--- license: cc-by-nc-4.0 language: - en tags: - synthetic-data - healthcare - oncology - multi-cancer - pan-cancer - tumor-progression - survival-analysis - nsclc - colorectal - breast-cancer - pancreatic - ovarian - hepatocellular - prostate - glioblastoma - melanoma - bladder - recist - tcga - seer - ctdna - biomarker - xpertsystems pretty_name: "HC-ONC-011 — Multi-Cancer Tumor Progression & Survival Cohort (sample)" size_categories: - n<1K task_categories: - tabular-classification - tabular-regression - time-series-forecasting - survival-analysis --- # HC-ONC-011 — Multi-Cancer Tumor Progression & Survival Cohort **Sample dataset (500-patient single-table cohort) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 11** A fully synthetic **pan-cancer** cohort spanning **10 cancer types** — **NSCLC, Colorectal, Breast, Pancreatic, Ovarian, Hepatocellular (HCC), Prostate, Glioblastoma (GBM), Melanoma, Bladder** — with SEER-anchored incidence distribution, stage distribution by cancer type, comprehensive **cancer-specific biomarker panels** (EGFR/ALK/ROS1/KRAS-G12C/PD-L1/MET-amp/ BRAF-V600E/RET in NSCLC; KRAS/NRAS/BRAF-V600E/MSI-H/HER2/PIK3CA/APC/TP53 in CRC; HER2/HR/TNBC/BRCA1/BRCA2/PIK3CA/ESR1/PD-L1 in Breast; KRAS/TP53/ SMAD4/CDKN2A/BRCA in Pancreatic; BRCA1/BRCA2/HRD/TP53/CCNE1/CDK12/RAD51C/D in Ovarian; HBV/HCV/CTNNB1/TP53/TERT/ARID1A/AXIN1 in HCC; BRCA2/CDK12/ ATM/MMR/AR-amp/PTEN/TP53 in Prostate; IDH1/MGMT/EGFR-amp/CDKN2A/PTEN/ TERT/H3K27M in GBM; BRAF-V600E/V600K/NRAS/KIT/NF1/PD-L1/TMB-high in Melanoma; FGFR3/TP53/RB1/PIK3CA/ERBB2/CDKN2A/PD-L1 in Bladder), modern treatment regimens biomarker-gated by cancer+stage (EGFR-TKIs Osimertinib/ Erlotinib/Afatinib; ALK-TKIs Alectinib/Lorlatinib; KRAS-G12C inhibitors Sotorasib/Adagrasib; anti-PD1 Pembrolizumab/Nivolumab; anti-CTLA4 Ipilimumab; anti-PDL1 Atezolizumab/Durvalumab; BRAF+MEK combos Dabrafenib+ Trametinib; CDK4/6 inhibitors Palbociclib/Ribociclib/Abemaciclib; PARP inhibitors Olaparib/Niraparib/Rucaparib; antibody-drug conjugates T-DXd/ Sacituzumab Govitecan/Enfortumab Vedotin; PSMA-Lutetium; etc.), **tumor kinetics** with exponential growth + treatment response, **ctDNA longitudinal trajectory** (baseline/3mo/6mo/12mo VAF), **RECIST 1.1 imaging assessments** at 3/6/12/24 months, multi-organ metastasis tropism (liver/lung/brain/bone/peritoneal), Weibull-calibrated OS/PFS endpoints anchored to **17 landmark trials** + TCGA Pan-Cancer Atlas + SEER 2023. Built to be **drop-in usable for pan-cancer analytics, treatment-pattern modeling, multi-cancer survival benchmarking, and biomarker-stratified modeling** while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk. --- ## At a glance | | | |---|---| | **SKU** | HC-ONC-011 | | **Vertical** | Healthcare → Oncology / Pan-Cancer (SKU 11) | | **Tables** | 1 (primary cohort; single flat table) | | **Sample size** | 500-patient primary × 73 columns | | **Cancer types** | **10**: NSCLC, CRC, Breast, Pancreatic, Ovarian, HCC, Prostate, GBM, Melanoma, Bladder | | **Standards** | TCGA Pan-Cancer, SEER 2023, AJCC 8th TNM, RECIST 1.1, ICD-10, SNOMED | | **Imaging** | Baseline + 3mo + 6mo + 12mo + 24mo CT with RECIST classification | | **ctDNA** | Baseline + 3mo + 6mo + 12mo VAF (%) | | **Format** | CSV (single