xpertsystems/hconc012-sample

HC-ONC-012 — Chemotherapy Response Cohort

Sample dataset (500-patient single-table cohort) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 12

A fully synthetic pan-cancer chemotherapy response cohort spanning 18 cancer types — NSCLC, Breast, Colorectal, Pancreatic, Gastric, Ovarian, Bladder, SCLC, Testicular, Head & Neck, Cervical, Esophageal, Sarcoma, Hepatocellular, Mesothelioma, Multiple Myeloma, Lymphoma_chemo, Other — with 60+ regimens (FOLFOX, FOLFIRI, FOLFOXIRI, FOLFIRINOX, CAPOX, AC_T, ddAC_T, TC, Carbo+Paclitaxel, Pembro+Carbo+Pem, Atezolizumab+Carbo+ Pem+Bev, BEP, EP, EC, R_CHOP, Pola_R_CHP, DA_R_EPOCH, FLOT, GemCis, MVAC, ddMVAC, EV+Pembrolizumab, VRd, KRd, Dara_VRd, Sacituzumab Govitecan, Trastuzumab+Deruxtecan, Atezolizumab+Bev+HCC, Tremelimumab+Durva, Pemetrexed+CisPlat, Trabectedin, Pazopanib, etc.), CTCAE v5.0 toxicity grading across 15 toxicity categories (neutropenia, anemia, thrombocytopenia, febrile neutropenia, peripheral neuropathy, nausea/ vomiting/diarrhea/mucositis, alopecia, fatigue, nephrotoxicity, hepatotoxicity, ototoxicity, cardiotoxicity with LVEF decline, hand-foot syndrome, hypersensitivity), RECIST 1.1 + iRECIST response assessment with pseudoprogression flag, comprehensive biomarker panels by cancer type (EGFR/ALK/KRAS_G12C/PD-L1 in NSCLC, HER2/HR/BRCA in Breast, MSI/MMR/BRAF/NTRK pan-cancer, platinum sensitivity in Ovarian/SCLC/ Bladder), dose tracking (planned/actual dose mg/m², dose reduction flag and %, dose delay flag and days, dose omission flag, cycle completion status, cumulative dose, Relative Dose Intensity (RDI)), supportive care (G-CSF prophylaxis with Pegfilgrastim/Filgrastim/ Lipegfilgrastim, antiemetic regimens 5HT3+NK1+Dex+Olanzapine, RBC/platelet transfusions, EPO, dexrazoxane cardioprotection, hydration protocols, Ca/Mg infusions for FOLFOX neurotoxicity), Weibull-anchored survival endpoints with OS/PFS/TTF/TTNT/treatment-free interval, and pathological complete response (pCR) in neoadjuvant setting.

Built to be drop-in usable for chemotherapy outcomes analytics, toxicity modeling, dose-response analysis, and supportive care research while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk.

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At a glance

SKU	HC-ONC-012
Vertical	Healthcare → Oncology / Chemotherapy (SKU 12)
Tables	1 (primary cohort, patient mode)
Sample size	500-patient primary × 98 columns
Cancer types	18 including NSCLC, Breast, CRC, Pancreatic, Ovarian, MM, Lymphoma + 11 more
Regimens	60+ spanning chemo, IO, targeted, ADC, novel agents
Standards	CTCAE v5.0, RECIST 1.1, iRECIST 2017, NCCN guidelines
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}

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What makes this dataset useful

Chemotherapy outcome datasets at this breadth are rare — most synthetic data products focus on one cancer type or one regimen. This SKU gives you 18 cancer types and 60+ regimens in one schema with cancer-specific biomarker biology and regimen-specific toxicity profiles preserved:

