--- license: cc-by-nc-4.0 language: - en tags: - synthetic-data - healthcare - oncology - lung-cancer - nsclc - sclc - egfr - alk - immunotherapy - tcga-luad - tcga-lusc - keynote - flaura - xpertsystems pretty_name: "HC-ONC-002 — Lung Cancer Synthetic Cohort (sample)" size_categories: - n<1K task_categories: - tabular-classification - tabular-regression - time-series-forecasting --- # HC-ONC-002 — Lung Cancer Synthetic Cohort **Sample dataset (500 patients × 116 columns) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 2** A fully synthetic, multimodal **lung cancer** cohort spanning the complete clinical pathway: smoking-stratified histology (NSCLC adeno/squamous/large cell + SCLC limited/extensive), AJCC 8th Edition T/N/M staging with site- specific metastases, comprehensive molecular biomarkers (EGFR with variant subtypes, ALK/ROS1 fusions, KRAS with G12C breakout, BRAF V600E, MET ex14, RET, NTRK, HER2, STK11, KEAP1, TP53), PD-L1 TPS+CPS scoring, TMB, MSI, treatment protocols across the IO/TKI era (surgery+adjuvant, SBRT, CCRT+ durvalumab, targeted TKIs, chemo-IO combinations), RECIST treatment response with pseudoprogression/hyperprogression flags, multimodal imaging (PET SUV/ MTV, ctDNA detection+VAF), IHC markers (TTF-1, p40, synaptophysin), adverse events including irAE phenotyping, and survival outcomes (PFS/OS with Weibull-derived event times). 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-002 | | **Vertical** | Healthcare → Oncology (SKU 2) | | **Sample size** | 500 patients × 116 columns | | **Modules** | 9 (Demographics, Histology+Staging, Molecular, Treatment, Response+Survival, Imaging+Pathology, Comorbidities, Adverse Events, Identifiers) | | **Standards** | AJCC 8th Edition, NCCN NSCLC/SCLC 2024, RECIST 1.1, CTCAE v5 | | **Format** | CSV | | **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 Lung cancer data lives across SEER (population incidence/survival, no molecular), TCGA LUAD/LUSC (deep genomics but n<1,000), clinical trial datasets (FLAURA/ALEX/KEYNOTE/CheckMate — tightly restricted), and real-world commercial datasets (Flatiron, COTA — expensive). This synthetic cohort gives you the **full lung cancer molecular+treatment+ outcomes phenome in one tidy table** with realistic dependencies: - ✅ **Smoking ↔ histology coupling** — never-smokers are ~60-70% adeno, current smokers more diverse - ✅ **Adeno ↔ EGFR/ALK coupling** — EGFR mutations 14-26% in adeno vs <2% in squamous; ALK 3-7% in adeno vs <1% elsewhere - ✅ **EGFR/ALK/ROS1 mutual exclusivity** (0 co-occurrences enforced) - ✅ **Stage IV EGFR+ NSCLC → 100% TKI** (NCCN Class I structural identity) - ✅ **SCLC ↔ TP53 coupling** — TP53 mutation ~85-94% in SCLC (matches George 2015) - ✅ **PD-L1 distribution with realistic spikes** at 0%, 1-49%, ≥50%, 100% - ✅ **OS ≥ PFS** always (0 violations across cohort) - ✅ **Treatment-specific survival calibration** — FLAURA EGFR osi PFS ~19 mo, ALEX alectinib PFS ~35 mo, KEYNOTE-024 pembro PFS ~14 mo - ✅ **irAE only in IO-treated patients** (0 violations) - ✅ **IHC marker fidelity** — TTF-1+ only in adeno (75%), p40+ only in squamous (90%), synaptophysin+ only in SCLC (85%) Coverage spans: - **NSCLC + SCLC combined** with smoking-stratified histology assignment - **AJCC 8th Edition