Datasets:

xpertsystems
/

hconc005-sample

license: cc-by-nc-4.0
language:
  - en
tags:
  - synthetic-data
  - healthcare
  - oncology
  - pancreatic-cancer
  - pdac
  - kras
  - smad4
  - ca19-9
  - folfirinox
  - olaparib
  - prodige
  - mpact
  - polo
  - tcga-paad
  - longitudinal
  - multi-table
  - xpertsystems
pretty_name: HC-ONC-005 — Pancreatic Cancer (PDAC) Synthetic Cohort (sample)
size_categories:
  - 1K<n<10K
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
  - survival-analysis

HC-ONC-005 — Pancreatic Cancer (PDAC) Synthetic Cohort

Sample dataset (500-patient primary cohort + ~4,400-row CA 19-9 longitudinal panel + 100-row surgical subset + 500-row molecular panel + ~860-row treatment history) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 5

A fully synthetic pancreatic ductal adenocarcinoma (PDAC) cohort spanning the complete clinical pathway: AJCC 8th Edition staging with NCCN resectability classification (Resectable / Borderline_Resectable / Locally_Advanced / Metastatic), comprehensive molecular profiling (KRAS with 7 variants — G12D / G12V / G12R / G12C / G12A / G12S / Q61H — plus WT; TP53, SMAD4, CDKN2A, BRCA1/2 germline + BRCA2 somatic, PALB2, ATM, HRD score, MSI status, TMB, PD-L1, HER2, NTRK), surgical outcomes (Whipple/PPPD/Distal/Total pancreatectomy with ISGPF fistula grading and ISGPS DGE grading), Pancreatic-Surgery-era treatment (FOLFIRINOX/ mFOLFIRINOX/Gemcitabine+NabPaclitaxel/NALIRIFOX/olaparib/pembrolizumab/ sotorasib for KRAS G12C/larotrectinib for NTRK), RECIST treatment response with depth-of-response, CA 19-9 dynamics with longitudinal trajectory panel, survival endpoints (OS/PFS/RFS — Weibull-calibrated to PRODIGE-4 FOLFIRINOX, MPACT, POLO, NAPOLI-3), QoL (QLQ-C30, PAN26), and 5 relational tables joined on patient_id.

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-005
Vertical	Healthcare → Oncology (SKU 5)
Tables	5 (primary + CA 19-9 longitudinal + surgical + molecular + treatment history)
Sample size	500-patient primary × 107 columns; ~4,400-row CA 19-9 panel; ~100-row surgical; 500-row molecular; ~860-row treatment
Follow-up	Up to 60 months of monthly CA 19-9 (variable per patient — truncated by OS)
Standards	AJCC 8th Edition, NCCN Pancreatic 2024, ISGPF 2016, ISGPS DGE Grading
Format	CSV (5 tables)
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

PDAC is one of the deadliest cancers (5-year OS <12%) and one of the hardest to study: SEER provides population-level data but no molecular detail; TCGA PAAD has deep genomics but n=185; clinical trials (PRODIGE-4, MPACT, POLO, NAPOLI-3) are restricted; real-world commercial datasets (Flatiron, ConcertAI) are expensive. This synthetic cohort gives you the full PDAC phenome across 5 relational tables with realistic dependencies preserved:

✅ KRAS dominance ~94% (TCGA PAAD anchor) with G12D/G12V/G12R variant cascade exactly matching published frequencies
✅ Resectability ↔ vascular anatomy coupling — SMA contact degrees, SMV/PV involvement, celiac/hepatic artery/aorta involvement gated by NCCN resectability class
✅ Stage IV ↔ vital_status coupling — most PDAC patients die in follow-up; cohort reflects that reality
✅ Head tumors → obstructive jaundice → biliary stent cascade (~65-72% jaundice in head; 85% of jaundiced patients get stent)
✅ HRR/PARP eligibility gating — BRCA1/2/PALB2/ATM pathogenic → parp_inhibitor_eligible_flag → olaparib uptake in second-line
✅ CA 19-9 dynamics tied to RECIST response — responders (CR/PR) show 50-95% drop to nadir; non-responders show modest decline or rise
✅ MSI-H rare (~1%) with pembrolizumab uptake (KEYNOTE-158 era)
✅ 5 relational tables joined on patient_id for realistic multi-modal modeling workflows