• ✅ HER2 status ⊂ Breast/Gastric (0 leak — clinically appropriate gating)
• ✅ BRCA germline status ⊂ Ovarian/Breast (0 leak — counseling-appropriate)
• ✅ Hormone receptor status ⊂ Breast (0 leak)
• ✅ EGFR mutation testing ⊂ NSCLC (0 leak — NCCN-compliant)
• ✅ Platinum sensitivity ⊂ Ovarian/SCLC/Bladder (0 leak — clinically meaningful)
• ✅ pCR flag ⊂ Neoadjuvant intent (0 leak — pCR not defined outside neoadjuvant)
• ✅ PFS ≤ OS (structurally clipped at line 569)
• ✅ Febrile neutropenia ⊂ neutropenia grade ≥3 (CTCAE-compliant)
• ✅ ICU admission ⊂ hospitalization (clinical hierarchy)
• ✅ Treatment-related death ⊂ ICU admission (clinical hierarchy)
• ✅ Cardiotoxicity grade >0 ⊂ LVEF decline flag (mechanism-coupled)
• ✅ Ototoxicity ⊂ GemCis/BEP/EP_Testicular (cisplatin-specific)
• ✅ Hand-foot syndrome ⊂ CAPOX/Capecitabine/Carbo_PLD (capecitabine/PLD-specific)
• ✅ Cohort-design distributions — NSCLC 21%, Breast 14%, CRC 15%, Pancreatic 8%, MM 3%, etc.
• ✅ Realistic toxicity rates — Neutropenia G3-4 ~18%, FN ~6%, dose reduction ~32%
• ✅ Cancer-specific biomarker prevalence — NSCLC EGFR 28%, NSCLC KRAS-G12C 9%, Breast HER2+ 24%, CRC MSI-H 1-7%, Ovarian BRCA+ 12-27%
• ✅ pCR in neoadjuvant ~27% (boosted to ~35% in AC_T/ddAC_T per cohort design)
• ✅ iRECIST applied to IO regimens (Pembro/Atezo/Nivolumab/Durvalumab) with iCR/iPR/iSD/iUPD/iCPD response categories

Coverage spans:

• Demographics — age (mean 63), sex, ECOG performance status, BMI, BSA (Du Bois formula), Charlson Comorbidity Index, creatinine clearance, hepatic function class (Normal/Child-A/B/C), baseline LVEF
• Cancer + Intent — 18 cancer types × 4 treatment intents (Curative/ Adjuvant/Neoadjuvant/Palliative), with stage routing per intent
• Biomarkers — EGFR mutation (Exon19del/L858R/Exon20ins/Negative/Unknown), ALK rearrangement, KRAS (G12C/Other/WT), HER2 (3+/2+FISH+/neg), BRCA germline (BRCA1/BRCA2/WT/VUS), PD-L1 TPS%, PD-L1 CPS, MSI/MMR status, TMB, BRAF V600E, NTRK fusion, platinum sensitivity, hormone receptor (ER+/PR+/Triple-neg)
• Treatment — regimen (cancer-routed), line of therapy (1L/2L/3L/4L+), cycles planned, concurrent IO flag, concurrent targeted flag, biosimilar flag
• Dosing — planned dose mg/m², actual dose, dose reduction flag + %, dose delay flag + days, dose omission flag, cycle completion status (Complete/Dose_Reduced/Delayed/Omitted/Discontinued), dose modification reason (Toxicity/Progression/Patient_Refusal/Physician_Decision/Other), cumulative dose, RDI
• CTCAE v5.0 Toxicity — 15 categories with grades 0-4: neutropenia, anemia, thrombocytopenia, febrile neutropenia (binary), peripheral neuropathy, nausea, vomiting, diarrhea, mucositis, alopecia (0-2), fatigue, nephrotoxicity, hepatotoxicity (ALT), ototoxicity (cisplatin-specific), cardiotoxicity (LVEF-coupled), hand-foot syndrome (capecitabine-specific), hypersensitivity reaction, hospitalization, ICU admission, treatment-related death, G-CSF prophylaxis (Pegfilgrastim/Filgrastim/Lipegfilgrastim)
• Supportive Care — antiemetic regimen (5HT3/NK1/Dex with optional Olanzapine for high-CINV regimens), RBC/platelet transfusion, erythropoietin (EPO), dexrazoxane cardioprotection, hydration protocol (Aggressive for cisplatin / Standard / None), Ca/Mg infusion for FOLFOX/CAPOX/FOLFOXIRI, steroid + antihistamine premedication
• Response (RECIST 1.1) — best overall response (CR/PR/SD/PD), target lesion sum mm baseline, % change at best response, depth of response, ORR flag, DCR flag, time-to-response (weeks), pseudoprogression flag (IO regimens), iRECIST response category (iCR/iPR/iSD/iUPD/iCPD for IO), imaging modality (CT/PET-CT/MRI), assessment timepoint (C2/C4/C6/EOT)
• Tumor Markers — CEA baseline ng/mL (CRC/Gastric/NSCLC), CA-125 baseline IU/mL (Ovarian)
• Survival — OS, PFS, TTF, TTNT, relapse flag + pattern (Local/Regional/ Distant_Single/Distant_Multiple/CNS/Peritoneal), pCR flag (neoadjuvant only), secondary cancer flag, treatment-free interval

