xpertsystems/hconc009-sample

HC-ONC-009 — Melanoma Synthetic Cohort

Sample dataset (500-patient primary cohort, single-table) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 9

A fully synthetic melanoma cohort spanning the six WHO subtypes (Superficial Spreading SSM ~70%, Nodular NM ~15%, Lentigo Maligna LMM ~5%, Acral Lentiginous ALM ~5%, Uveal UM ~3%, Desmoplastic DM ~2%) with AJCC 8th Edition TNM staging computed from Breslow thickness + ulceration + mitotic rate, Sentinel Lymph Node Biopsy (SLNB) outcomes with T-stage-driven positivity rates, comprehensive genomic profiling (BRAF V600E / V600K / V600R / Non-V600, NRAS, KIT, NF1, PTEN, CDKN2A, GNAQ/GNA11 for uveal melanoma, TMB, PD-L1 CPS, ctDNA VAF, MSI), NCCN-compliant surgical resection with margin status and complete lymph node dissection, modern immunotherapy (Pembrolizumab/Nivolumab mono, Ipilimumab+Nivolumab combo, Relatlimab+Nivolumab anti-LAG-3, Atezolizumab+Cobimetinib+Vemurafenib triplet, Ipilimumab mono, TIL therapy) with KEYNOTE-006/CheckMate-067/RELATIVITY-047-anchored ORR, CR, and median PFS, comprehensive immune-related adverse event (irAE) profiling (colitis, pneumonitis, hypophysitis, thyroiditis, hepatitis, skin, neurologic, renal, cardiac) with grade-3/4 rates, BRAF/MEK targeted therapy (Dabrafenib+Trametinib, Vemurafenib+ Cobimetinib, Encorafenib+Binimetinib) with COMBI-d/v-anchored PFS and acquired resistance mechanisms (NRAS-mut, MEK1-mut, BRAF amplification, KRAS-mut, COT1-amp, PDGFRA, epigenetic), paradoxical MAPK activation (cutaneous squamous cell carcinoma from vemurafenib), metastasis organ tropism (lung 40%, liver 25%, brain in M1d, bone in M1c, GI, adrenal, soft tissue, distant lymph), adjuvant radiation + ICI, and Weibull-calibrated survival endpoints.

Built to be drop-in usable for melanoma analytics, modeling, demos, and education while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk.

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At a glance

SKU	HC-ONC-009
Vertical	Healthcare → Oncology / Dermatology (SKU 9)
Tables	1 (primary cohort; no longitudinal panel)
Sample size	500-patient primary × 105 columns
Standards	AJCC 8th Edition TNM, NCCN Melanoma 2024, MSLT-II SLNB, RECIST 1.1, CTCAE for irAE
Subtypes	SSM + NM + LMM + ALM + UM + DM (6 subtypes)
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

Melanoma is biologically heterogeneous with five strikingly different subtypes (cutaneous SSM/NM/LMM/ALM/DM vs uveal UM) driven by different molecular pathways, different anatomic sites, and different prognoses. Real-world datasets either pool subtypes (losing biology) or focus on one subtype (limiting generalization). This synthetic cohort gives you all six subtypes in one schema with realistic subtype-specific dependencies:

• ✅ UM ↔ GNAQ/GNA11 pathognomonic (Van Raamsdonk 2009) — UM never has BRAF V600 (0 violations); GNAQ/GNA11 never appears outside UM
• ✅ BRAF V600 in cutaneous ~60% (Davies 2002, Hodis 2012)
• ✅ Targeted therapy ⊂ BRAF V600+ (0 violations) — only BRAF V600+ patients get Dabrafenib/Vemurafenib/Encorafenib
• ✅ NCCN-concordant ICI gating — ICI excluded from stage IA/IB/IIA (0 violations); stage IIB+ eligible
• ✅ DREAMseq sequencing strategy — Stage IV BRAF+ patients: 60% ICI first, 40% targeted first (Atkins 2023)
• ✅ AJCC 8th Edition stage derived from T/N/M — Breslow thickness + ulceration + mitotic rate drive T-stage; SLNB results drive N-stage; metastasis flag drives M-stage
• ✅ SLNB+ monotonic in T-stage — T1a 0%, T1b 6%, T2a 10%, T3a 24%, T4b 44% (Morton 2014, MSLT-II)
• ✅ Paradoxical MAPK in vemurafenib — cutaneous SCC arises in ~20% of Vemurafenib mono, ~7% of Vemurafenib+Cobimetinib (0 leak to other regimens)
• ✅ Brain mets ⊂ M1d (structural) — only M1d patients can have brain involvement (AJCC 8th Edition convention)
• ✅ CheckMate-067-anchored irAE — Ipi+Nivo G3-4 irAE ~59% (cohort 45-65%)
• ✅ KEYNOTE-006-anchored response — Pembrolizumab ORR ~45% (cohort ~50-58% with TMB/PDL1 boost)
• ✅ Hyperprogression flag restricted to ICI-treated PD patients (Champiat 2017)
• ✅ Re-excision ⊂ R1/R2 margin (structural)
• ✅ cuSCC ⊂ vemurafenib regimens (paradoxical MAPK)
• ✅ TIL therapy included as a treatment option (Lifileucel 2024 FDA approval)

Coverage spans:

• WHO melanoma subtypes — SSM, NM, LMM, ALM, UM, DM with subtype- specific age, primary site, and Fitzpatrick skin type distributions
• AJCC 8th Edition — T1a/T1b/T2a/T2b/T3a/T3b/T4a/T4b (Breslow + ulceration), N0/N1a/N1b/N1c/N2a/N2b/N2c/N3a/N3b/N3c (LN positive count + in-transit
  • macro/micro metastasis), M0/M1a/M1b/M1c/M1d (with LDH-elevated "_1" modifier), overall stage IA/IB/IIA/IIB/IIC/IIIA/IIIB/IIIC/IIID/IV
• SLNB outcomes — performed flag, micro/macro metastasis, positive flag, positive node count, in-transit mets
• Genomics — BRAF (V600E/V600K/V600R/Non-V600/WT), NRAS, KIT, NF1, PTEN, CDKN2A, GNAQ/GNA11 (UM), TMB + TMB-high flag, PD-L1 CPS, ctDNA VAF, MSI
• Surgery — wide local excision / amputation, NCCN-margin (1.0/1.5/2.0 cm by T-stage), R0/R1/R2 status, margin-to-tumor distance, CLND flag, reconstruction (primary closure/skin graft/flap), wound complications, re-excision flag
• Immunotherapy — 7 regimens (Pembro/Nivo mono, Ipi+Nivo, Rela+Nivo, Atezo+Cobi+Vemu triplet, Ipi mono, TIL), line of therapy, cycles, ORR/CR (with TMB/PDL1 boost, PTEN-loss penalty), Weibull PFS, irAE any-grade + G3-4 + organ (8 systems), specific irAE flags (colitis, pneumonitis, hypophysitis, thyroiditis), steroid use, discontinuation, hyperprogression
• Targeted therapy — 5 regimens (Dab+Tram, Vemu+Cobi, Enco+Bini, Vemu mono, Dab mono), combo flag, ORR, Weibull PFS, acquired resistance (NRAS/MEK1/ BRAF-amp/KRAS/COT1/PDGFRA/epigenetic), cuSCC paradoxical
• Metastasis — 8 organ sites (lung/liver/brain/bone/GI/adrenal/soft-tissue/ distant-lymph), metastasis count, oligometastatic flag, brain met count + size, visceral flag, time-to-first-metastasis
• Radiation + Adjuvant — radiation flag + site (Brain_SRS/Nodal_Basin), adjuvant ICI flag + regimen, intralesional therapy (T-Vec)
• Survival — OS + event flag, RFS + event flag, duration of response, time-to-next-treatment, cause of death (Melanoma/Treatment_Toxicity/ Non_Cancer/Censored)