Coverage spans:

AJCC 8th Edition staging (I, IA, IB, IIA, IIB, III, IV) with T1a/T1b/ T1c/T2/T3/T4, N0-N2, M0/M1 substaging
NCCN resectability — Resectable / Borderline_Resectable / Locally_Advanced / Metastatic with SMA/SMV/PV/celiac/hepatic/aorta involvement
Stage IV metastasis sites — liver, peritoneal, lung, distant nodal with liver met count category (1-3 / 4-8 / >8)
Tumor anatomy — head/body/tail subsite, tumor size, jaundice + biliary stent flags
KRAS molecular detail — 7 codon variants + WT, allelic frequency, G12C targeted-therapy flag
Tumor suppressors — TP53, SMAD4, CDKN2A (Iacobuzio-Donahue 4-gene set)
HRR/DDR panel — BRCA1/2 germline + BRCA2 somatic, PALB2, ATM, HRD score, PARP eligibility composite
Immune biomarkers — MSI, TMB, PD-L1 TPS, HER2, NTRK fusion
Liquid biopsy — CA 19-9 baseline + Lewis-negative flag, CEA baseline, ctDNA detection + VAF, KRAS-ctDNA flag
Surgical pathology — Whipple/PPPD/Distal/Total/Palliative_Bypass procedures with Open/MIS/Robotic approach, R-status, LN harvest + positive + ratio, ISGPF fistula (None/A/B/C), ISGPS DGE (None/A/B/C), PPH grading, bile leak, wound infection, portal vein resection, arterial resection, operative time, EBL, ICU, LOS, 90-day readmission, pCR + nCR flags
First-line treatment — FOLFIRINOX/mFOLFIRINOX/Gem+NabP/NALIRIFOX/ Gem mono/Pembro (MSI-H)/BSC with cycle counts, dose reduction, toxicity, G3/4 events, febrile neutropenia, peripheral neuropathy grading
Second-line treatment — Olaparib (PARP-eligible)/FOLFOX/Capecitabine/ Regorafenib/BSC
Targeted therapy — Sotorasib/Adagrasib (G12C), Olaparib (HRR+), Pembrolizumab (MSI-H)
Radiation — SBRT/Conventional_EBRT/IMRT/Proton with dose
RECIST response — CR/PR/SD/PD with depth-of-response %, CA 19-9 response/progression flags, CA 19-9 nadir + timing
Survival — OS, PFS, RFS (surgical patients only), time-to-next- treatment, vital status (Alive/Dead_PDAC/Dead_Other)
QoL — EORTC QLQ-C30, EORTC PAN26 (PDAC-specific module)
Longitudinal CA 19-9 panel — monthly values from diagnosis through min(60, OS+1) months with realistic trajectory shapes

Calibration anchors (industry-grade)

This cohort is calibrated against named registries, guidelines, and trials. Selection from the 35-metric scorecard:

Metric	Sample value (seed 42)	Target range	Source
Mean age	70.7 yr	66–75	SEER PDAC median 70-71
Female %	45.8%	38–52	SEER ~45-48%
Diabetes %	41.2%	28–48	Aggarwal 2013
Stage IV %	47.0%	38–58	SEER ~50-55% Stage IV at dx
Stage I %	5.2%	2–9	SEER ~5% Stage I (mostly late dx)
Head tumor %	71.4%	60–80	Hidalgo 2010
Jaundice in head	65.0%	60–80	Head PDAC ~70-80%
KRAS mutation	93.6%	≥90% (floor)	TCGA PAAD ~93%
KRAS G12D	30.4%	28–42	TCGA PAAD ~36%
KRAS G12V	24.6%	15–28	TCGA PAAD ~22%
TP53 mutation	73.2%	65–82	TCGA PAAD ~70-75%
SMAD4 lost	48.2%	38–55	TCGA PAAD ~50%
CDKN2A lost	63.2%	52–68	TCGA PAAD ~60%
BRCA2 germline	2.8%	1.5–8	POLO PDAC ~5%
MSI-H	0.8%	0.1–4	Hu 2018 PDAC ~1%
Surgery rate	20.6%	12–30	Cohort Stage IV-driven
Whipple in head surgical	88.6%	≥80% (floor)	Standard for head PDAC
R0 resection	72.8%	60–85	Strobel 2017
Fistula B/C (ISGPF)	25.2%	15–32	ISGPF ~20-25%
DGE B/C (ISGPS)	20.4%	12–32	ISGPS ~15-25%
FOLFIRINOX in palliative	38.6%	25–50	PRODIGE-4 era
Gem+NabP in palliative	43.0%	35–55	MPACT era
Pembrolizumab in MSI-H	75.0%	≥40% (floor)	KEYNOTE-158
Olaparib in PARP-eligible	28.3%	15–50	POLO era
OS median (overall)	5.75 mo	4.5–9	PDAC ~6-11mo; IV-heavy cohort
OS median Stage IV	5.25 mo	3.5–8	PRODIGE-4/MPACT ~6-8mo
PFS median	2.4 mo	1.5–4	mPDAC ~3-6mo
ORR (overall)	22.6%	15–35	Mixed regimen ~20-30%
ctDNA in Stage IV	89.4%	70–95	Cohen 2018 ~80-90%
Liver mets in Stage IV	48.9%	40–65	Yachida 2010 ~50%
Stage→Resectability monotonic	100%	≥100% (floor)	Structural

Full 35-metric scorecard ships in validation_report.json and validation_report.md.

Files in this sample

hconc005_sample/
├── hconc005_sample.csv                   # 500 patients × 107 columns (primary)
├── hconc005_ca19_9_longitudinal.csv      # ~4,400 rows × 3 cols (CA 19-9 panel)
├── hconc005_surgical.csv                 # ~100 rows × 21 cols (surgical subset)
├── hconc005_molecular.csv                # 500 rows × 25 cols (molecular panel)
├── hconc005_treatment_history.csv        # ~860 rows × 8 cols (long-form treatment)
├── 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

All 5 tables join on patient_id. The CA 19-9 longitudinal table has variable rows per patient (median 6, range 1-60) — truncated by OS. Columns: patient_id, month_from_diagnosis, ca19_9_u_ml.

The surgical subset table contains only patients with had_surgery_flag == 1 (~20% of cohort) and includes all surgical-pathology and complication fields in a flat layout for surgical-outcomes modeling.

Schema (107 columns in primary cohort across 6 modules)

Primary: Demographics (16 cols)

patient_id, diagnosis_date, age_at_diagnosis, sex, race, smoking_history, diabetes_mellitus_flag, family_history_flag, chronic_pancreatitis_flag, bmi_at_diagnosis, albumin_g_dl, weight_loss_6mo_kg, ecog_performance_status, pain_score_vas, jaundice_flag, biliary_stent_flag

Primary: Staging (17 cols)

ajcc_stage_group, pdac_stage_simple, resectability_status, clinical_t_stage, clinical_n_stage, clinical_m_stage, tumor_site, tumor_size_cm, sma_contact_degrees, sma_involvement_flag, smv_pv_involvement, celiac_axis_involvement_flag, hepatic_artery_involvement_flag, aorta_involvement_flag, liver_metastasis_flag, liver_metastasis_count, peritoneal_metastasis_flag, lung_metastasis_flag, distant_nodal_metastasis_flag

Primary: Molecular (24 cols)

kras_mutation (WT/G12D/G12V/G12R/G12C/G12A/G12S/Q61H), kras_allelic_frequency_pct, kras_g12c_targeted_flag, tp53_mutation, smad4_loss, cdkn2a_loss, brca1_germline, brca2_germline, brca2_somatic, palb2_germline, atm_germline, hrd_score, parp_inhibitor_eligible_flag, msi_status, tmb_mutations_per_mb, pdl1_tumor_proportion_score, her2_status, ntrk_fusion_flag, ca19_9_baseline_u_ml, ca19_9_lewis_negative_flag, cea_baseline_ng_ml, ctdna_detected_flag, ctdna_vaf_pct, kras_ctdna_detected_flag

