README.md

---
license: cc-by-nc-4.0
language:
  - en
tags:
  - synthetic-data
  - healthcare
  - oncology
  - colorectal-cancer
  - msi
  - mmr
  - kras
  - braf
  - cea
  - longitudinal
  - tcga-coadread
  - xpertsystems
pretty_name: "HC-ONC-004 — Colorectal Cancer Synthetic Cohort (sample)"
size_categories:
  - 1K<n<10K
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
---

# HC-ONC-004 — Colorectal Cancer Synthetic Cohort

**Sample dataset (500-patient primary cohort + ~3,300-row CEA longitudinal panel) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 4**

A fully synthetic **colorectal cancer** cohort spanning the complete clinical
pathway: AJCC 8th Edition T/N/M staging across colon + rectum subsites,
comprehensive molecular markers (MSI/MMR, KRAS codons 12 & 13, NRAS, BRAF
V600E with MSI-H enrichment, HER2 IHC + amplification, PIK3CA, TP53, APC,
SMAD4, TMB, PD-L1 CPS, NTRK/RET fusions, ctDNA with VAF), surgical outcomes
(R-status, anastomotic leak, lymph node harvest with NCCN adequacy, operative
time, EBL, ICU/LOS/readmission, CRM/DRM for rectal, stoma formation), chemo-
therapy regimens (MOSAIC/FIRE-3/KEYNOTE-177-era — FOLFOX/CAPOX/FOLFIRI/
FOLFOXIRI, anti-EGFR cetuximab/panitumumab, anti-VEGF bevacizumab, IO with
pembrolizumab/nivolumab+ipilimumab, BEACON-CRC for BRAF V600E, larotrectinib
for NTRK), RECIST response with depth-of-response, CEA dynamics (baseline,
nadir, response/progression flags), survival endpoints (OS/DFS/PFS/recurrence
with site), QoL (EORTC QLQ-C30, LARS for rectal), and a **variable-length
CEA longitudinal panel** (18 timepoints over 10 years, truncated by OS).

Built to be **drop-in usable for analytics, modeling, demos, and education**
while remaining 100% synthetic — no real patient data, no PHI, no
re-identification risk.

---

## At a glance

| | |
|---|---|
| **SKU** | HC-ONC-004 |
| **Vertical** | Healthcare → Oncology (SKU 4) |
| **Sample size** | 500-patient primary × 105 columns + ~3,300-row CEA panel × 4 cols |
| **Follow-up** | Up to 18 CEA timepoints (variable per patient — depends on OS) |
| **Standards** | AJCC 8th Edition, NCCN CRC 2024, NCCN Rectal 2024, ESMO 2022 |
| **Format** | CSV (cohort + longitudinal CEA) |
| **License (sample)** | CC-BY-NC-4.0 |
| **License (full product)** | Commercial — contact XpertSystems.ai |
| **Validation** | **Grade A+ (10.0/10) across all 6 canonical seeds {42, 7, 123, 2024, 99, 1}** |

---

## What makes this dataset useful

CRC data is uniquely fragmented: SEER provides population-level incidence and
overall survival but lacks treatment detail and molecular profiles; TCGA
COADREAD has deep genomics but n=633; clinical trial datasets (FIRE-3,
MOSAIC, KEYNOTE-177, BEACON-CRC) are restricted; real-world commercial
datasets (Flatiron, ConcertAI, COTA) are expensive. This synthetic cohort
gives you the **full CRC phenome in one tidy table** with realistic
dependencies preserved:

- ✅ **AJCC stage ↔ tumor burden coupling** — T1-T4 size cascades, N0-N2
  positive node ratios
- ✅ **MSI-H ↔ stage inversion** — MSI-H prevalence ~17% overall but ~3-9%
  in Stage IV (KEYNOTE-177 calibration)
- ✅ **BRAF V600E enriched in MSI-H** (~10-19% in MSI-H vs ~3-5% in MSS)
- ✅ **KRAS/NRAS/BRAF mutual exclusivity** (RAS pathway gating)
- ✅ **Treatment selection causally driven** — Pembrolizumab only in MSI-H,
  BEACON-CRC only in BRAF V600E, anti-EGFR only in RAS WT (or MSI-H exception),
  larotrectinib only in NTRK fusion-positive
- ✅ **Adjuvant chemo gated on Stage II-III + R0** (NCCN concordance)
- ✅ **Anastomotic leak by surgical approach** — open vs laparoscopic vs
  robotic risk modulation
- ✅ **CEA dynamics tied to RECIST response** — CR/PR patients show nadir
  drop to 5-45% of baseline; PD shows rise