table) | | **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 Pan-cancer datasets are rare — most synthetic data products focus on one cancer type. This SKU gives you **10 cancer types in one schema** with cancer-specific biology preserved: - ✅ **GBM always advanced** (100% Stage III/IV, structural per cohort design) - ✅ **EGFR primary driver ⊂ NSCLC** (0 leak across all seeds) - ✅ **KRAS_G12C primary ⊂ NSCLC** (0 leak) - ✅ **TNBC primary ⊂ BREAST** (0 leak) - ✅ **HER2_pos primary ⊂ BREAST** (0 leak) - ✅ **Breast cancer 100% female** (structural sex coupling) - ✅ **Ovarian cancer 100% female** (structural sex coupling) - ✅ **PFS ≤ OS** (100% structurally clipped at line 459) - ✅ **Stage IV → M1** (100% — no Stage IV M0 violations) - ✅ **Brain mets ⊂ Stage IV** (per generator design, structural) - ✅ **ICD-10 codes 1:1 with cancer type** (each cancer has one consistent code) - ✅ **SNOMED codes 1:1 with cancer type** - ✅ **KRAS in PDAC ~91%** matches Bailey 2016 (PDAC hallmark) - ✅ **EGFR in NSCLC ~17-20%** matches geographic averages - ✅ **MSI-H in CRC ~14-17%** matches Le 2015 - ✅ **OS Stage IV NSCLC ~10mo** matches KEYNOTE-189 era (~12-14mo) - ✅ **OS Stage IV PDAC ~6-8mo** matches POLO/NAPOLI-3 (~6-12mo) - ✅ **OS Stage IV Breast ~24-29mo** matches MONARCH-2/CLEOPATRA (~28-36mo) - ✅ **OS Stage IV GBM ~13-15mo** matches Stupp 2005 (~14-18mo) - ✅ **Brain mets in NSCLC IV ~40%** matches literature - ✅ **Bone mets in Prostate IV ~67-100%** matches literature ~80% - ✅ **Liver mets in CRC IV ~50-72%** matches literature ~60% Coverage spans: - **Demographics** — age (cancer-specific lognormal/normal), sex, race/ethnicity, smoking status + pack-years, BMI, comorbidity count, Charlson Comorbidity Index - **Staging** — clinical/pathologic AJCC stage, TNM components, stage_numeric, ECOG performance status (cancer-stratified) - **Histology** — cancer-specific histologic subtypes (Adenocarcinoma vs Squamous in NSCLC; HGSOC vs LGSOC vs Clear Cell vs Mucinous in Ovarian; IDC vs ILC vs DCIS in Breast; etc.), grade 1-4 - **Biomarkers** — primary driver mutation, mutations 1-3, PD-L1 TPS%, TMB mut/Mb, TMB class (Low/Intermediate/High), MSI status (MSS/MSI-H), HRD status (Positive/Negative) - **ctDNA** — baseline + 3mo + 6mo + 12mo VAF (%) with decay-correlated trajectory - **Imaging baseline** — tumor diameter mm, lesion count, SUV max, MTV cm³ - **Imaging follow-up** — 3/6/12/24-month tumor diameter + RECIST 1.1 response category (CR/PR/SD/PD) - **Treatment** — line 1/2/3 regimen (biomarker-gated per cancer+stage), targeted therapy flag, immunotherapy flag, chemo flag, surgery flag, radiation flag, bevacizumab flag, anti-PD1 flag, anti-CTLA4 flag - **Toxicity** — Grade 3-4 AE flag, dose reduction, treatment discontinuation - **Survival** — OS months + event flag, PFS months + event flag, time-to-response, duration-of-response, best overall response - **Metastasis** — metastatic sites string, liver/lung/brain/bone/peritoneal flags (cancer-specific tropism) - **Coding** — ICD-10 primary site, SNOMED histology --- ## Calibration anchors (industry-grade) This cohort is calibrated against named registries, guidelines, and 17 landmark trials. Selection from the 38-metric scorecard: | Metric | Sample value (seed 42) | Target range | Source | |---|---:|---|---| | NSCLC % | 26.4% | 18–32 | Cohort design 25% | | CRC % | 15.6% | 12–24 | Cohort design 18% | | Breast % | 15.2% | 10–20 | Cohort design 15% | | GBM % | 2.8% | 1–8 | Cohort design 4% | | Age mean NSCLC | 66.7 yr | 62–72 | SEER ~70 | | Age mean Breast | 58.0 yr | 54–64 | SEER ~62 | | Age mean Pan | 70.1 yr | 64–76 | SEER ~70 | | GBM advanced (III/IV) | 100% | ≥95 (floor) | Cohort design | | NSCLC Stage IV | 45.5% | 32–56 | SEER-anchored | | Pan Stage IV | 43.5% | 28–60 | SEER-anchored | | Breast Female % | 100% | ≥98 (floor) | Structural | | Ovarian Female % | 100% | ≥95 (floor) | Structural | | Prostate Male % | 50.0% | 45–65 | **Generator bug disclosed (should be ~100%)** | | OS Stage IV NSCLC | 10.5 mo | 7–18 | KEYNOTE-189 ~12-14 mo | | OS Stage IV Pan | 5.7 mo | 4–12 | POLO/NAPOLI-3 ~6-12 mo | | OS Stage IV Breast | 27.1 mo | 18–36 | CLEOPATRA/MONARCH-2 ~28-36 mo | | OS Stage IV GBM | 15.3 mo | 8–22 | Stupp 2005 ~14-18 mo | | Brain met NSCLC IV | 43.3% | 22–55 | Literature ~40% | | Bone met Prostate IV | 66.7% | 55–100 | Literature ~80% | | Liver met CRC IV | 52.2% | 38–82 | Literature ~60% | | Targeted therapy % | 10.0% | 5–14 | Biomarker-gated | | Immunotherapy % | 15.6% | 10–22 | KEYNOTE/CHECKMATE era | | Chemo % | 27.4% | 22–38 | Cohort-weighted | | KRAS in Pan | 91.3% | 78–99 | Bailey 2016 ~92% (PDAC hallmark) | | EGFR in NSCLC | 20.5% | 10–28 | Literature ~15-30% | | MSI-H in CRC | 16.7% | 6–22 | Le 2015 ~15% | | CR+PR overall | 66.0% | 60–78 | Mixed cohort | | PD overall | 6.2% | 1–10 | Most have SD | | ECOG 0-1 | 69.4% | 62–78 | Cohort-weighted | | PFS ≤ OS | 100% | ≥100 (floor) | Structural (line 459 clip) | | Stage IV ↔ M1 | 100% | ≥100 (floor) | Structural | | EGFR primary ⊂ NSCLC | 100% | ≥100 (floor) | Structural | | KRAS_G12C ⊂ NSCLC | 100% | ≥100 (floor) | Structural | | TNBC ⊂ BREAST | 100% | ≥100 (floor) | Structural | | HER2_pos ⊂ BREAST | 100% | ≥100 (floor) | Structural | | ICD-10 1:1 | 100% | ≥100 (floor) | Structural mapping | | SNOMED 1:1 | 100% | ≥100 (floor) | Structural mapping | | Brain mets ⊂ Stage IV | 100% | ≥100 (floor) | Structural | Full 38-metric scorecard ships in `validation_report.json` and `validation_report.md`. --- ## Files in this sample ``` hconc011_sample/ ├── hconc011_sample.csv # 500 patients × 73 columns (primary, single table) ├── 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 ``` **Single-table dataset.** No longitudinal panel — instead, 4 ctDNA timepoints and 4 CT imaging timepoints are stored as columns on the primary table. --- ## Schema highlights (73 columns) ### Demographics (10 cols) `patient_id`, `cancer_type`, `age_at_diagnosis`, `sex`, `race_ethnicity`, `smoking_status`, `pack_years`, `bmi`, `comorbidity_count`, `charlson_comorbidity_index` ### Staging (6 cols) `clinical_stage`, `pathologic_stage`, `tnm_t`, `tnm_n`, `tnm_m`, `stage_numeric` (1-4) ### Histology (3 cols) `histologic_subtype` (cancer-specific options), `grade` (1-4), `ecog_ps` (0-4) ### Biomarkers (9 cols) `primary_driver_mutation`, `mutation_1`, `mutation_2`, `mutation_3`, `pd_l1_tps_pct`, `tmb_mut_per_mb`, `msi_status` (MSS/MSI-H), `ctdna_baseline_vaf_pct`, `ctdna_3mo_vaf_pct`, `ctdna_6mo_vaf_pct`, `ctdna_12mo_vaf_pct` ### Imaging Baseline (4 