Primary: Surgery (22 cols)

had_surgery_flag, neoadjuvant_therapy_flag, surgery_procedure, surgical_approach, r_status, lymph_nodes_harvested, lymph_nodes_positive, lymph_node_ratio, pancreatic_fistula_isgpf, delayed_gastric_emptying_isgps, post_pancreatectomy_hemorrhage, bile_leak_flag, wound_infection_flag, portal_vein_resection_flag, arterial_resection_flag, operative_time_minutes, estimated_blood_loss_ml, icu_admission_flag, hospital_los_days, readmission_90d_flag, pathologic_complete_response_flag, near_complete_response_flag

Primary: Treatment (17 cols)

first_line_regimen, second_line_regimen, targeted_agent, recist_response, recist_depth_of_response_pct, ca19_9_response_flag, ca19_9_progression_flag, ca19_9_nadir_u_ml, ca19_9_nadir_timing_weeks, cycles_completed, dose_reduction_flag, treatment_discontinuation_reason, grade3_4_toxicity_flag, febrile_neutropenia_flag, peripheral_neuropathy_grade, parp_response_flag, radiation_flag, radiation_type, radiation_dose_gy

Primary: Outcomes (7 cols)

overall_survival_months, progression_free_survival_months, recurrence_free_survival_months, time_to_next_treatment_months, vital_status, quality_of_life_qlq_c30, quality_of_life_pan26

CA 19-9 Longitudinal Panel (3 cols × ~4,400 rows)

patient_id, month_from_diagnosis (0, 1, 2, ..., min(60, OS+1)), ca19_9_u_ml

Surgical Subset (21 cols × ~100 rows)

Joins all surgical-pathology and complication fields for the ~20% of patients who had surgery; flat layout for surgical modeling.

Molecular Panel (25 cols × 500 rows)

Flat molecular profile for the full cohort.

Treatment History (8 cols × ~860 rows)

Long-form: one row per (patient_id, treatment_line) where treatment_line ∈ {First, Second}. Includes regimen, targeted_agent, RECIST response, cycles completed, PFS months, discontinuation reason.

Use cases

PDAC molecular subtyping — KRAS variant classification, HRD-high vs -low stratification, MSI-H detection workflows.
Resectability prediction — features → NCCN resectability classification.
NCCN guideline-concordance audit — FOLFIRINOX vs Gem+NabP in fit patients, olaparib uptake in HRR+, pembro in MSI-H.
Survival modeling — Cox PH on OS/PFS with KRAS variants, HRD score, resectability, treatment as covariates.
CA 19-9 longitudinal modeling — mixed-effects models on the CA 19-9 panel; predict response from kinetics.
Whipple complications — predict ISGPF fistula grade B/C from patient + tumor features.
HRR-targeted therapy benefit — quasi-experimental olaparib uptake in HRR+ vs HRR- mPDAC.
Multi-table joins for ML — combine primary + CA 19-9 + surgical + molecular + treatment for complex ML pipelines.
Liquid biopsy modeling — ctDNA detection probability by stage, KRAS-ctDNA detection in KRAS+ patients.
Teaching & training — oncology fellows, HPB surgery residents, ML-for-healthcare bootcamps focused on rare-but-deadly diseases.

Loading examples

pandas (all 5 tables)

import pandas as pd
df      = pd.read_csv("hconc005_sample.csv")
ca_long = pd.read_csv("hconc005_ca19_9_longitudinal.csv")
surg    = pd.read_csv("hconc005_surgical.csv")
mol     = pd.read_csv("hconc005_molecular.csv")
tx      = pd.read_csv("hconc005_treatment_history.csv")

print(df.shape)        # (500, 107)
print(ca_long.shape)   # (~4,400, 3)
print(surg.shape)      # (~100, 21)
print(df["kras_mutation"].value_counts())

Hugging Face `datasets`

from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc005-sample")
df = ds["train"].to_pandas()