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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	54.4 yr	50–62	SEER ~64; cohort younger (subtype-driven)
Male %	57.8%	48–65	SEER ~55-60%
SSM %	71.0%	62–78	Cohort design ~70%
UM %	2.6%	1–7	~3% of melanoma
ALM %	5.2%	2–8	~5% of melanoma
Stage IA %	17.0%	10–22	SEER 35% (cohort drift disclosed)
Stage IV %	10.0%	6–14	SEER 11%
BRAF V600 overall	56.2%	48–65	Mixed cohort
BRAF V600 in cutaneous	58.9%	52–68	Davies 2002 ~50-60%
BRAF V600 in UM	0%	≥100% (no-violations)	Structural
NRAS overall	11.2%	6–14	BRAF-NRAS mutex drops from ~20% to ~10%
KIT in ALM	26.9%	12–38	Curtin 2006 ~10-20%
GNAQ in UM	76.9%	20–80	Van Raamsdonk 2009 ~45%; wide variance
TMB-high %	70.4%	58–75	Hayward 2017 ~60-70%
TMB median	14.8 mut/Mb	10–18	Cutaneous ~15
R0 margin	95.2%	88–98	NCCN-compliant ~94%
ICI uptake	28.2%	22–38	Stage IIB+ × 65%
Pembro ORR	58.2%	40–65	KEYNOTE-006 45% + TMB boost
Ipi+Nivo ORR	50.0%	15–100	CheckMate-067 58%; wide variance at small n
Ipi+Nivo G3-4 irAE	50.0%	32–75	CheckMate-067 59%
Targeted ORR	69.4%	50–78	COMBI-d/v ~68%
Dab+Tram ORR	80.0%	50–85	COMBI-d 68%
OS median overall	112.4 mo	95–140	Stage I-heavy cohort
OS median Stage IV	24.3 mo	12–32	CheckMate 067 mOS 36mo, real-world ~24mo
Lung met Stage IV	36.0%	28–55	Patel 1978, Damsky 2014
Brain met Stage IV	18.0%	8–30	M1d × 70%
Liver met Stage IV	24.0%	10–42	~25%
UM excludes BRAF V600	100%	≥100% (floor)	Structural
GNAQ/GNA11 ⊂ UM	100%	≥100% (floor)	Structural
Targeted ⊂ BRAF V600+	100%	≥100% (floor)	Structural
ICI ⊄ early stage	100%	≥100% (floor)	NCCN
Stage IV ↔ M1	100%	≥100% (floor)	Structural
Brain mets ⊂ M1d	100%	≥100% (floor)	AJCC convention
cuSCC ⊂ vemurafenib	100%	≥100% (floor)	Paradoxical MAPK
Re-excision ⊂ R1/R2	100%	≥100% (floor)	Structural
Hyperprogression ⊂ ICI-PD	100%	≥100% (floor)	Champiat 2017

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

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Files in this sample

hconc009_sample/
├── hconc009_sample.csv                # 500 patients × 105 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 or subset extraction in this sample (the generator produces a single comprehensive flat table).

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Schema highlights (105 columns in primary cohort across 9 modules)

Demographics (12 cols)

patient_id, diagnosis_year, age_at_diagnosis, sex, fitzpatrick_skin_type (1-6), melanoma_subtype (SSM/NM/LMM/ALM/UM/DM), primary_site, family_history_melanoma_flag, prior_melanoma_flag, uv_exposure_index, tanning_bed_use_flag, immunosuppression_flag, ecog_performance_status

Staging (14 cols)

breslow_thickness_mm, ulceration_flag, mitotic_rate_per_mm2, t_stage (T1a-T4b), slnb_performed_flag, slnb_result, slnb_positive_flag, lymph_node_count_positive, in_transit_metastasis_flag, n_stage (N0-N3c), m_stage (M0/M1a-d with _1 LDH modifier), ldh_uln_ratio, overall_ajcc_stage, distant_metastasis_flag