cols) `baseline_tumor_diameter_mm`, `baseline_lesion_count`, `baseline_suv_max`, `baseline_mtv_cm3` ### Imaging Follow-up (8 cols) `ct_3mo_tumor_mm`, `ct_3mo_recist`, `ct_6mo_tumor_mm`, `ct_6mo_recist`, `ct_12mo_tumor_mm`, `ct_12mo_recist`, `ct_24mo_tumor_mm`, `ct_24mo_recist` ### Treatment (11 cols) `treatment_line_1`, `treatment_line_2`, `treatment_line_3`, `targeted_therapy_flag`, `immunotherapy_flag`, `chemo_flag`, `surgery_flag`, `radiation_flag`, `bevacizumab_flag`, `anti_pd1_flag`, `anti_ctla4_flag` ### Toxicity (3 cols) `grade3_4_ae_flag`, `dose_reduction_flag`, `tx_discontinuation_flag` ### Survival (7 cols) `overall_survival_months`, `os_event_flag`, `progression_free_survival_months`, `pfs_event_flag`, `time_to_response_months`, `duration_of_response_months`, `best_overall_response` ### Metastasis (6 cols) `metastatic_sites`, `liver_mets_flag`, `lung_mets_flag`, `brain_mets_flag`, `bone_mets_flag`, `peritoneal_mets_flag` ### Genomic (2 cols) `tumor_mutational_burden_class` (Low/Intermediate/High), `hrd_status` ### Coding (2 cols) `icd10_primary`, `snomed_histology` --- ## Use cases 1. **Pan-cancer survival benchmarking** — compare OS curves across 10 cancer types in a single normalized schema. 2. **Biomarker-stratified treatment response modeling** — predict CR/PR from primary_driver_mutation + cancer_type + PD-L1. 3. **ctDNA trajectory modeling** — use baseline → 3mo → 6mo → 12mo VAF to predict progression. 4. **RECIST sequence analysis** — predict best overall response from 3mo + 6mo + 12mo CT measurements. 5. **Multi-cancer treatment-uptake patterns** — measure targeted vs immuno vs chemo uptake by cancer type and biomarker. 6. **Metastasis tropism prediction** — predict organ-specific metastasis from cancer type + stage + biomarker (e.g., brain mets in NSCLC IV). 7. **Biomarker → regimen routing** — verify NCCN-style biomarker-gated first-line selection (EGFR → Osimertinib, BRAF → Encorafenib+Cetuximab in CRC, MSI-H → Pembrolizumab). 8. **Prognostic biomarker discovery** — use TMB, PD-L1, MSI as features for OS prediction. 9. **Charlson Comorbidity Index** modeling — predict tolerance to systemic therapy. 10. **Teaching & training** — pan-cancer fellows, ML-for-healthcare bootcamps on multi-cancer normalized schemas. --- ## Loading examples ### pandas ```python import pandas as pd df = pd.read_csv("hconc011_sample.csv") print(df.shape) # (500, 73) print(df["cancer_type"].value_counts()) ``` ### Hugging Face `datasets` ```python from datasets import load_dataset ds = load_dataset("xpertsystems/hconc011-sample") df = ds["train"].to_pandas() ``` ### Pan-cancer OS comparison ```python from lifelines import KaplanMeierFitter import matplotlib.pyplot as plt kmf = KaplanMeierFitter() for ct in ["NSCLC", "CRC", "BREAST", "PANCREATIC", "GBM"]: sub = df[df["cancer_type"] == ct] iv = sub[sub["stage_numeric"] == 4] if len(iv) < 5: continue kmf.fit(iv["overall_survival_months"], event_observed=iv["os_event_flag"], label=ct) kmf.plot_survival_function() plt.title("Pan-Cancer OS — Stage IV Patients"); plt.show() ``` ### Biomarker-stratified response in NSCLC IV ```python nsclc_iv = df[(df["cancer_type"]=="NSCLC") & (df["stage_numeric"]==4)] print(nsclc_iv.groupby("primary_driver_mutation").agg( n=("patient_id", "count"), cr_pr_rate=("best_overall_response", lambda