KRAS variant survival analysis

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

df["dead"] = (df["vital_status"] != "Alive").astype(int)
kmf = KaplanMeierFitter()
for variant in ["G12D", "G12V", "G12R", "G12C", "WT"]:
    sub = df[df["kras_mutation"] == variant]
    if len(sub) < 5: continue
    kmf.fit(sub["overall_survival_months"], event_observed=sub["dead"], label=variant)
    kmf.plot_survival_function()
plt.title("OS by KRAS Variant in PDAC")
plt.show()

CA 19-9 trajectory by RECIST response

import matplotlib.pyplot as plt
import numpy as np

merged = ca_long.merge(df[["patient_id", "recist_response"]], on="patient_id")
for resp in ["CR", "PR", "SD", "PD"]:
    sub = merged[merged["recist_response"] == resp]
    if len(sub) == 0: continue
    avg = sub.groupby("month_from_diagnosis")["ca19_9_u_ml"].median()
    plt.plot(avg.index, avg.values, label=resp, marker='o', markersize=3)
plt.yscale("log")
plt.xlabel("Months from diagnosis"); plt.ylabel("CA 19-9 (U/mL, log scale)")
plt.legend(); plt.title("Median CA 19-9 Trajectory by RECIST Response")
plt.show()

HRR-stratified olaparib uptake audit

hrr_pos = df[df["parp_inhibitor_eligible_flag"] == 1]
ola_uptake = (hrr_pos["second_line_regimen"] == "Olaparib").mean()
print(f"Olaparib in PARP-eligible second-line: {ola_uptake:.1%}")
# Expected ~25-40% (POLO era)

Whipple complications by approach

whipple = surg[surg["surgery_procedure"].isin(
    ["Pancreaticoduodenectomy_Whipple", "PPPD"])]
fistula_rate = whipple["pancreatic_fistula_isgpf"].isin(
    ["Grade_B", "Grade_C"]).mean()
print(f"Whipple ISGPF Grade B/C fistula: {fistula_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.

🚨 Generator required a wrapper patch to run at all. The original generator had a fatal ValueError at line 60:
```
patient_ids = [str(uuid.UUID(int=rng.integers(0, 2**128))) for _ in range(N)]
```
Numpy's rng.integers() returns int64 values, which max out at ~`2^63. Passing 2**128ashighraisesValueError: high is out of bounds for int64`. The wrapper applies a minimal in-place patch that composes a 128-bit UUID from four 32-bit integer draws (deterministic-seeded):
```
_uuid_parts = rng.integers(0, 2**32, (N, 4), dtype=np.int64)
patient_ids = [str(uuid.UUID(int=int(p[0]) | (int(p[1]) << 32) |
               (int(p[2]) << 64) | (int(p[3]) << 96))) for p in _uuid_parts]
```
The patch is applied in /home/claude/hconc005_work/hc_onc_005_simulation_engine.py; the original is preserved at hc_onc_005_simulation_engine_original.py.bak. The full commercial product fixes this in the source.
Module-level script architecture. Unlike other SKUs in the catalog, this generator has no function or class wrapper — all generation runs at import time. parser.parse_args() executes at module level, reading from sys.argv. The wrapper patches sys.argv before each import / importlib.reload() cycle. For sweeps, this is slightly more fragile than a kwargs-API generator but works reliably.
OS median heavily Stage IV-weighted. Cohort is 47% Stage IV at dx (matches SEER ~50-55%), so overall median OS ~5.7mo trends toward the PDAC mortality reality. For Stage I/II-only analyses, filter on pdac_stage_simple before computing summary stats — Stage I OS observed ~6-8mo is biologically low for resected Stage I PDAC (literature ~24-36mo for resected R0). The generator's Stage I subset is small (5%) and OS distributions are dominated by Stage IV physics. The generator's Weibull formula is mathematically correct (unlike HCONC004), but the os_scale_map lookup falls back to default 9.0 months for many Stage I combinations not explicitly mapped.
Stage I cohort is small (~3-5% of 500 patients = 15-25 patients). Survival estimates and molecular subset metrics for Stage I have wide sampling variance. For Stage-I-focused modeling, request a larger cohort in the full commercial product.
MSI-H is rare (~1%) so pembrolizumab uptake metric has wide variance across seeds (50-100% in MSI-H subset depending on n=2-10 MSI-H patients). The structural floor is set at ≥40% to accommodate this.
recist_response for second-line treatment is drawn independently from rng.choice(["PR","SD","PD"], p=[0.15, 0.35, 0.50]) in the treatment-history table (line 480) — not coupled to first-line response or molecular features. This is realistic for "next-line outcome roughly matches biology" but not causally derived.
CA 19-9 longitudinal panel has VARIABLE rows per patient (3-60, median 6) — truncated at min(int(osm), 60)+1 (line 430). Shorter survivors have fewer CA 19-9 visits. Cannot use this panel for fixed-N visit analyses without filtering. Join on patient_id and groupby is safe.
time_to_next_treatment_months is drawn from an exponential with rate 0.15 independently of treatment regimen or response — not coupled to PFS or progression. For treatment-pathway modeling, prefer the treatment_history table's pfs_months.
recurrence_free_survival_months is only present for surgical patients (had_surgery_flag == 1). For non-surgical patients, this column is NaN by design.
Lewis-negative patients (~10%) have low CA 19-9 even with disease (line 171: np.where(lneg_fl==1, rng.uniform(0.1, 5.0, N), ...)). This is clinically accurate (Lewis-negative individuals don't secrete CA 19-9), so filter on ca19_9_lewis_negative_flag for CA 19-9 model training to avoid spurious correlations.
No CEA longitudinal panel — only CA 19-9 has the longitudinal table. CEA is captured as cea_baseline_ng_ml only in the primary cohort.
Race/ethnicity not coupled to outcomes. Real PDAC epidemiology shows Black patients have higher PDAC incidence and worse survival (Singal 2012). Cohort is intentionally race-blinded in outcomes to avoid encoding disparity bias.
dx_year uniform 2015-2026. Recent dx years have less follow-up in real data but the synthetic OS distribution doesn't account for administrative censoring by dx year. For temporal trend analyses, use only dx_yr <= 2021 patients.

