ArticlePatterns (New York, N.Y.)2024
TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Bridging the data latency gap: automated extraction of genomic biomarkers from unstructured clinical documents to support real-world oncology data.Cancer causes & control : CCC · 2026Article
- SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.Nature cancer · 2026Article
- Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Single cell spatial transcriptomics track the evolutionary hierarchy and microenvironment remodeling during breast carcinoma invasion.Nature communications · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Evaluating agentic AI for biological discovery in autonomous and copilot settings.bioRxiv : the preprint server for biology · 2026Article
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- ATRX loss in sarcomas is associated with dysregulated gene and transposable element expression, loss of DNA methylation, and worse survival.ESMO rare cancers · 2026Article
- SimSUM - simulated benchmark with structured and unstructured medical records.Journal of biomedical semantics · 2025Article
- Clinically aligned multi-modal image-text model for pan-cancer prognosis prediction.BioData mining · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.Diagnostics (Basel, Switzerland) · 2025Article
- Cancer type, stage and prognosis assessment from pathology reports using LLMs.Scientific reports · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- Iterative refinement and goal articulation to optimize large language models for clinical information extraction.NPJ digital medicine · 2025Article
- The DRAGON benchmark for clinical NLP.NPJ digital medicine · 2025Article
- scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.Briefings in bioinformatics · 2025Article
- Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports.medRxiv : the preprint server for health sciences · 2025Article
- Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. Finally, we perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.