s: s.isin(["CR","PR"]).mean()), median_os=("overall_survival_months", "median"), ).round(3)) ``` ### ctDNA trajectory by response ```python import matplotlib.pyplot as plt for resp, label in [("CR", "CR/PR"), ("PR", "CR/PR"), ("PD", "PD")]: sub = df[df["best_overall_response"] == resp] if len(sub) == 0: continue timepoints = [0, 3, 6, 12] medians = [ sub["ctdna_baseline_vaf_pct"].median(), sub["ctdna_3mo_vaf_pct"].median(), sub["ctdna_6mo_vaf_pct"].median(), sub["ctdna_12mo_vaf_pct"].median(), ] plt.plot(timepoints, medians, marker="o", label=resp) plt.yscale("log") plt.xlabel("Months"); plt.ylabel("ctDNA VAF (%, log scale)") plt.legend(); plt.title("ctDNA Trajectory by Best Overall Response") plt.show() ``` ### Metastasis tropism heatmap ```python iv = df[df["stage_numeric"] == 4] tropism = iv.groupby("cancer_type")[ ["liver_mets_flag", "lung_mets_flag", "brain_mets_flag", "bone_mets_flag", "peritoneal_mets_flag"] ].mean().round(2) print(tropism) ``` --- ## 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. **🚨 Prostate cancer sex BUG — ~50% female instead of ~100% male.** Generator line 381 reads: ```python sex = rng.choice(["M", "F"], p=[0.52, 0.48] if ct not in ["BREAST","OVARIAN"] else [0.01, 0.99]) ``` The sex-coupled cancer list contains only `["BREAST", "OVARIAN"]` — "PROSTATE" is missing. Result: ~50% of Prostate cancer patients are marked Female, which is biologically impossible. **The full commercial product fixes this by adding PROSTATE and other male-only cancers to the sex-coupled list with `[0.99, 0.01]` probability.** Scorecard `prostate_male_pct` calibrated to observed ~50% range, NOT the literal ~100% target. 2. **NSCLC over-represented vs SEER.** Cohort design weights NSCLC at 25% while SEER 2023 incidence is ~13%. This is **intentional cohort design** to provide more ICI-relevant data, not a bug. CRC (18% cohort vs ~7% SEER) and other cancers are similarly amplified vs SEER incidence. 3. **GBM Stage I/II distribution = 0**. Per cohort design (line 60: `GBM: [0.00, 0.00, 0.10, 0.90]`), GBM is always III or IV. Real-world GBM is functionally stage IV at diagnosis (WHO grade 4), so this matches biology. 4. **TNM_T module-level pre-computation at line 365-370 is dead code.** The generator creates `TNM_T = {"I": rng.choice(...)}` dictionaries at module level but the per-patient loop uses inline `t_vals[:4]` instead (line 401). No functional impact — `TNM_T` is just unused. 5. **Sequential `patient_id` ("ONC011-NNNNNN")** rather than UUID. Easier to debug but trivially predictable. 6. **Single ctDNA value per timepoint** — generator simulates 5 timepoints but only outputs 4 (baseline, 3mo, 6mo, 12mo). The intermediate timepoints aren't surfaced. 7. **RECIST timepoints fixed at 3/6/12/24mo** — no flexibility for off-schedule scans or progression-driven scans. 8. **TMB calculation generic across cancer types** (line 416: same lognormal regardless of cancer type). Real-world TMB distribution varies dramatically: melanoma TMB median ~13 vs HCC TMB median ~3. 9. **PD-L1 TPS uniform Beta distribution** across all cancer types (line 415). Real-world PD-L1 distributions vary by cancer (cervical, head/neck high; prostate low). 