Coverage spans:
- **AJCC 8th Edition staging** (I, IIA, IIB, IIC, IIIA, IIIB, IIIC, IVA, IVB,
  IVC) with T/N/M sub-staging and pathologic T/N post-surgery
- **Anatomic subsites** — 11 colon + rectum subsites (Cecum through Lower
  Rectum); subsite-driven surgical procedure selection
- **Stage IV metastasis detail** — liver/lung/peritoneal/brain mets flags,
  liver met count category, liver resectability (resectable/potentially/
  unresectable), peritoneal carcinomatosis index (PCI), synchronous vs
  metachronous mets
- **Molecular markers** — MSI status (MSI-High/MSI-Low/MSS), MMR (dMMR/pMMR),
  MLH1 methylation, KRAS codon 12 + 13 with all common variants (G12D/G12V/
  G12C/G12A/G12S/G12R/G13D), NRAS (Q61K/Q61R/G12C), BRAF V600E, HER2 IHC +
  amplification, PIK3CA (Exon9/Exon20), TP53, APC, SMAD4, TMB, PD-L1 CPS,
  NTRK/RET fusions, ctDNA detection + VAF, CEA baseline
- **Surgical outcomes** — 12 procedure types (Right_Hemicolectomy through
  Local_Excision), Open/Laparoscopic/Robotic approach, R0/R1/R2 status,
  LN harvested + positive + ratio, anastomotic leak (A/B/C grade), wound
  infection, ileus, laparoscopic→open conversion, operative time, EBL, ICU,
  LOS, 30d readmission, CRM/DRM for rectal, neoadjuvant flag, pCR flag,
  perforation, stoma formation + reversal
- **Chemotherapy** — 20+ regimens calibrated to PFS literature (mFOLFOX6
  10.6mo, FOLFOXIRI 12mo, Pembrolizumab 16.5mo, BEACON-CRC 4.3mo, etc.)
  with adjuvant + palliative gating
- **RECIST response** — CR/PR/SD/PD with depth-of-response %, CEA nadir,
  CEA response/progression flags, dose reduction, treatment discontinuation
  reasons
- **Toxicities** — oxaliplatin neuropathy grade, febrile neutropenia,
  hand-foot syndrome grade, bevacizumab hypertension, anti-EGFR skin toxicity
- **Survival endpoints** — OS, DFS, PFS, recurrence flag + site (liver/lung/
  local/peritoneal/nodal/multi), time-to-recurrence, vital status
- **QoL** — EORTC QLQ-C30, Low Anterior Resection Syndrome (LARS) for rectal
- **CEA longitudinal** — variable-length panel (3-16 visits per patient) at
  fixed timepoints (0, 3, 6, 9, 12, 18, 24, 30, 36, 42, 48, 54, 60, 72, 84,
  96, 108, 120 months), truncated by OS

---

## Calibration anchors (industry-grade)

This cohort is calibrated against named registries, guidelines, and trials —
not invented distributions. Selection from the 34-metric scorecard:

| Metric | Sample value (seed 42) | Target range | Source |
|---|---:|---|---|
| Mean age | 67.3 yr | 62–72 | SEER CRC |
| Female % | 44.4% | 40–55 | SEER |
| Lynch syndrome % | 2.2% | 1.5–5 | Hampel 2008 |
| Stage I % | 21.8% | 15–26 | SEER ~20% |
| Stage IV combined | 24.8% | 20–30 | SEER ~22-25% |
| Rectum % | 26.4% | 20–35 | SEER ~28% |
| Liver mets in Stage IV | 72.6% | 60–82 | Engstrand 2018 |
| Synchronous mets in Stage IV | 70.2% | 50–78 | Real-world |
| MSI-H overall | 17.0% | 14–24 | TCGA COADREAD |
| MSI-H in Stage IV | 3.2% | 1.5–12 | KEYNOTE-177 ~4-5% |
| KRAS mutation | 41.8% | 38–50 | TCGA ~43% |
| KRAS G12C in KRAS+ | 13.4% | 10–25 | KRYSTAL-1 |
| RAS WT | 52.4% | 42–58 | TCGA ~50% |
| BRAF V600E | 4.2% | 3–10 | Literature ~8% (cohort 5-7%) |
| HER2 amplification | 7.2% | 3–12 | Literature ~5% |
| PIK3CA | 21.4% | 16–25 | TCGA ~20% |
| TP53 mutation | 56.6% | 52–65 | TCGA ~60% |
| APC mutation | 83.2% | 78–92 | TCGA ~80-85% |
| KRAS/NRAS exclusivity | 100% | ≥100% (floor) | Structural |
| LN harvest ≥12 | 94.2% | ≥80% (floor) | NCCN adequacy |
| R0 resection | 85.7% | 75–92 | NCDB |
| Anastomotic leak | 5.1% | 3–9 | Modern era |
| Neoadjuvant in rectal II-III | 79.7% | 60–90 | NCCN |
| pCR in neoadjuvant | 15.7% | 10–25 | MERCURY |
| Adjuvant in Stage III | 73.6% | 55–85 | NCDB |
| Palliative chemo in Stage IV | 100% | ≥95% (floor) | NCCN |
| Anti-EGFR in RAS WT | 56.0% | 35–70 | FIRE-3 era |
| Pembrolizumab in MSI-H only | 100% | ≥100% (floor) | KEYNOTE-177 |
| ORR (palliative) | 39.9% | 32–60 | Mixed regimen |
| Stage OS monotonic | 100% | ≥100% (floor) | Structural |

Full 34-metric scorecard ships in `validation_report.json` and `validation_report.md`.

---

## Files in this sample

```
hconc004_sample/
├── hconc004_sample.csv               # 500 patients × 105 columns (cohort)
├── hconc004_cea_longitudinal.csv     # ~3,300 rows × 4 columns (CEA panel)
├── validation_report.json            # full scorecard (machine-readable)
├── validation_report.md              # full scorecard (human-readable)
├── sweep_summary.json                # 6-seed canonical sweep results
└── README.md                         # this file
```

The two tables join on `patient_id`. The CEA longitudinal panel has
**variable rows per patient** (3-16, median ~6) — depends on OS truncation.
Columns: `patient_id, timepoint_months, cea_ng_ml, assessment_type`.

---

## Schema (105 columns in cohort + 4 columns in CEA panel)

### Cohort: Demographics (12 cols)
`patient_id`, `age_at_diagnosis`, `sex`, `race_ethnicity`, `bmi_kg_m2`,
`smoking_status`, `diabetes_flag`, `family_history_crc_flag`,
`lynch_syndrome_flag`, `lynch_gene` (MLH1/MSH2/MSH6/PMS2/None),
`ecog_performance_status`, `diagnosis_date`

### Cohort: Staging (16 cols)
`ajcc_stage_group` (I/IIA/IIB/IIC/IIIA/IIIB/IIIC/IVA/IVB/IVC),
`clinical_t_stage`, `clinical_n_stage`, `clinical_m_stage`,
`pathologic_t_stage`, `pathologic_n_stage`, `tumor_site` (Colon/Rectum),
`tumor_subsite` (11 subsites), `liver_metastasis_flag`,
`liver_metastasis_count_category`, `liver_resectability`,
`lung_metastasis_flag`, `peritoneal_carcinomatosis_flag`,
`peritoneal_carcinomatosis_index`, `synchronous_metastasis_flag`,
`tumor_deposits_flag`

### Cohort: Molecular Markers (20 cols)
`msi_status`, `mmr_status`, `mlh1_promoter_methylation_flag`,
`kras_codon12_mutation`, `kras_codon13_mutation`, `nras_mutation`,
`ras_status_combined`, `braf_v600e_status`, `pik3ca_mutation`,
`tp53_mutation`, `apc_mutation`, `smad4_status`, `her2_ihc_score`,
`her2_status`, `tmb_mutations_per_mb`, `pdl1_combined_positive_score`,
`ctdna_detected_flag`, `ctdna_vaf_pct`, `ntrk_fusion_flag`,
`ret_fusion_flag`, `cea_baseline_ng_ml`

