ranjithraj/cancer-knowledge-base

Cancer Knowledge Base — the open, verified oncology KB for RAG & LLM evaluation

The only open CC-BY-4.0 oncology knowledge base that combines:

• 110/110 trials cited with PMID + NCT + PubMed/ClinicalTrials.gov URLs, and 32 prognosis rows linked to verified SEER 2016–2022 references — no LLM-synthetic dataset has this.
• A provable 152-question MCQ benchmark — every answer derives from this KB's own structured data and carries a citation + golden docs, so it is open-book verifiable (unlike exam-based medical benchmarks).
• An oncology RAG evaluation suite — 1,239-chunk retrieval pool, 456 MedRGB-style sufficiency / noise / counterfactual-robustness variants, MIRAGE/MedRGB interchange exports, a dependency-free harness (eval_mcq.py), and a submission package.
• A ground-truth validation layer that independently audits LLM-synthetic oncology datasets against the KB's cited statements — published scorecards for CancerGUIDE, OncoBench, and MedGUIDE-MCQA-8K.
• Universal TNM / staging + sub-stages (65 staging rows) and ICD-10 / ICD-O-3 / LOINC / HGNC interop codes for joins into precision-oncology pipelines.
• Localization with provenance: 9 locales, 4,728 glossary terms, ≥60% KB-wide translatable coverage for every language, and 20 reviewed-as-auto translation rows.

Educational reference only — not medical advice. Fully de-identified by construction.

Quick start

import pandas as pd

documents = pd.read_parquet("hf://datasets/ranjithraj/cancer-knowledge-base/documents.parquet")
guidelines = pd.read_parquet("hf://datasets/ranjithraj/cancer-knowledge-base/guidelines.parquet")
trials = pd.read_parquet("hf://datasets/ranjithraj/cancer-knowledge-base/evidence_levels.parquet")

# Full-text search any document (FTS is a first-class column)
breast = documents[documents.path == "know/breast-ref.md"].body.iloc[0]

# Every guideline statement, citation-linked
print(guidelines[guidelines.source == "know/breast-ref.md"])

# 152 provable MCQs with difficulty / evidence / citation / golden docs
mcq = pd.read_parquet("hf://datasets/ranjithraj/cancer-knowledge-base/mcq_questions.parquet")

Evaluate your own RAG/LLM on the oncology benchmark in one command:

python3 scripts/eval_mcq.py --retriever bm25 --topk 3   # BM25 end-to-end
python3 scripts/eval_mcq.py --predictions preds.jsonl   # score your system

A 3-minute Colab walkthrough is at colab_quickstart.ipynb — load the KB, run BM25 retrieval, and reproduce the benchmark baseline.

Suggested uses

• RAG evaluation — score your retriever+generator on benchmark/ (MIRAGE/MedRGB interchangeable).
• Fine-tuning — instruction-tune or domain-adapt small LLMs for oncology QA on the 58 de-identified documents.
• Benchmarking synthetic data — screen LLM-generated oncology datasets against the KB's evidence index (scripts/validate_synthetic.py).
• Multilingual oncology content — build localized Q&A or terminology tools from the code-anchored glossary (9 locales).
• Entity linking / coding — ICD-10 / LOINC / ICD-O-3 / HGNC mappings in codes.parquet.

Citation

@misc{cancer-knowledge-base-2026,
  title = {{Cancer Knowledge Base}: an open, verified, provable oncology knowledge base for RAG and LLM evaluation},
  author = {Ranjithraj, {R}},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/ranjithraj/cancer-knowledge-base}},
  note = {CC-BY-4.0; 110/110 trials cited; 152-question provable MCQ benchmark; validation scorecards for CancerGUIDE/OncoBench/MedGUIDE}
}

Contents (parquet files)