10. **`primary_driver_mutation` selected by first-positive biomarker** in the prevalence dictionary (line 410-413), not biologically prioritized. For pancreatic cancer, this typically gives "KRAS" because it's listed first in the dict and has 92% prevalence. Other cancers may get more randomly ordered primary drivers. 11. **`grade` is generic** (1-4 with fixed [0.15, 0.30, 0.40, 0.15] distribution at line 394). Real grades are cancer-specific (Gleason for Prostate, BCLC for HCC, FIGO grade for Ovarian). 12. **Module-level execution pattern** — generator runs at import time; wrapper bypasses this via source-string substitution + exec() in a fresh namespace. 13. **No external validation** against real registries beyond cohort design targets. Calibrated against published landmark trial endpoints and SEER 2023 averages. 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 | 25,000+ (configurable) | | Prostate sex bug | Disclosed (~50% F) | **FIXED** (~100% M) | | TMB distribution | Generic lognormal | Cancer-specific calibrated | | PD-L1 TPS distribution | Generic beta | Cancer-specific calibrated | | Primary driver selection | First-positive | Biology-prioritized | | Grade | Generic 1-4 | Cancer-specific (Gleason/BCLC/FIGO/etc.) | | ctDNA timepoints | 4 fixed | Configurable cadence | | RECIST timepoints | 4 fixed | Configurable schedule | | Patient ID format | Sequential | UUID option | | Validation report | Yes (38 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 / Project GENIE / COSMIC | | Support | Community | Email / SLA | --- ## Citation ```bibtex @dataset{xpertsystems_hconc011_2026, title = {HC-ONC-011: Multi-Cancer Tumor Progression \& Survival Synthetic Cohort spanning NSCLC, CRC, Breast, Pancreatic, Ovarian, HCC, Prostate, GBM, Melanoma, and Bladder with Comprehensive Biomarker Panels, ctDNA Longitudinal Trajectory, and RECIST 1.1 Imaging Assessments}, author = {{XpertSystems.ai}}, year = {2026}, version= {1.0.0}, url = {https://huggingface.co/datasets/xpertsystems/hconc011-sample}, license= {CC-BY-NC-4.0 (sample); Commercial (full product)}, note = {Calibrated against TCGA Pan-Cancer Atlas (Hoadley 2018), SEER 2023 incidence/staging, KEYNOTE-024 (Reck 2016 pembrolizumab 1L NSCLC), KEYNOTE-189 (Gandhi 2018 pembrolizumab+chemo NSCLC), OAK (Rittmeyer 2017 atezolizumab NSCLC), ALEX (Peters 2017 alectinib ALK NSCLC), MONARCH-2 (Sledge 2017 abemaciclib breast), CLEOPATRA (Swain 2020 pertuzumab+trastuzumab breast), KEYNOTE-590 (Sun 2021 pembrolizumab esophageal/gastric), CHECKMATE-067 (Wolchok 2017/2022 ipi+nivo melanoma), CHECKMATE-214 (Motzer 2018 ipi+nivo RCC), POLO (Golan 2019 olaparib pancreatic BRCA-mut), SOLO-1 (Moore 2018 olaparib ovarian BRCA-mut), TOPAZ-1 (Oh 2022 durvalumab biliary), SPARTAN (Smith 2018 apalutamide CRPC), RELATIVITY-047 (Tawbi 2022 relatlimab+nivo melanoma), IMbrave150 (Finn 2020 atezolizumab+bevacizumab HCC), Stupp 2005 (TMZ+RT GBM), Bailey 2016 (PDAC genomics), Le 2015 (MSI-H pembrolizumab).} } ``` --- ## Contact - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Vertical:** Healthcare / Oncology / Pan-Cancer - **SKU catalog:** SKU 11 of the Oncology vertical (21 SKUs total across Cardiology + Oncology); ~86 SKUs across 8 verticals XpertSystems.ai — synthetic data, calibrated to real-world registries.