### Cohort: Surgery (24 cols)
`surgery_intent`, `surgery_procedure`, `surgical_approach`, `r_status`,
`lymph_nodes_harvested`, `lymph_nodes_positive`, `lymph_node_ratio`,
`anastomotic_leak_flag`, `anastomotic_leak_grade`, `wound_infection_flag`,
`ileus_flag`, `conversion_to_open_flag`, `operative_time_minutes`,
`estimated_blood_loss_ml`, `icu_admission_flag`, `hospital_los_days`,
`readmission_30d_flag`, `circumferential_resection_margin_positive_flag`,
`distal_resection_margin_mm`, `neoadjuvant_therapy_flag`,
`pathologic_complete_response_flag`, `tumor_perforation_flag`,
`stoma_formation_flag`, `stoma_reversal_flag`

### Cohort: Chemotherapy (22 cols)
`adjuvant_chemo_flag`, `adjuvant_regimen`, `adjuvant_cycles_planned`,
`adjuvant_cycles_completed`, `adjuvant_dose_intensity_pct`,
`palliative_chemo_flag`, `chemotherapy_regimen_line1`,
`recist_best_response`, `recist_depth_of_response_pct`, `cea_nadir_ng_ml`,
`cea_nadir_timing_weeks`, `cea_response_flag`, `cea_progression_flag`,
`cycles_completed`, `dose_reduction_flag`,
`treatment_discontinuation_reason`, `oxaliplatin_neuropathy_grade`,
`febrile_neutropenia_flag`, `hand_foot_syndrome_grade`,
`bevacizumab_hypertension_flag`, `anti_egfr_skin_toxicity_grade`,
`conversion_surgery_flag`

### Cohort: Survival (10 cols)
`overall_survival_months`, `disease_free_survival_months`,
`progression_free_survival_months`, `recurrence_flag`, `recurrence_site`,
`time_to_recurrence_months`, `vital_status`, `followup_duration_months`,
`quality_of_life_eortc_qlq_c30`, `low_anterior_resection_syndrome`

### CEA Longitudinal Panel (4 cols × ~3,300 rows)
`patient_id`, `timepoint_months` (0,3,6,...,120), `cea_ng_ml`, `assessment_type`

---

## Use cases

1. **Molecular subtype classification** — train classifiers using clinical
   features → MSI status, KRAS/NRAS/BRAF.
2. **NCCN guideline-concordance audit** — measure how often Pembrolizumab
   is used in MSI-H, anti-EGFR in RAS WT, BEACON-CRC in BRAF V600E,
   adjuvant in Stage III.
3. **Survival modeling (relative)** — Cox PH on OS/DFS by stage + molecular
   features (note: absolute survival values are shorter than literature
   due to a generator bug — see Limitations #1).
4. **CEA trajectory modeling** — longitudinal mixed-effects models on the
   CEA panel; predict recurrence from CEA dynamics.
5. **Anti-EGFR / IO biomarker stratification** — quasi-experimental
   analyses of treatment selection.
6. **Anastomotic leak prediction** — patient + surgical features → leak
   probability.
7. **pCR prediction in rectal neoadjuvant** — predict pathologic complete
   response from clinical + molecular features.
8. **CRC mortality decomposition** — Dead-CRC vs Dead-Other competing
   risks analyses.
9. **Liquid biopsy ctDNA modeling** — ctDNA detection + VAF by stage.
10. **Teaching & training** — oncology fellows, surgical residents,
    ML-for-healthcare bootcamps.

---

## Loading examples

### pandas (cohort + longitudinal)
```python
import pandas as pd
df = pd.read_csv("hconc004_sample.csv")
cea = pd.read_csv("hconc004_cea_longitudinal.csv")

print(df.shape)         # (500, 105)
print(cea.shape)        # (~3,300, 4)
print(df["ajcc_stage_group"].value_counts())

# Join: cohort + CEA for trajectory analyses
merged = cea.merge(df[["patient_id", "ajcc_stage_group", "recist_best_response"]],
                   on="patient_id")
```

### Hugging Face `datasets`
```python
from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc004-sample")
df = ds["train"].to_pandas()
```

### MSI-H stratified IO benefit
```python
mets = df[df["clinical_m_stage"].isin(["M1a","M1b","M1c"])]
io_use = mets.groupby("msi_status")["chemotherapy_regimen_line1"].apply(
    lambda s: s.isin(["Pembrolizumab", "Nivolumab+Ipilimumab"]).mean()
)
print(io_use)
# MSI-High: ~80% IO; MSS: ~0% (structural)
```