Genomics (14 cols)

braf_mutation_status (V600E/V600K/V600R/Non_V600/WT), braf_v600_flag, nras_mutation_flag, kit_mutation_flag, nf1_mutation_flag, pten_loss_flag, cdkn2a_loss_flag, gnaq_mutation_flag, gna11_mutation_flag, tmb_mutations_per_mb, tmb_high_flag, pd_l1_cps, ctdna_vaf_pct, msi_status

Surgery (9 cols)

surgery_type, excision_margin_cm, surgical_margin_status (R0/R1/R2), margin_to_tumor_distance_mm, complete_lymph_node_dissection_flag, reconstruction_type, wound_complication_flag, time_to_surgery_days, re_excision_flag

Immunotherapy (16 cols)

immunotherapy_flag, ici_regimen (7 options), ici_line (1L/2L/3L+), ici_cycles_administered, best_overall_response_ici (CR/PR/SD/PD), ici_pfs_months, ici_pfs_event_flag, irAE_any_grade_flag, irAE_grade_3_4_flag, irAE_organ_system (8 systems), irAE_colitis_flag, irAE_pneumonitis_flag, irAE_hypophysitis_flag, irAE_thyroid_flag, immunotherapy_discontinuation_flag, steroid_for_irAE_flag, hyperprogression_flag

Targeted Therapy (9 cols)

targeted_therapy_flag, targeted_regimen (5 options), combo_braf_mek_flag, best_overall_response_targeted, targeted_pfs_months, targeted_pfs_event_flag, acquired_resistance_flag, resistance_mechanism (8 mechanisms), cutaneous_squamous_cell_ca_flag (paradoxical MAPK)

Metastasis (14 cols)

metastasis_site_lung, metastasis_site_liver, metastasis_site_brain, metastasis_site_bone, metastasis_site_gi, metastasis_site_adrenal, metastasis_site_soft_tissue, metastasis_site_distant_lymph, metastasis_count, oligometastatic_flag, brain_metastasis_count, brain_metastasis_size_max_cm, visceral_metastasis_flag, time_to_first_metastasis_months

Radiation + Adjuvant (5 cols)

radiation_flag, radiation_site, adjuvant_ici_flag, adjuvant_regimen, intralesional_therapy_flag

Survival (7 cols)

overall_survival_months, os_event_flag, recurrence_free_survival_months, rfs_event_flag, duration_of_response_months, time_to_next_treatment_months, cause_of_death (Melanoma/Treatment_Toxicity/Non_Cancer/Censored)

Metadata (3 cols)

sku, generator_version, seed

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Use cases

1. Subtype-stratified survival modeling — Cox PH on OS by subtype with BRAF V600/NRAS/KIT as molecular covariates.
2. AJCC 8th Edition stage prediction — predict overall stage from T/N/M components.
3. SLNB+ prediction — predict sentinel node positivity from Breslow, ulceration, mitotic rate, age, sex.
4. ICI response prediction — predict ORR/CR from TMB, PD-L1, PTEN loss, BRAF status (KEYNOTE-006/CheckMate-067).
5. irAE risk stratification — predict G3-4 irAE from regimen and patient features.
6. DREAMseq sequencing audit — measure ICI-first vs targeted-first uptake in Stage IV BRAF V600+.
7. Brain metastasis prediction — predict brain involvement in Stage IV patients (M1d gating).
8. Acquired resistance modeling — predict mechanism of MEK/BRAF resistance from time-on-treatment and patient features.
9. NCCN guideline-concordance — measure adherence to margin guidelines, CLND post-MSLT-II, RT in stage IIIC/D.
10. Teaching & training — dermatology, medical oncology fellows, ML-for-healthcare bootcamps on solid tumor with rich molecular
    • treatment + outcomes detail.