staging** (IA/IB/IIA/IIB/IIIA/IIIB/IIIC/IVA/IVB) with T1a-T4 sub-staging, N0-N3 nodal staging, M0/M1a/M1b/M1c - **Site-specific metastasis flags** — brain, bone, liver, adrenal - **Comprehensive molecular profile** — EGFR (Exon19del/L858R/T790M/Exon20ins/ Other), ALK fusions (EML4/KIF5B/Other), ROS1 fusions, KRAS (G12C/G12V/G12D/ G13C/G12A), BRAF (V600E/non-V600E), MET ex14, RET, NTRK, HER2, STK11, KEAP1, TP53 - **Immunooncology biomarkers** — PD-L1 TPS + CPS with categorization, TMB high flag, MSI status - **Treatment regimens** — surgery types (lobectomy/segmentectomy/wedge/ VATS/robotic), SBRT, CCRT, IMRT, chemo (cisplatin-pemetrexed, carbo- paclitaxel, etoposide-platinum), IO (pembrolizumab, atezolizumab, durvalumab, nivolumab+ipilimumab), TKIs (osimertinib, alectinib, brigatinib, lorlatinib, entrectinib, sotorasib, adagrasib, dabrafenib- trametinib, tepotinib, capmatinib, selpercatinib), bevacizumab, adjuvant osimertinib (ADAURA-style) - **RECIST treatment response** — CR/PR/SD/PD with ORR/DCR, time-to-response, duration-of-response, CT response % change - **Pseudoprogression + hyperprogression** flags in IO-treated patients - **Liquid biopsy** — ctDNA detection, VAF%, clearance flag - **Multimodal imaging** — PET SUV-max, MTV - **IHC panel** — TTF-1, p40, synaptophysin/CD56 - **Survival outcomes** — PFS/OS with Weibull-derived event times, treatment-specific lambda calibration (FLAURA, ALEX, KEYNOTE-024, PACIFIC, IMpower133) - **Adverse events** — irAE type (pneumonitis, colitis, hepatitis, endocrinopathy, dermatitis) with grade, chemo AEs (nausea, neuropathy, cytopenias), G-CSF use, hospitalization --- ## Calibration anchors (industry-grade) This cohort is calibrated against named registries, guidelines, and trials — not invented distributions. Selection from the 31-metric scorecard: | Metric | Sample value (seed 42) | Target range | Source | |---|---:|---|---| | Mean age | 66.9 yr | 62–72 | SEER lung cancer | | Female % | 48.2% | 40–56 | SEER ~47% | | Never smoker % | 15.0% | 10–25 | SEER ~15-20% | | Current smoker % | 37.2% | 30–50 | SEER | | Adenocarcinoma % | 40.0% | 32–48 | SEER ~40-45% | | Squamous % | 25.2% | 20–33 | SEER ~25-30% | | SCLC % | 29.6% | 20–38 | Cohort over-enriched vs SEER 13% (disclosed) | | Adeno in never-smokers | 57.3% | 50–90 | SEER ~70-85% | | Stage IV in NSCLC | 44.6% | 35–55 | SEER ~40-50% | | EGFR in adeno | 19.0% | 10–30 | TCGA LUAD ~15%; LCMC ~17% | | ALK in adeno | 4.5% | 2.5–8 | Literature ~5-7% | | KRAS in adeno | 27.0% | 14–32 | TCGA LUAD ~30% | | KRAS G12C in KRAS+ | 27.8% | 25–50 | CodeBreaK 100 | | PD-L1 zero % | 26.6% | 22–38 | KEYNOTE-024 ~30% | | PD-L1 ≥50% | 54.0% | 40–60 | Enriched cohort | | TTF-1+ in adeno | 73.5% | 60–85 | Bishop 2010 ASCP | | p40+ in squamous | 91.3% | ≥80% (floor) | Bishop 2012 ASCP | | TKI in Stage IV EGFR+ NSCLC | 100% | ≥90% (floor) | NCCN Class I | | Surgery in early NSCLC | 67.8% | 50–75 | NCDB | | CCRT in locally advanced | 57.1% | 40–80 | PACIFIC era | | OS median (overall) | 15.85 mo | 12–22 | Mixed cohort | | ORR (overall) | 46.6% | 35–55 | Mixed treatment cohort | | ECOG 0-1 % | 70.8% | 60–80 | NCCN-era trials | | irAE in IO-treated | 27.6% | 20–40 | CheckMate-227 | | Brain mets in Stage IV NSCLC | 29.9% | 22–42 | Sorensen 1988, Schouten 2002 | | Bone mets in Stage IV NSCLC | 36.3% | 28–48 | NSCLC autopsy series | | TP53 in SCLC | 85.1% | 75–100 | George 2015 | | ctDNA detection in advanced NSCLC | 81.6% | ≥70% (floor) | Guardant360 | Full 31-metric scorecard ships in `validation_report.json` and `validation_report.md`. --- ## Files in this sample ``` hconc002_sample/ ├── hconc002_sample.csv # 500 patients × 116 columns ├── 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 ``` --- ## Schema (116 columns across 9 modules) ### Module 1 — Identifiers & Dates (3 cols) `patient_id`, `site_id`, `diagnosis_date` ### Module 2 — Demographics (15 cols) `age_at_diagnosis`, `sex`, `race`, `insurance`, `smoking_status`, `pack_years`, `cigarettes_per_day`, `smoking_duration_years`, `years_since_quitting`, `low_dose_ct_screening_history`, `second_hand_smoke_exposure`, `occupational_exposure`, `radon_exposure_flag`, `family_history_lung_cancer`, `bmi` ### Module 3 — Histology & Staging (18 cols) `histology_primary` (Adenocarcinoma/Squamous_Cell/Large_Cell/SCLC_Limited/ SCLC_Extensive), `histology_subtype`, `clinical_stage` (IA/IB/IIA/IIB/IIIA/ IIIB/IIIC/IVA/IVB or **truncated SCLC "Limi"/"Exte"** — see Limitations #1), `t_stage`, `n_stage`, `m_stage`, `tumor_size_cm`, `tumor_location`, `tumor_laterality`, `pleural_invasion_flag`, `vascular_invasion_flag`, `lymphovascular_invasion_flag`, `satellite_nodule_flag`, `brain_metastasis_flag`, `bone_metastasis_flag`, `liver_metastasis_flag`, `adrenal_metastasis_flag`, `metastasis_sites` ### Module 4 — Molecular Biomarkers (18 cols) `egfr_mutation`, `alk_fusion`, `ros1_fusion`, `kras_mutation`, `braf_mutation`, `met_exon14_skip`, `ret_fusion`, `ntrk_fusion`, `her2_alteration`, `stk11_mutation`, `keap1_mutation`, `tp53_mutation`, `pd_l1_tumor_proportion_score`, `pd_l1_combined_positive_score`, `pd_l1_category`, `tmb_mutations_per_mb`, `tmb_high_flag`, `microsatellite_status` ### Module 5 — Treatment (14 cols) `treatment_regimen`, `targeted_therapy`, `immunotherapy_agent`, `chemotherapy_regimen`, `surgery_type`, `surgical_margin_status`, `radiation_type`, `radiation_dose_gy`, `treatment_cycles_completed`, `treatment_adherence_pct`, `dose_reduction_flag`, `bevacizumab_flag`, `adjuvant_chemotherapy_flag`, `adjuvant_osimertinib_flag` ### Module 6 — Response & Survival (15 cols) `progression_free_survival_months`, `pfs_event_flag`, `overall_survival_months`, `os_event_flag`, `time_to_treatment_failure_months`, `best_overall_response`, `objective_response_flag`, `disease_control_flag`, `time_to_response_months`, `duration_of_response_months`, `ct_response_pct_change`, `pseudoprogression_flag`, `hyperprogression_flag`, `next_line_therapy_flag`, `ldh_at_progression_u_l` ### Module 7 — Imaging & Pathology (9 cols) `pet_ct_suv_max`, `pet_ct_mtv_ml`, `ctdna_detection_flag`, `ctdna_vaf_pct`, `ctdna_clearance_flag`, `pathology_grade`, `ihc_ttf1`, `ihc_p40`, `ihc_synaptophysin_cd56` ### Module 8 — Comorbidities (16 cols) `ecog_performance_status`, `fev1_pct_predicted`, `dlco_pct_predicted`, `copd_flag`, `copd_gold_stage`, `cardiovascular_disease_flag`, `diabetes_flag`, `hypertension_flag`, `prior_malignancy_flag`, `charlson_comorbidity_index`, `albumin_g_dl`, `ldh_baseline_u_l`, `hemoglobin_g_dl`, `neutrophil_lymphocyte_ratio`, `platelet_lymphocyte_ratio`, `c_reactive_protein_mg_l` ### Module 9 — Adverse Events (8 cols) `irae_flag`, `irae_type`, `irae_grade`, `nausea_grade`, `peripheral_neuropathy_grade`, `cytopenias_grade`, `hospitalization_flag`, `g_csf_use_flag` --- ## Use cases 1. **Histology classification models** — train classifiers using smoking history, demographics, imaging features → adeno/squamous/SCLC subtype. 