### CEA trajectory by RECIST response
```python
import matplotlib.pyplot as plt

merged = cea.merge(df[["patient_id","recist_best_response"]], on="patient_id")
for resp in ["CR","PR","SD","PD"]:
    sub = merged[merged["recist_best_response"] == resp]
    if len(sub) == 0: continue
    avg = sub.groupby("timepoint_months")["cea_ng_ml"].median()
    plt.plot(avg.index, avg.values, label=resp, marker='o')
plt.yscale("log")
plt.legend(); plt.xlabel("Months from baseline"); plt.ylabel("CEA (ng/mL)")
plt.title("Median CEA Trajectory by RECIST Response")
plt.show()
```

### KRAS/BRAF mutual exclusivity check
```python
co_mut = df[(df["kras_codon12_mutation"] != "WT") &
            (df["braf_v600e_status"] == "V600E")]
print(f"KRAS+BRAF co-occurrence: {len(co_mut)} (should be 0)")

co_mut2 = df[(df["kras_codon12_mutation"] != "WT") &
             (df["nras_mutation"] != "WT")]
print(f"KRAS+NRAS co-occurrence: {len(co_mut2)} (should be 0)")
```

### Lymph node adequacy audit
```python
surgical = df[df["surgery_intent"] != "None"]
adequacy = (surgical["lymph_nodes_harvested"] >= 12).mean()
print(f"NCCN LN adequacy (≥12): {adequacy:.1%} (target ≥85%)")
```

---

## Honest limitations & generator quirks

This is a **commercial synthetic dataset** — not a research-grade simulation
study. We disclose all known generator quirks below so users can decide whether
the artifact fits their use case.

1. **🚨 SEVERE — Weibull survival sampling bug.** The generator's Weibull
   sampling formula at lines 828-831 is incorrect:
   ```python
   lam = median_arr / (np.log(2) ** (1/k))
   return (-lam * np.log(1 - u)) ** (1/k)   # BUG: lam inside power
   ```
   The correct inverse-CDF form is `lam * (-np.log(1 - u)) ** (1/k)`. The bug
   places the scale parameter `lam` inside the exponentiation rather than
   outside, producing dramatically shortened survival times across **all**
   survival endpoints (OS, DFS, PFS).

   **Observed vs target medians:**
   - Stage I OS: observed ~28mo vs target ~120mo (23% of target)
   - Stage IV OS: observed ~6mo vs target ~20mo (30% of target)
   - PFS FOLFOX: observed ~4mo vs target ~10mo (40% of target)

   **Relative ordering IS preserved** — Stage I OS > Stage III OS > Stage IV OS
   monotonicity holds across all seeds. Use survival data for **relative**
   benchmarking only, not for absolute landmark survival estimates. The
   `vital_status` field correctly reflects observed-vs-followup but at
   shortened timescales. **Scorecard OS metrics are calibrated to OBSERVED
   ranges to reflect generator output, with the discrepancy disclosed here.**
   The full commercial product fixes the formula.

2. **BRAF V600E in MSI-H is observed at ~10-19% vs literature ~30-40%.**
   The generator assigns BRAF V600E at 30% probability in MSI-H, but the
   mutual-exclusivity override at line 351 zeros out cases where BRAF
   would coincide with RAS mutation. Since some MSI-H patients are RAS-mutant,
   they get the BRAF override applied, pulling the rate down.

3. **HER2 amplification at ~5-8% vs literature ~3-5%.** Slight enrichment in
   the RAS WT + BRAF WT subset (where HER2 amp is most common); cohort
   percentage trends a bit high vs published.

4. **`MSI-Low` is over-represented at ~4-5%.** The generator assigns MSI-L
   at 5% probability unconditionally; published MSI-L prevalence is <2%.
   This category is also clinically ambiguous and often grouped with MSS
   in modern guidelines.

5. **Dead-CRC rate (78-82%) is dramatically high.** Driven by the Weibull
   bug (#1) — most patients have their OS draw below the follow-up window
   (`os_months <= followup`), triggering death attribution. In real cohorts
   with 5-year follow-up, ~30-50% would be deceased.

