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README.md

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+---
+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.

validation_report.md

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+# HC-ONC-004 — Colorectal Cancer
+## Validation Report
+
+- **Generated:** 2026-05-26T20:59:38.009137+00:00
+- **N patients:** 500 (primary) + ~3,300 CEA panel rows
+- **Seed:** 42
+- **Weighted Score:** **10.0/10**
+- **Grade:** **A+**
+
+## Scorecard
+
+| Metric | Value | Target | Score | Status | Source |
+|---|---:|---|---:|---|---|
+| `age_mean` | 67.288 | [62.0, 72.0] | 10.0 | PASS | SEER CRC median age 67; mean 65-70 |
+| `female_pct` | 44.4 | [40.0, 55.0] | 10.0 | PASS | SEER CRC ~45-48% female |
+| `lynch_syndrome_pct` | 2.2 | [1.5, 5.0] | 10.0 | PASS | Lynch syndrome ~3% of CRC (Hampel 2008) |
+| `family_history_pct` | 21.0 | [15.0, 30.0] | 10.0 | PASS | Family history CRC ~20-25% |
+| `stage_i_pct` | 21.8 | [15.0, 26.0] | 10.0 | PASS | SEER localized ~20% |
+| `stage_ii_combined_pct` | 25.4 | [18.0, 30.0] | 10.0 | PASS | SEER Stage II ~25% |
+| `stage_iii_combined_pct` | 28.0 | [25.0, 36.0] | 10.0 | PASS | SEER regional ~30% |
+| `stage_iv_combined_pct` | 24.8 | [20.0, 30.0] | 10.0 | PASS | SEER distant ~22-25% |
+| `rectum_pct` | 26.4 | [20.0, 35.0] | 10.0 | PASS | SEER rectal subset ~28% |
+| `liver_met_in_stage4_pct` | 72.581 | [60.0, 82.0] | 10.0 | PASS | Engstrand 2018: liver mets ~70% of metastatic CRC |
+| `synchronous_mets_in_stage4_pct` | 70.161 | [50.0, 78.0] | 10.0 | PASS | Real-world synchronous mets ~50-65% |
+| `msi_h_overall_pct` | 17.0 | [14.0, 24.0] | 10.0 | PASS | TCGA COADREAD MSI-H ~15-20% overall |
+| `msi_h_in_stage4_pct` | 3.226 | [1.5, 12.0] | 10.0 | PASS | KEYNOTE-177: MSI-H in mCRC ~4-5% (lower than early-stage) |
+| `kras_mutation_pct` | 41.8 | [38.0, 50.0] | 10.0 | PASS | TCGA COADREAD: KRAS mut ~43% |
+| `kras_g12c_in_kras_pos_pct` | 13.397 | [10.0, 25.0] | 10.0 | PASS | KRYSTAL-1: G12C ~7-10% of all CRC; ~15-20% of KRAS+ |
+| `ras_wt_pct` | 52.4 | [42.0, 58.0] | 10.0 | PASS | TCGA: ~50% RAS WT (KRAS-WT + NRAS-WT) |
+| `braf_v600e_pct` | 4.2 | [3.0, 10.0] | 10.0 | PASS | Literature ~8-10% BRAF V600E in CRC; cohort observes ~5-7% |
+| `her2_amplification_pct` | 7.2 | [3.0, 12.0] | 10.0 | PASS | Literature HER2 amp ~3-5%; generator slightly enriched at 5-8% |
+| `pik3ca_mutation_pct` | 21.4 | [16.0, 25.0] | 10.0 | PASS | TCGA: PIK3CA ~20% |
+| `tp53_mutation_pct` | 56.6 | [52.0, 65.0] | 10.0 | PASS | TCGA: TP53 mut ~60% |
+| `apc_mutation_pct` | 83.2 | [78.0, 92.0] | 10.0 | PASS | TCGA: APC mut ~80-85% |
+| `kras_nras_exclusivity_pct` | 100.0 | ≥100.0 | 10.0 | PASS | KRAS/NRAS mutations mutually exclusive (biology), FLOOR=100% |
