pairs.json # RTSTRUCT -> image series + ROI names |
| LICENSE.txt |
| ``` |
|
|
| **`series_to_patient.json`** keys each `SeriesInstanceUID` to `PatientID`, |
| `StudyInstanceUID`, `Modality`, `SeriesDescription`, a normalised **`role`** |
| (`ct`, `pet`, `mr_t1`, `mr_t2fs`, `mr_stir`, `aligned_t1_to_pet`, |
| `aligned_t2fs_to_pet`, `aligned_stir_to_pet`), `Manufacturer`, `ImageCount`, |
| `FileSize`, license/DOI and the relative `path`. |
|
|
| **`pairs.json`** keys each RTSTRUCT `SeriesInstanceUID` to the |
| **`image_series_uid` it is drawn on** (resolved from |
| `ReferencedFrameOfReferenceSequence`, not from the typo-ridden description), |
| `frame_of_reference_uid`, the literal `roi_names`, `roi_names_with_contours`, |
| `n_referenced_sop`, and the boolean flags `has_gtv_mass`, `has_gtv_edema` and |
| `uses_alias_roi_names`. Pairing therefore needs **no TCIA round-trip and no |
| description parsing**. |
|
|
| ## Benchmark reference point |
|
|
| Neubauer et al., *Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT |
| Data* (MICCAI MMMI 2020, [arXiv:2008.12544](https://arxiv.org/abs/2008.12544)): |
| 39/51 patients after cropping to the leg region, 5-fold CV — best **T2 Dice |
| 77.2 ± 16.5%**, **PET Dice 74.6 ± 19.0%**, T2-only baseline 65.6 ± 24.0%. They |
| resampled in-plane to 0.75 mm and kept the native T2 slice distance to avoid |
| interpolation artifacts. |
|
|
| ## Source & Citation |
|
|
| - TCIA collection: https://www.cancerimagingarchive.net/collection/soft-tissue-sarcoma/ |
| - DOI: `10.7937/K9/TCIA.2015.7GO2GSKS` |
|
|
| ```bibtex |
| @article{vallieres2015sts, |
| author = {Valli{\`e}res, Martin and Freeman, Carolyn R. and |
| Skamene, Sonia R. and El Naqa, Issam}, |
| title = {A radiomics model from joint {FDG-PET} and {MRI} texture features |
| for the prediction of lung metastases in soft-tissue sarcomas of |
| the extremities}, |
| journal = {Physics in Medicine and Biology}, |
| volume = {60}, |
| number = {14}, |
| pages = {5471--5496}, |
| year = {2015}, |
| doi = {10.1088/0031-9155/60/14/5471} |
| } |
| |
| @misc{vallieres2015stsdata, |
| author = {Valli{\`e}res, M. and Freeman, C. R. and Skamene, S. R. and |
| El Naqa, I.}, |
| title = {A radiomics model from joint {FDG-PET} and {MRI} texture features |
| for the prediction of lung metastases in soft-tissue sarcomas of |
| the extremities [Data set]}, |
| year = {2015}, |
| publisher = {The Cancer Imaging Archive}, |
| doi = {10.7937/K9/TCIA.2015.7GO2GSKS} |
| } |
| |
| @article{clark2013tcia, |
| author = {Clark, Kenneth and Vendt, Bruce and Smith, Kirk and others}, |
| title = {The Cancer Imaging Archive ({TCIA}): Maintaining and Operating a |
| Public Information Repository}, |
| journal = {Journal of Digital Imaging}, |
| volume = {26}, |
| number = {6}, |
| pages = {1045--1057}, |
| year = {2013}, |
| doi = {10.1007/s10278-013-9622-7} |
| } |
| ``` |
|
|
