ArticleNPJ precision oncology2025
Real-world application of large language models for automated TNM staging using unstructured gynecologic oncology reports.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed.
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Article
- Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer types.medRxiv : the preprint server for health sciences · 2026Article
- Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual data entry in cancer registries is both time-consuming and prone to error. Although large language models (LLMs) offer promising solutions, prior studies have frequently relied on preprocessed datasets or required complex fine-tuning, limiting their applicability in clinical settings. Here, we assessed the performance of out-of-the-box LLMs on TNM classification tasks using only prompt engineering, without data anonymization or model fine-tuning. We identified manual registry error rates of 5.5-17.0% in a real-world gynecologic cancer registry. Both a cloud-based LLM (Gemini 1.5; T- and N-stage accuracy: 0.994 and 0.993, respectively) and the top-performing local model (Qwen2.5 72B; T- and N-stage accuracy: 0.971 and 0.923, respectively) outperformed existing manual entries in extracting pathological T and N classifications. These models also achieved accuracies of 0.909 and 0.895 in clinical M classification, respectively. Our approach reflects real-world clinical workflows and offers a practical solution for enhancing data integrity in clinical registries using LLMs.
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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.