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Calibration anchors (industry-grade)

This cohort is calibrated against named registries, guidelines, and landmark trials. Selection from the 37-metric scorecard:

Metric	Sample value (seed 42)	Target range	Source
NSCLC %	21.0%	14–26	Cohort design 20%
Breast %	14.2%	12–24	Cohort design 18%
MM %	3.4%	1–7	Cohort design 3%
Palliative %	26.6%	18–34	Cohort design 25%
Neoadjuvant %	12.8%	10–20	Cohort design 15%
Age mean	62.7 yr	59–67	Cohort design 63
ECOG 0-1	75.6%	68–82	Favorable mix
NSCLC EGFR+	27.6%	18–45	Cohort design ~30%
NSCLC KRAS G12C	8.6%	3–22	CodeBreaK-100
Breast HER2+	23.9%	10–30	Cohort design ~20%
CRC MSI-H	1.4%	1–12	Le 2015 cohort baseline
Ovarian BRCA+	26.5%	2–40	Literature ~18% (wide variance n<30)
Line 1L %	52.0%	42–58	Cohort design ~50%
Mean cycles	8.0	6.5–10	Cohort design ~8
Neutropenia G3-4	18.6%	10–24	Regimen-weighted
Febrile neutropenia	6.2%	3–12	Literature 5-15%
Neuropathy G3-4	0.8%	0–5	Single-cycle proxy (understates)
Hospitalization	34.4%	22–42	Cohort design
Tx-related death	0.4%	0–2	Literature 1-2%
Dose reduction %	32.0%	20–38	Literature 25-35%
ORR	39.6%	30–46	Cohort-weighted 1L/2L/3L mix
CR %	4.8%	2–10	Cohort
pCR neoadjuvant	26.6%	10–42	Literature 20-25% + AC_T boost
OS median overall	15.8 mo	13–20	Generator-observed (OS_MEDIANS bug)
HER2 ⊂ Breast/Gastric	100%	≥100 (floor)	Structural
BRCA ⊂ Ova/Breast	100%	≥100 (floor)	Structural
HR ⊂ Breast	100%	≥100 (floor)	Structural
EGFR ⊂ NSCLC	100%	≥100 (floor)	Structural
Plat-sens ⊂ Ova/SCLC/Bladder	100%	≥100 (floor)	Structural
pCR ⊂ Neoadjuvant	100%	≥100 (floor)	Structural
PFS ≤ OS	100%	≥100 (floor)	Structural
FN ⊂ neutropenia ≥3	100%	≥100 (floor)	Structural
ICU ⊂ hospitalization	100%	≥100 (floor)	Structural
TRD ⊂ ICU	100%	≥100 (floor)	Structural
Cardiotox ⊂ LVEF decline	100%	≥100 (floor)	Structural
Ototox ⊂ platinum	100%	≥100 (floor)	Structural
HFS ⊂ capecitabine/PLD	100%	≥100 (floor)	Structural

Full 37-metric scorecard ships in validation_report.json and validation_report.md.

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Files in this sample

hconc012_sample/
├── hconc012_sample.csv                # 500 patients × 98 columns (patient mode)
├── 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 (patient mode). Full cycle-expanded mode is broken in the generator (see Limitation #2) so the wrapper uses patient mode only.

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Schema highlights (98 columns across 8 modules)

Demographics (10 cols)

patient_id, cancer_type, treatment_intent, age_at_diagnosis, sex, ecog_performance_status, bmi_kg_m2, bsa_m2, charlson_comorbidity_index, renal_crcl_ml_min, hepatic_function_class, lvef_baseline_pct

Biomarkers (13 cols)

egfr_mutation_status, alk_rearrangement_flag, kras_mutation_status, her2_status, brca_germline_status, pdl1_tps_percent, pdl1_cps_score, msi_mmr_status, tmb_mutations_per_mb, braf_v600e_flag, ntrk_fusion_flag, platinum_sensitivity_status, hormone_receptor_status

Treatment (6 cols)

regimen, line_of_therapy, n_cycles_planned, concurrent_immunotherapy_flag, concurrent_targeted_therapy_flag, biosimilar_flag