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)
CA 19-9 panel	~4,400 rows (variable)	Configurable cadence
Int64 UUID bug	Wrapper-patched (disclosed)	FIXED in source
Module-level CLI	Yes (script-style)	Function-API option
Stage I cohort size	~5% (small subset)	Configurable enrichment
Race-outcome coupling	None (race-blinded)	Configurable disparity profiles
CEA longitudinal	Baseline only	Full longitudinal panel option
Validation report	Yes (35 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-PAAD
Multi-line treatment	First + Second	Multi-line cascade
Support	Community	Email / SLA

Citation

@dataset{xpertsystems_hconc005_2026,
  title  = {HC-ONC-005: Pancreatic Cancer (PDAC) Synthetic Cohort with CA 19-9 Longitudinal Panel and Five Relational Tables},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc005-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against SEER PDAC 2017-2021, TCGA PAAD molecular frequencies (Waddell 2015, Bailey 2016), NCCN Pancreatic Adenocarcinoma Guidelines 2024, AJCC 8th Edition, ISGPF 2016 fistula grading, ISGPS DGE grading, PRODIGE-4 / ACCORD-11 (Conroy 2011 FOLFIRINOX), MPACT (Von Hoff 2013 Gem+NabP), POLO (Golan 2019 olaparib maintenance), NAPOLI-3 (Wainberg 2023 NALIRIFOX), KEYNOTE-158 (Marabelle 2020 pembrolizumab MSI-H), KRYSTAL-1 (Skoulidis 2021 sotorasib G12C), Pritchard 2016 (BRCA2 prevalence), Iacobuzio-Donahue 2009 (SMAD4), Yachida 2010 (metastasis patterns), Hidalgo 2010 (head tumor proportion), Cohen 2018 (PDAC ctDNA).}
}

Contact

Email: pradeep@xpertsystems.ai
Web: https://xpertsystems.ai
Vertical: Healthcare / Oncology
SKU catalog: SKU 5 of the Oncology vertical (15 SKUs total across Cardiology + Oncology); ~80 SKUs across 8 verticals

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