ArticleNature communications2021
Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Clinical Trial Notifications Triggered by Artificial Intelligence-Detected Cancer Progression: A Randomized Trial.JAMA network open · 2025Trial
- Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study.Scientific reports · 2024Trial
- Does WHO/ISUP grade IV histology provide prognostic insight in chromophobe renal cell carcinoma?International urology and nephrology · 2026Article
- Review
- Leveraging tumor dynamics to discover mutations influencing progression and treatment response for precision oncology.Genome medicine · 2026Article
- Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Machine learning-based models for tumor mutation burden prediction in gastrointestinal cancers: a systematic review and meta-analysis.Discover oncology · 2026Review
- TNM-Accountable Whole-Body 3-Dimensional Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Report Drafting in Lung Cancer Cohorts via Structured Impressions and Organ-wise Exemplar Synthesis.Research (Washington, D.C.) · 2026Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Article
- Empirical evaluation of artificial intelligence distillation techniques for ascertaining cancer outcomes from electronic health records.NPJ digital medicine · 2025Article
- Collaborating across sectors in service of open science, precision oncology, and patients: an overview of the AACR Project GENIE (Genomics Evidence Neoplasia Information Exchange) Biopharma Collaborative (BPC).ESMO real world data and digital oncology · 2025Review
- Tumor-Agnostic Therapies in Practice: Challenges, Innovations, and Future Perspectives.Cancers · 2025Review
- Article
- Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.Nature communications · 2024Article
- Predictors of response to CDK4/6i retrial after prior CDK4/6i failure in ER+ metastatic breast cancer.NPJ breast cancer · 2024Article
- Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Predictors of Response to CDK4/6i Retrial After Prior CDK4/6i Failure in ER+ Metastatic Breast Cancer.Research square · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.
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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.