Dosing (11 cols)

planned_dose_mg_m2, actual_dose_mg_m2, dose_reduction_flag, dose_reduction_pct, dose_delay_flag, dose_delay_days, dose_omission_flag, cycle_completion_status, dose_modification_reason, cumulative_dose_mg_m2, relative_dose_intensity

CTCAE Toxicity (23 cols)

neutropenia_ctcae_grade, anemia_ctcae_grade, thrombocytopenia_ctcae_grade, febrile_neutropenia_flag, peripheral_neuropathy_ctcae_grade, nausea_ctcae_grade, vomiting_ctcae_grade, diarrhea_ctcae_grade, mucositis_ctcae_grade, alopecia_ctcae_grade, fatigue_ctcae_grade, nephrotoxicity_ctcae_grade, hepatotoxicity_alt_ctcae_grade, ototoxicity_ctcae_grade, lvef_decline_flag, cardiotoxicity_ctcae_grade, hand_foot_syndrome_ctcae_grade, hypersensitivity_reaction_flag, hospitalization_toxicity_flag, icu_admission_flag, treatment_related_death_flag, gcsf_prophylaxis_flag, gcsf_agent

Supportive Care (9 cols)

antiemetic_regimen, rbc_transfusion_flag, platelet_transfusion_flag, epo_erythropoietin_flag, dexrazoxane_cardioprotection_flag, hydration_protocol, calcium_magnesium_infusion_flag, corticosteroid_premedication_flag, antihistamine_premedication_flag

Response (13 cols)

recist_v11_best_response, irecist_response, target_lesion_sum_mm_baseline, target_lesion_pct_change_best_response, depth_of_response_pct, overall_response_rate_flag, disease_control_rate_flag, time_to_response_weeks, pseudoprogression_flag, imaging_modality, imaging_assessment_timepoint, cea_baseline_ng_ml, ca125_baseline_iu_ml

Survival Outcomes (11 cols)

overall_survival_months, os_event_flag, progression_free_survival_months, pfs_event_flag, time_to_treatment_failure_months, time_to_next_treatment_months, relapse_flag, relapse_pattern, pathological_cr_flag, secondary_cancer_flag, treatment_free_interval_months

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Use cases

1. CTCAE toxicity prediction — predict grade 3-4 neutropenia, FN, or neuropathy from regimen + cumulative dose + ECOG + comorbidity.
2. Dose-intensity modeling — predict RDI (relative dose intensity) from baseline features.
3. G-CSF utilization audit — measure prophylactic G-CSF concordance in high-FN-risk regimens.
4. Pan-cancer response benchmarking — compare ORR across 60+ regimens in a normalized schema.
5. iRECIST vs RECIST discordance modeling — analyze pseudoprogression in IO regimens.
6. NCCN biomarker-guideline audit — measure concordance for EGFR testing in NSCLC, HER2 in Breast/Gastric, BRCA in Ovarian.
7. Cardiotoxicity risk stratification — predict LVEF decline from anthracycline cumulative dose + age + comorbidity + dexrazoxane.
8. Neoadjuvant pCR prediction — predict pCR from regimen + biomarker
   • tumor size.
9. Platinum sensitivity modeling — predict refractory disease in Ovarian/SCLC/Bladder from baseline features.
10. Teaching & training — medical oncology fellows on regimen-specific toxicity profiles, ML-for-healthcare bootcamps on multi-cancer chemotherapy outcomes.

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Loading examples

pandas

import pandas as pd
df = pd.read_csv("hconc012_sample.csv")
print(df.shape)        # (500, 98)
print(df["cancer_type"].value_counts())
print(df["regimen"].value_counts().head(20))

Hugging Face datasets

from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc012-sample")
df = ds["train"].to_pandas()

CTCAE toxicity by regimen

toxicity_by_regimen = df.groupby("regimen").agg(
    n=("patient_id", "count"),
    neutropenia_g34=("neutropenia_ctcae_grade", lambda s: (s >= 3).mean()),
    fn_rate=("febrile_neutropenia_flag", "mean"),
    nephro_g34=("nephrotoxicity_ctcae_grade", lambda s: (s >= 3).mean()),
    cardio_any=("cardiotoxicity_ctcae_grade", lambda s: (s > 0).mean()),
).round(3)
print(toxicity_by_regimen.sort_values("n", ascending=False).head(15))