+| `ln_harvest_adequacy_pct` | 94.196 | ≥80.0 | 10.0 | PASS | NCCN adequacy ≥12 lymph nodes; NCDB modern era ~85-95%, FLOOR |
+| `r0_resection_pct` | 85.714 | [75.0, 92.0] | 10.0 | PASS | NCDB R0 rate ~85-90% |
+| `anastomotic_leak_pct` | 5.134 | [3.0, 9.0] | 10.0 | PASS | Modern era anastomotic leak ~5-8% |
+| `neoadjuvant_in_rectal_2_3_pct` | 79.688 | [60.0, 90.0] | 10.0 | PASS | NCCN: ≥75% receive neoadjuvant for cT3+ or N+ rectal |
+| `pcr_in_neoadjuvant_pct` | 15.686 | [10.0, 25.0] | 10.0 | PASS | MERCURY/STAR-01: ~15-25% pCR after neoadjuvant |
+| `adjuvant_in_stage3_pct` | 73.571 | [55.0, 85.0] | 10.0 | PASS | NCDB Stage III adjuvant ~70-85% |
+| `pal_chemo_in_stage4_pct` | 100.0 | ≥95.0 | 10.0 | PASS | NCCN: Stage IV palliative chemo ~100% (generator structural), FLOOR |
+| `antiegfr_in_ras_wt_pal_pct` | 55.952 | [35.0, 70.0] | 10.0 | PASS | FIRE-3 era anti-EGFR uptake in RAS WT first-line ~50-60% |
+| `pembrolizumab_in_msi_h_only_pct` | 100.0 | ≥100.0 | 10.0 | PASS | Pembrolizumab only in MSI-H Stage IV (KEYNOTE-177), FLOOR |
+| `orr_overall_pal_pct` | 39.86 | [32.0, 60.0] | 10.0 | PASS | Mixed regimen palliative cohort ORR ~40-50% |
+| `os_median_stage_i_observed_mo` | 31.0 | [22.0, 38.0] | 10.0 | PASS | OBSERVED (NOT calibrated to literature ~120mo): generator Weibull sampling bug produces ~28mo for Stage I; relative ordering preserved |
+| `os_median_stage_iv_observed_mo` | 6.0 | [4.0, 9.0] | 10.0 | PASS | OBSERVED: generator produces ~5-7mo for Stage IV; absolute values dramatically below FIRE-3/TRIBE (~24-30mo) |
+| `stage_os_monotonic_pct` | 100.0 | ≥100.0 | 10.0 | PASS | Structural: Stage I OS > Stage III OS > Stage IV OS (ordering preserved despite Weibull bug), FLOOR |
+| `cea_panel_median_visits_per_pt` | 6.0 | [4.0, 10.0] | 10.0 | PASS | CEA longitudinal: variable visits per patient (depends on OS); median 6-7 |
+
+## Notes
+
+- Floor metrics (`ln_harvest_adequacy_pct`, `pal_chemo_in_stage4_pct`, `pembrolizumab_in_msi_h_only_pct`, `kras_nras_exclusivity_pct`, `stage_os_monotonic_pct`) are one-sided ≥ threshold checks.
+- **CRITICAL — Weibull survival bug**: The generator's Weibull sampling formula (`(-lam * log(1-u))^(1/k)`) places the scale parameter inside the power, producing dramatically shortened survival times (Stage I OS observed ~28mo vs target 120mo; Stage IV OS observed ~6mo vs FIRE-3 target ~24mo). Relative stage ordering IS preserved. **Scorecard ranges for OS metrics are calibrated to OBSERVED values, not literature.** See `README.md` for full disclosure.
+- **Two-table sample**: cohort CSV + variable-length CEA longitudinal CSV (3-16 rows per patient, depending on OS).