| --- |
| 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. |
| |