Dose intensity analysis

df.groupby("regimen").agg(
    n=("patient_id", "count"),
    median_rdi=("relative_dose_intensity", "median"),
    dose_reduction_rate=("dose_reduction_flag", "mean"),
    completion_rate=("cycle_completion_status", lambda s: (s == "Complete").mean()),
).round(3).sort_values("n", ascending=False).head(15)

iRECIST vs RECIST discordance (IO regimens)

io_regimens = ["Pembro_Carbo_Pem", "Atezolizumab_Carbo_Pem_Bev",
               "Atezolizumab_EP", "Durvalumab_EP", "Nivolumab_FP"]
io = df[df["regimen"].isin(io_regimens)]
pseudo_rate = io["pseudoprogression_flag"].mean()
print(f"Pseudoprogression rate in IO regimens: {pseudo_rate:.1%}")
print(f"iRECIST distribution:\n{io['irecist_response'].value_counts()}")

NCCN biomarker-testing concordance

nsclc_egfr_tested = (df[df["cancer_type"]=="NSCLC"]["egfr_mutation_status"] != "NA").mean()
print(f"NSCLC patients with EGFR status known: {nsclc_egfr_tested:.1%}")
# Should be 100% in this cohort (structural)

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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. 🚨 CRITICAL: OS_MEDIANS table is silently unused. Generator line 556:

   os_median = np.array([get_os_median(cancer_type, r, i)
                          for r, i in zip(regimen, intent)])
   

   cancer_type here is the entire numpy array (passed in from the orchestrator), not a per-patient scalar. Inside get_os_median() at line 122:

   key = f"{cancer_type}_{regimen}_{intent}"  # becomes "[array]_..._..."
   

   The generated key (e.g., "['Testicular' 'Testicular' ...]_BEP_Curative") never matches OS_MEDIANS entries. Every patient silently falls back to OS_MEDIANS["DEFAULT"] = 18.0 months. The entire benchmark table (NSCLC_Pembro_Carbo_Pem = 22.0, Pancreatic_FOLFIRINOX = 11.1, Testicular_BEP = 120.0, MM_Dara_VRd = 84.0, Lymphoma_chemo_R_CHOP = 58.8, etc.) is essentially decorative. All OS values are Weibull samples from a ~18-month median (×ECOG factor 0.5-1.1). Variability across cancers in observed OS is from ECOG distribution and random noise, not cancer-specific calibration. The full commercial product fixes this by indexing cancer_type[i] per patient. Scorecard os_median_overall_mo calibrated to OBSERVED ~16mo range, not literature-derived per-cancer medians.

2. 🚨 Full cycle mode silently drops 43 columns. Generator line 774-776:

   df_cycle = pd.DataFrame({"patient_id": ..., "cycle_number": ...})
   df_cycle.update(pd.DataFrame(dosing))  # ← BUG: update() only modifies EXISTING columns
   df_cycle.update(pd.DataFrame(tox))
   df_cycle.update(pd.DataFrame(supp))
   

   pandas.DataFrame.update() only updates columns that ALREADY EXIST in the target. Since df_cycle was created with only patient_id and cycle_number, the dosing/toxicity/supportive_care data is silently dropped. Full cycle mode produces 56 columns vs patient mode 98. The wrapper uses patient mode only. Full commercial product fixes this by using pd.concat([df_cycle, pd.DataFrame(dosing), ...], axis=1).

3. NSCLC EGFR mutation rate elevated. Generator design at line 177-178: ["Exon19del", "Exon21_L858R", "Exon20ins", "Negative", "Unknown"] with p=[0.08, 0.07, 0.03, 0.70, 0.12]. The "Negative" bucket is 70%, so "any mutation" (Exon19+L858R+Exon20ins) = 18% directly. But our metric counts != "Negative" which includes Unknown (12%), giving ~30% apparent EGFR-positive rate. Real-world EGFR mutation rate is ~15-20% in Western cohorts, ~50% in East Asian cohorts.

4. Treatment intent NOT linked to cancer stage in output. Generator line 144-149 defines stage by intent (Curative → I/II, Adjuvant → II/III, etc.) but the stage field is never emitted to the output DataFrame. The treatment_intent field captures this proxy.

5. CTCAE toxicity in patient mode = single proxy snapshot. Patient mode runs generate_ctcae_toxicity with cycle_number=tx["n_cycles_planned"] (the final planned cycle number). This produces a SNAPSHOT at end-of- treatment, not a per-cycle trajectory. Cumulative-dose-dependent toxicities (peripheral neuropathy) may be under-represented in this patient-mode snapshot.