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Loading examples

pandas

import pandas as pd
df = pd.read_csv("hconc009_sample.csv")
print(df.shape)        # (500, 105)
print(df["melanoma_subtype"].value_counts())
print(df["overall_ajcc_stage"].value_counts().sort_index())

Hugging Face datasets

from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc009-sample")
df = ds["train"].to_pandas()

Subtype-stratified survival curves

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

kmf = KaplanMeierFitter()
for subtype in ["SSM", "NM", "ALM", "UM"]:
    sub = df[df["melanoma_subtype"] == subtype]
    if len(sub) < 5: continue
    kmf.fit(sub["overall_survival_months"], event_observed=sub["os_event_flag"], label=subtype)
    kmf.plot_survival_function()
plt.title("Melanoma OS by Subtype"); plt.show()

BRAF V600 vs WT survival in Stage IV

stage_iv = df[df["overall_ajcc_stage"] == "IV"].copy()
for braf, label in [(1, "BRAF V600+"), (0, "BRAF WT/Non-V600")]:
    sub = stage_iv[stage_iv["braf_v600_flag"] == braf]
    print(f"{label}: n={len(sub)}, mOS={sub['overall_survival_months'].median():.1f} mo, "
          f"ICI uptake={sub['immunotherapy_flag'].mean():.1%}, "
          f"Targeted uptake={sub['targeted_therapy_flag'].mean():.1%}")

ICI regimen comparison

ici = df[df["immunotherapy_flag"] == 1]
comparison = ici.groupby("ici_regimen").agg(
    n=("patient_id", "count"),
    orr=("best_overall_response_ici", lambda s: s.isin(["CR","PR"]).mean()),
    cr_rate=("best_overall_response_ici", lambda s: (s == "CR").mean()),
    irae_g34_rate=("irAE_grade_3_4_flag", "mean"),
    median_pfs=("ici_pfs_months", "median"),
).round(3)
print(comparison)

SLNB+ rate by T-stage (Morton 2014)

t_order = ["T1a","T1b","T2a","T2b","T3a","T3b","T4a","T4b"]
for t in t_order:
    sub = df[df["t_stage"] == t]
    if len(sub) >= 5:
        rate = sub["slnb_positive_flag"].mean()
        print(f"{t}: SLNB+ = {rate:.1%} (n={len(sub)})")

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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. Stage distribution drift vs SEER. The generator has a published STAGE_DISTRIBUTION table calibrated to SEER 2015-2019 (IA: 35%, IB: 16%, IIA: 8%, ...), but it ONLY uses this to draw a metastatic_flag (line 343). The actual overall_ajcc_stage is computed downstream from T/N/M via the map_overall_stage() function (line 365), where T-stage comes from Breslow thickness lognormal (centered at 1.2mm for non-NM subtypes), and N-stage comes from SLNB results. Result: cohort Stage IA is ~16% (vs SEER 35%), Stage IIA is ~25% (vs SEER 8%), Stage IIIA is ~1% (vs SEER 6%). For analyses requiring SEER-matching stage distribution, re-weight by stage or use the full commercial product which corrects this.

2. BRAF V600 + NRAS co-occurrence ~0.6% (3/500 patients at seed 42). Real-world data shows BRAF V600 and NRAS are nearly mutually exclusive (Hodis 2012 ~1% co-mutation). Generator allows 1% baseline NRAS rate even in BRAF V600+ patients (line 403: nras_base_prob = np.where(braf_v600_flag == 1, 0.01, 0.20)). The 0.6% observed is consistent with this design but inflates mutual exclusion.

3. NRAS rate ~9-11% (vs literature ~20%). Because NRAS is gated to ~1% when BRAF V600+ (and BRAF V600+ is ~55-60% of cohort), the effective overall NRAS rate is ~10%, below the published ~20%. For NRAS-focused modeling, treat as relative ordering, not absolute rate.

4. Brain metastasis structurally gated to M1d only (line 720). Real- world brain mets can occur in M1a/b/c patients too (often as progression). Cohort enforces strict M-stage → organ tropism mapping, which may not generalize to real-world metastatic pathways.

5. Bone metastasis structurally gated to M1c only (line 722). Same structural restriction.

6. Soft tissue metastasis structurally gated to M1a only (line 726). Same structural restriction.

7. OS median for Stage IV ~24mo — within real-world range (CheckMate 067 modern era mOS ~36mo; historical Stage IV ~6-9mo). Cohort represents a modern-era mixed population including some responders to immunotherapy.