2. **EGFR/ALK/KRAS biomarker prediction** — clinical+demographic features → likelihood of actionable mutation; benchmark precision-medicine referral logic. 3. **Treatment selection modeling** — NCCN guideline-concordance scoring (TKI for driver mutations, IO for PD-L1≥50%, CCRT+durvalumab for locally advanced). 4. **Survival prediction** — Cox PH on PFS/OS with stage + molecular + treatment covariates; treatment-specific landmark analyses. 5. **RECIST response prediction** — multimodal features → ORR / pCR / hyperprogression risk. 6. **PD-L1 distribution analytics** — score distribution modeling for trial inclusion criteria. 7. **Liquid biopsy modeling** — ctDNA detection probability by stage + tumor burden; VAF dynamics. 8. **Immune-related adverse event prediction** — risk stratification by IO agent + clinical features. 9. **Real-world data benchmarking** — quasi-experimental analyses with treatment arm comparisons. 10. **Teaching & training** — oncology fellows, lung cancer multidisciplinary conferences, ML-for-healthcare courses. --- ## Loading examples ### pandas ```python import pandas as pd df = pd.read_csv("hconc002_sample.csv") print(df.shape) # (500, 116) print(df["histology_primary"].value_counts()) print(df.groupby("clinical_stage")["overall_survival_months"].median()) ``` ### Hugging Face `datasets` ```python from datasets import load_dataset ds = load_dataset("xpertsystems/hconc002-sample") df = ds["train"].to_pandas() ``` ### Driver mutation classification ```python from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import train_test_split # EGFR vs no-EGFR in adenocarcinoma adeno = df[df["histology_primary"] == "Adenocarcinoma"].copy() adeno["egfr_pos"] = (adeno["egfr_mutation"] != "None").astype(int) features = ["age_at_diagnosis", "sex", "smoking_status", "pack_years", "race", "family_history_lung_cancer", "tumor_size_cm", "tp53_mutation", "pd_l1_tumor_proportion_score"] X = pd.get_dummies(adeno[features]) y = adeno["egfr_pos"] X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.25, random_state=42) clf = GradientBoostingClassifier(random_state=42).fit(X_tr, y_tr) print(f"AUC features: {sorted(zip(X.columns, clf.feature_importances_), key=lambda x: -x[1])[:5]}") ``` ### Survival analysis by treatment regimen ```python from lifelines import KaplanMeierFitter import matplotlib.pyplot as plt stage_iv = df[df["clinical_stage"].isin(["IVA","IVB"])].copy() kmf = KaplanMeierFitter() for reg, sub in stage_iv.groupby("treatment_regimen"): if len(sub) < 5: continue kmf.fit(sub["overall_survival_months"], event_observed=sub["os_event_flag"], label=reg) kmf.plot_survival_function() plt.title("OS by Treatment Regimen — Stage IV NSCLC") plt.show() ``` ### NCCN guideline-concordance audit ```python # NCCN: TKI for EGFR+ Stage IV NSCLC nsclc_iv_egfr = df[(~df["histology_primary"].isin(["SCLC_Limited","SCLC_Extensive"])) & (df["clinical_stage"].isin(["IVA","IVB"])) & (df["egfr_mutation"] != "None")] tki_rate = (nsclc_iv_egfr["treatment_regimen"] == "Targeted_TKI").mean() print(f"TKI in Stage IV EGFR+ NSCLC: {tki_rate:.1%} (NCCN target ≥90%)") # NCCN: CCRT+durvalumab for locally advanced unresectable NSCLC locally_adv = df[df["clinical_stage"].isin(["IIIA","IIIB","IIIC"]) & (~df["histology_primary"].isin(["SCLC_Limited","SCLC_Extensive"]))] ccrt_rate = (locally_adv["treatment_regimen"] == "CCRT").mean() print(f"CCRT in locally advanced NSCLC: {ccrt_rate:.1%}") ``` --- ## 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. **SCLC stage labels are truncated to 4 characters.** Due to a fixed-length string dtype, when the generator assigns `clinical_stage = "Limited"` or `"Extensive"` for SCLC patients (after initially populating with NSCLC labels like `"IIIC"`), the strings are truncated to `"Limi"` and `"Exte"`. **Downstream impact:** the `m_stage` calculation uses `np.isin(stage, ["IVA","IVB","Extensive"])` — `"Exte"` doesn't match `"Extensive"`, so SCLC_Extensive patients incorrectly get assigned `m_stage = "M0"` and no metastasis sites, despite Extensive SCLC being metastatic by definition. The ctDNA detection rate is also lower in SCLC_Extensive patients (gets ~35% non-advanced rate instead of ~82% advanced rate). **The wrapper's metrics use NSCLC-only subsets for metastasis and ctDNA computations to avoid contaminating analyses.** Full product fixes the dtype. 2. **SCLC is over-represented at ~30% of cohort vs SEER ~13%.** Generator's histology probabilities assign SCLC 27-32% across smoking strata. This is a **design choice for a cohort enriched in advanced disease**, appropriate for SCLC-focused modeling but **not** appropriate for population-level epidemiology. For SEER-calibrated SCLC fraction (~13%), sub-sample or re-weight the SCLC subset. 3. **Module 3 (histology assignment) uses `np.random.choice` (legacy global state)** at lines 148, 152, 156 instead of the modular `rng`. The wrapper mitigates by calling `np.random.seed(seed)` before generation, but this means **per-row histology values are deterministic only for the first call in a process**. Distributions are stable across all canonical seeds. Full product migrates these draws to the modular RNG. 4. **CCI calculation has a typo: hypertension contribution multiplied by 0.** Line 680 reads `htn * 0` instead of `htn`, effectively excluding hypertension from the Charlson Comorbidity Index sum. Observed CCI mean is ~1.5 (would be ~2.1 with HTN included). **The `hypertension_flag` column is still correctly populated** — only the CCI summary metric is affected. 5. **EGFR/ALK/ROS1 are forced mutually exclusive** (generator design). This is biologically accurate (true co-occurrence is exceedingly rare) but means compound-driver patients are not represented. 6. **Stage IV EGFR+ NSCLC → 100% TKI assignment** is enforced (no chemo-only stage IV EGFR+ patients). NCCN-concordant but real-world ~85-92% receive TKI first-line; the remaining 8-15% receive chemo for reasons like ECOG ≥3, T790M-only mutation, or patient preference — not modeled here. 7. **PD-L1 TPS uses a spike-mixture distribution** — spike at 0% (28%), continuous 1-49% (22%), continuous 50-99% (20%), spike at 100% (30%). This produces the characteristic bimodal distribution seen in IO trials but **slightly over-represents TPS≥50% (~50%) compared to KEYNOTE-024 screening population (~30%).