File	Rows	Description
documents.parquet	58	De-identified documents: metadata + body text
sections.parquet	402	Markdown headings + verbatim section content
markdown_tables.parquet	212	All markdown tables (header/rows as JSON)
markdown_links.parquet	154	Internal links between documents
prognosis.parquet	168	Lethality/survival tables by cancer type and stage (dated, educational)
biomarkers.parquet	72	Mutations, receptors, genetic syndromes
guidelines.parquet	175	Guideline-grade treatment statements per cancer (NCCN/ESMO/ASCO)
subtypes.parquet	110	Histologic subtypes by cancer
drugs.parquet	102	Drugs, class, mechanism, toxicity
staging.parquet	65	Staging systems + universal TNM/Stage 0-IV + sub-stages
regimens.parquet	85	Treatment regimens per case and phase
monitoring.parquet	109	Factors to monitor during chemo/targeted/surgery/RT
diagnostics.parquet	96	Recommended diagnostics per case
labs.parquet	72	Baseline labs (synthetic, clearly flagged)
imaging_findings.parquet	59	PET-CT / imaging findings (synthetic, clearly flagged)
consultations.parquet	59	Consultation plan evolution (synthetic, clearly flagged)
case_facts.parquet	218	Structured case fields (diagnosis, staging, prognosis)
evidence_levels.parquet	115	Evidence grading (guidelines + trials) per entity — 110 trials with PMID/NCT/URL
codes.parquet	82	Interop codes: ICD-10 sites/types, HGNC biomarkers, LOINC labs, ICD-O-3 breast subtypes
regimen_drugs.parquet	100	Regimen-to-drug join table
mcq_questions.parquet	152	152 provable oncology MCQs with difficulty / evidence / citation / golden docs
prognosis_sources.parquet	32	Prognosis rows linked to verified SEER references (vintage + value)
locales.parquet	9	Registered target languages (BCP-47 codes, review requirement, priority)
glossary.parquet	4728	Code-anchored terminology registry: concept -> code (ICD/LOINC/HGNC/ICD-O-3) -> local term per locale
translations.parquet	20	Per-document localized bodies with master_hash/content_hash provenance and review status

RAG Benchmark

benchmark/ contains an oncology RAG evaluation suite built from this knowledge base:

• retrieval_pool.parquet — 1,239 chunked retrieval units (sections, markdown tables, domain tables)
• mcq_benchmark.parquet — 152 multiple-choice questions with golden docs, difficulty, evidence level, and citations
• mcq_robustness.parquet — 456 MedRGB-style variants (sufficiency / noise / counterfactual-robustness)
• mirage_format.jsonl / medrgb_format.jsonl — interchange exports for MIRAGE-family leaderboards and MedRGB harnesses (see benchmark/MIRAGE_INTEGRATION.md)
• leaderboard.json + releases/*.json — recorded results with KB-commit / date / release snapshots for growth tracking
• baseline_preds.jsonl + SUBMISSION.md — reproducible baseline and submission protocol

Baseline (BM25@top3, no LLM): sufficiency 31.6% (random = 25%), retrieval@3 61.8%. Real headroom for embedding retrievers and strong generators — the pool is provably answerable, so gains are attributable to retrieval + reasoning, not answer leakage.

Ground-truth validation scorecards

scorecards/ holds the results of screening LLM-synthetic oncology datasets against the KB's cited statements (scripts/validate_synthetic.py). Each claim is tiered supported / partial / unsupported by lexical coverage of site-gated evidence, and every item links to the matching evidence chunk.

• cancerguide.all.summary.md — CancerGUIDE (316 synthetic patient notes): 238 scorable claims, 87.0% supported / 12.6% partial / 0.4% unsupported.
• oncobench.all.summary.md — OncoBench strong100 (100 adjudicated decision cases): 2,007 claims, 26.0% supported — the scorecard doubles as a KB coverage-gap map.
• medguide.all.summary.md — MedGUIDE-MCQA-8K (7,747 NCCN decision-tree MCQs): 6,952 claims, 21.2% supported.

Localization (l10n)

The KB is localized with provenance — every translation keeps its citation (via the master document), a master_hash so a change in the English source flags the locale stale, and an explicit review status (auto → reviewed → approved). All 9 registered locales are at ≥60% KB-wide translatable coverage (de/es/hi/zh ~520-525 terms, ar/bn/pt-BR/sw/te 527 terms each; 4,728 glossary terms total), measured script-agnostically across all 58 documents.

• locales.parquet — registered languages (de, zh, es, hi, ar, bn, pt-BR, sw, te)
• glossary.parquet — code-anchored terminology registry: each concept is linked to its language-neutral code (ICD-10 / ICD-O-3 / LOINC / HGNC) and maps to the local clinical term, so structured rows localize for free while codes stay the join key across languages.
• translations.parquet — localized document bodies (5 reference docs at 100% translatable coverage in de/zh/es/hi) with master_hash/content_hash and status.
• l10n/*/mcq.*.jsonl — localized MCQ exports (de/es/hi/zh) with preserved answer indices.

Coverage is not clinical approval: status='auto' rows are glossary-generated drafts that must be clinically reviewed (reviewed) before use. See l10n-moat.md and scripts/localize_pilot.py --coverage.

Privacy

This is the de-identified public subset. The real patient case (cases/specific/ — PHI) and all content derived from it are excluded. Documents are filtered by phi = 0.

Synthetic rows in labs, imaging_findings, and consultations are clearly flagged with synthetic = 1 and are illustrative public-case data — they are not real patient values.

Disclaimer

Educational reference only — not medical advice. Survival figures are population averages and may be outdated (prognosis.as_of marks their review date); consult current guidelines and clinicians for patient decisions.