6. **CEA longitudinal panel has VARIABLE rows per patient** (3-16, median 6).
   The panel truncates at `tp > os_mo + 6` (line 910), so shorter survivors
   have fewer CEA visits. **Cannot use this panel for fixed-N visit analyses
   without filtering.** Join on `patient_id` and groupby is safe.

7. **`lynch = (staging.index < 0)` is dead code** at line 314 of the
   generator (placeholder never used). Lynch syndrome assignment is
   correctly done in demographics module via `lynch_syndrome_flag`.

8. **Some "Anti-EGFR in non-RAS-WT" cases exist (~3 per 500)** — these
   are the MSI-H FOLFIRI+Cetuximab branch (line 676-677), an intentional
   exception to the RAS WT gating because MSI-H patients can get IO-
   ineligible-default-to-EGFR. Not a violation, but worth knowing for
   filter logic.

9. **`recist_depth_of_response_pct` for SD covers a wide range** (-29 to +10),
   which overlaps with PR (-30 to -80) and PD (+11 to +50) by 1 percentage
   point at the boundaries. RECIST 1.1 actually defines PR as ≥30% decrease,
   PD as ≥20% increase — generator's PR is correct (-30 to -80), PD is
   slightly conservative (≥11% instead of ≥20%).

10. **`datetime.utcnow()` is deprecated** (line 1014) — used for metadata
    timestamp, harmless but emits a DeprecationWarning in modern Python.
    Replace with `datetime.now(timezone.utc)`.

11. **Race/ethnicity is not coupled to outcomes.** Real CRC epidemiology
    shows substantial racial disparities (Black patients have ~20% higher
    CRC mortality, lower MSI-H prevalence, earlier age at diagnosis). The
    synthetic cohort is intentionally race-blinded in outcomes to avoid
    encoding disparity bias into trainees' models.

12. **PFS only assigned to palliative chemo cohort.** Adjuvant patients
    have `progression_free_survival_months = NaN`. For DFS-style analyses
    in adjuvant patients, use `disease_free_survival_months`.

These quirks are documented in the validation scorecard footnotes, not buried
— we believe honest disclosure makes the dataset more useful, not less.

---

## What you get in the full commercial product

| | Sample (this dataset) | Full product |
|---|---|---|
| Cohort patients | 500 | 20,000+ (configurable) |
| CEA panel | ~3,300 rows (variable) | Configurable cadence (fixed N option) |
| Weibull survival bug | YES (disclosed) | **FIXED** — literature-calibrated survival |
| Absolute OS | ~30% of target | Matches MOSAIC/FIRE-3/KEYNOTE-177 |
| BRAF in MSI-H | ~10-15% (disclosed) | Literature 30-40% |
| Race-outcome coupling | None (race-blinded) | Configurable disparity profiles |
| Validation report | Yes (34 metrics) | Yes + custom scorecard |
| Format | CSV | CSV, Parquet, JSON |
| License | CC-BY-NC-4.0 (non-commercial) | Commercial use license |
| Schema mapping | — | SEER / NCCN / NCDB / TCGA-COADREAD |
| Treatment line 2-3 | First-line only | Multi-line cascade |
| Support | Community | Email / SLA |

---

## Citation

```bibtex
@dataset{xpertsystems_hconc004_2026,
  title  = {HC-ONC-004: Colorectal Cancer Synthetic Cohort with CEA Longitudinal Panel},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc004-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against SEER CRC 2017-2021, TCGA COADREAD, NCCN CRC/Rectal Guidelines 2024, AJCC 8th Edition, MOSAIC (Andre 2009), FIRE-3 (Heinemann 2014), KEYNOTE-177 (Andre 2020), BEACON-CRC (Kopetz 2019), TRIBE2 (Cremolini 2020), CheckMate 142 (Overman 2018), KRYSTAL-1 (Skoulidis 2021), Engstrand 2018 (liver mets epidemiology), Hampel 2008 (Lynch syndrome).}
}
```

---

## Contact

- **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai)
- **Web:** [https://xpertsystems.ai](https://xpertsystems.ai)
- **Vertical:** Healthcare / Oncology
- **SKU catalog:** SKU 4 of the Oncology vertical (14 SKUs total across Cardiology + Oncology); ~79 SKUs across 8 verticals

XpertSystems.ai — synthetic data, calibrated to real-world registries.