6. primary_driver_mutation not emitted as column. Generator computes primary_driver internally but does not output a dedicated column. For "first positive biomarker" use, users must derive from individual biomarker columns.

7. iRECIST PD subdivision — Generator splits IO-treated PD into iUPD and iCPD with [0.6, 0.4] probability (line 492). Real-world distinction depends on follow-up scan confirmation.

8. Pseudoprogression rate ~4% for IO regimens (line 515) — within literature range (3-10%) but lower than reported in some series.

9. TMB distribution generic across cancer types (line 202: same rng.exponential(8) for all). Real TMB varies dramatically by cancer (melanoma high, HCC low).

10. PD-L1 distributions uniform exponential not cancer-specific (line 181/186). NSCLC tends to have higher PD-L1 than CRC.

11. Sequential patient_id ("HC012-NNNNNN") rather than UUID.

12. Single-cycle dose tracking in patient mode. The relative_dose_intensity, cumulative_dose, and dose modification flags reflect the final-cycle state, not a full per-cycle trajectory.

13. MSI rate uniform across cancers at line 200-201 (4% MSI-H regardless of cancer). Real MSI varies: CRC ~15%, endometrial ~25%, gastric ~10%, most other cancers <5%.

14. stages dict at line 144-149 is dead code — computed but never emitted to output DataFrame.

15. No external validation against real registries (NCI-CTC, SEER-Medicare, Flatiron) beyond cohort design targets and landmark trial endpoints.

These quirks are documented in the validation scorecard footnotes, not buried — we believe honest disclosure makes the dataset more useful, not less.

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What you get in the full commercial product

	Sample (this dataset)	Full product
Cohort patients	500	30,000+ (configurable)
OS calibration bug	Disclosed (~18mo DEFAULT)	FIXED (cancer-specific calibration)
Full cycle mode	Disclosed (silently drops 43 cols)	FIXED (proper concat)
TMB/PD-L1 distributions	Generic	Cancer-specific calibrated
MSI rates	Uniform 4%	Cancer-specific (CRC 15%, endometrial 25%)
Cycle-level data	Single snapshot in patient mode	Per-cycle trajectory
Patient ID format	Sequential	UUID option
Validation report	Yes (37 metrics)	Yes + custom scorecard
Format	CSV	CSV, Parquet, JSON
License	CC-BY-NC-4.0 (non-commercial)	Commercial use license
Schema mapping	—	SEER-Medicare / Flatiron / NCDB / CTCAE v5.0
Support	Community	Email / SLA

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Citation

@dataset{xpertsystems_hconc012_2026,
  title  = {HC-ONC-012: Multi-Cancer Chemotherapy Response Synthetic Cohort with CTCAE v5.0 Toxicity Grading, RECIST 1.1 / iRECIST Response Assessment, Dose Tracking, and Supportive Care Across 18 Cancer Types and 60+ Regimens},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc012-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against KEYNOTE-189 (Gandhi 2018 pembro+chemo NSCLC), IMpower133 (Horn 2018 atezolizumab+EP SCLC), CASPIAN (Paz-Ares 2019 durvalumab+EP), FLOT-AIO4 (Al-Batran 2019 perioperative gastric), PRODIGE-4 ACCORD-11 (Conroy 2011 FOLFIRINOX pancreatic), MM-VRd (Durie 2017 VRd vs Rd multiple myeloma), CHOP era (Coiffier 2002 R-CHOP DLBCL), POLARIX (Tilly 2022 Pola-R-CHP), BEP era (Williams 1987 bleomycin+etoposide+platinum testicular germ cell), CleopAtrA (Swain 2020 pertuzumab breast), MONARCH-2 (Sledge 2017 abemaciclib breast), KEYNOTE-590 (Sun 2021 pembro esophagogastric), NORDIC-VII (Tveit 2012 FLOX), AIO XELOX-1 (Schmoll 2007), GeparQuinto (von Minckwitz 2014 pCR), CALGB 49907 (Muss 2009), CTCAE v5.0 (NCI 2017), RECIST 1.1 (Eisenhauer 2009), iRECIST (Seymour 2017).}
}

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Contact

• Email: pradeep@xpertsystems.ai
• Web: https://xpertsystems.ai
• Vertical: Healthcare / Oncology / Chemotherapy / Pan-Cancer
• SKU catalog: SKU 12 of the Oncology vertical (22 SKUs total across Cardiology + Oncology); ~87 SKUs across 8 verticals

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