** Cohort is enriched in IO-eligible patients. 8. **Treatment-specific survival lambdas are point-calibrated to single trials** (FLAURA, ALEX, KEYNOTE-024, PACIFIC, IMpower133). Real-world survival distributions show wider variance and include trial-ineligible patients with worse outcomes. **Cohort survival skews trial-ish.** 9. **Adjuvant osimertinib (ADAURA) flag is independent of EGFR mutation status** — the generator assigns `adjuvant_osimertinib_flag = 1` with probability 0.80 for early-stage EGFR+ post-surgery patients, but does not block assignment for EGFR-negative patients. **Filter on `egfr_mutation != "None"` before using this flag for ADAURA-style analyses.** 10. **Race/ethnicity is not coupled to molecular biomarkers.** Real lung cancer epidemiology shows substantial racial differences (EGFR in Asian never-smokers ~50% vs White ~15%; KRAS in White smokers higher than Asian). The synthetic cohort is intentionally race-blinded in molecular assignment to avoid encoding real-world disparity bias into trainees' models. If you're studying disparities, use real LCMC or TCGA-LUAD data. 11. **scipy.stats is NOT imported** (clean — no dead imports in this generator), unlike HCONC001. 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 | |---|---|---| | Patients | 500 | 15,000+ (configurable) | | SCLC stage truncation | "Limi"/"Exte" bug (disclosed) | Fixed to "Limited"/"Extensive" | | SCLC fraction | ~30% (over-enriched) | Configurable (SEER 13% → enriched 30%) | | Histology RNG | Legacy `np.random` (disclosed) | Migrated to modular `rng` | | CCI calculation | HTN excluded (bug) | Full Charlson | | Adjuvant osimertinib gating | EGFR-independent | Gated on EGFR+ | | Race-biomarker coupling | None (race-blinded) | Configurable LCMC-calibrated | | Validation report | Yes (31 metrics) | Yes + custom scorecard | | Format | CSV | CSV, Parquet, JSON | | License | CC-BY-NC-4.0 (non-commercial) | Commercial use license | | Schema mapping | — | SEER / NCDB / TCGA-LUAD-LUSC / Flatiron | | Longitudinal extension | No | Optional treatment-line trajectory | | Support | Community | Email / SLA | --- ## Citation ```bibtex @dataset{xpertsystems_hconc002_2026, title = {HC-ONC-002: Lung Cancer Synthetic Cohort}, author = {{XpertSystems.ai}}, year = {2026}, version= {1.0.0}, url = {https://huggingface.co/datasets/xpertsystems/hconc002-sample}, license= {CC-BY-NC-4.0 (sample); Commercial (full product)}, note = {Calibrated against SEER lung cancer 2017-2021, TCGA LUAD/LUSC, NCCN NSCLC/SCLC Guidelines 2024, AJCC 8th Edition, FLAURA (Soria 2018), ALEX (Peters 2017), CheckMate-816/9LA (Forde 2022, Paz-Ares 2021), KEYNOTE-024/189/407 (Reck 2016, Gandhi 2018, Paz-Ares 2018), IMpower133 (Horn 2018), PACIFIC (Antonia 2017), CodeBreaK 100 (Skoulidis 2021), Guardant360.} } ``` --- ## Contact - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Vertical:** Healthcare / Oncology - **SKU catalog:** SKU 2 of the Oncology vertical (12 SKUs total across Cardiology + Oncology); ~77 SKUs across 8 verticals XpertSystems.ai — synthetic data, calibrated to real-world registries.