8. DREAMseq sequencing assumed 60/40 split in Stage IV BRAF+ between ICI-first and targeted-first (line 538). DREAMseq actually showed superior OS with ICI-first, so real-world post-2022 split is shifting toward ICI-first majority.

9. Hyperprogression rate ~7% in ICI-PD patients. Real-world incidence is debated (Champiat 2017 ~10%, Kato 2017 ~13%). Cohort matches lower- end estimate.

10. response_draw cascading logic at line 575-577 — the CR cutoff is at base_cr (which is the WHOLE CR rate, not conditional given eligible), then PR cutoff at adjusted_orr. This produces correct overall ORR but the conditional probabilities of CR vs PR are mechanically constrained.

11. TMB-high boost (+8%) and PD-L1 boost (+5%) applied uniformly to all ICI regimens (line 563-564). Real-world predictive biomarker effects are regimen-specific (TMB-high less informative for Ipi+Nivo than for pembro mono).

12. Sequential patient_id ("MEL-NNNNNNN") rather than UUID. Easier to debug but trivially predictable. Use UUID conversion if needed for anonymization workflows.

13. No longitudinal panel. Unlike other catalog SKUs (HCONC003 PSA, HCONC006 AFP, HCONC008 PET), this generator produces a single flat table. For longitudinal modeling, request the full commercial product which includes optional response trajectory panels.

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	25,000+ (configurable)
Tables	1 (flat)	Optional longitudinal response panel
Stage distribution	T/N/M-derived (drift disclosed)	SEER-matched calibration
BRAF/NRAS mutex	1% co-occurrence allowed	Strict mutual exclusion option
NRAS rate	~10% (cohort)	Configurable to ~20% (literature)
Brain/bone/soft-tissue gating	Strict M-stage mapping	Configurable multi-organ tropism
Patient ID format	Sequential (MEL-NNNNNNN)	UUID 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 / NCDB / IO-NETWORK
Support	Community	Email / SLA

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Citation

@dataset{xpertsystems_hconc009_2026,
  title  = {HC-ONC-009: Melanoma Synthetic Cohort with AJCC 8th Edition Staging, BRAF/NRAS/KIT/GNAQ Genomic Profiling, KEYNOTE-006 / CheckMate-067 / RELATIVITY-047 Immunotherapy, COMBI-d/v Targeted Therapy, and Organ-Specific Metastasis Tropism},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc009-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against KEYNOTE-006 (Robert 2015, Schachter 2017 pembrolizumab), CheckMate 067 (Larkin 2015, Wolchok 2022 ipilimumab+nivolumab), RELATIVITY-047 (Tawbi 2022 relatlimab+nivolumab anti-LAG3), COMBI-d (Long 2014 dabrafenib+trametinib), COMBI-v (Robert 2015), COLUMBUS (Dummer 2018 encorafenib+binimetinib), coBRIM (Larkin 2014 vemurafenib+cobimetinib), DREAMseq EA6134 (Atkins 2023 sequencing in BRAF+ Stage IV), AJCC 8th Edition Melanoma Staging (Gershenwald 2017), MSLT-II (Faries 2017 SLNB CLND), SEER 2015-2019 incidence/staging, Davies 2002 (BRAF in melanoma), Hodis 2012 (TCGA melanoma genomics), Van Raamsdonk 2009 (GNAQ in uveal), Curtin 2006 (KIT in acral), Hayward 2017 (cutaneous melanoma TMB), Champiat 2017 (hyperprogression on ICI), Lifileucel TIL therapy 2024 FDA approval.}
}

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Contact

• Email: pradeep@xpertsystems.ai
• Web: https://xpertsystems.ai
• Vertical: Healthcare / Oncology / Dermatology
• SKU catalog: SKU 9 of the Oncology vertical (19 SKUs total across Cardiology + Oncology); ~84 SKUs across 8 verticals

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