ArticleFrontiers in digital health2026
Evaluating large language models for automated TNM staging from PET-CT reports: a multi-cancer comparative study.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A Cross-Sectional Study of Large Language Models in Lung Cancer Information Delivery: Readability, Quality, and Patient-Centred Evaluation.Healthcare (Basel, Switzerland) · 2026Article
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6 authors.
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Abstract
Purpose: To evaluate three large language models (LLMs), including ChatGPT 5, ChatGPT 4o, and ChatGPT 3.5, in automating TNM staging from PET-CT reports across six cancer types, and to assess their clinical utility compared with junior radiologists. Materials and methods: PET-CT reports from 552 treatment-naive patients in two institutions with confirmed primary malignancies (lung, breast, liver, pancreatic, renal, and prostate cancer) were analyzed. Three ChatGPT-series LLMs and five junior radiologists independently performed TNM staging. Reference standards were established by two senior radiologists according to the 8th version of American Joint Committee on Cancer (AJCC) staging system. Performance was evaluated using accuracy rates. Intra-model agreement was assessed by repeating each model three times per report with identical prompts, and inter-model agreement was evaluated using Cohen's Results: ChatGPT 5 achieved the highest overall accuracy (82.1%, 453/552), followed by ChatGPT 4o (74.3%, 410/552), both significantly outperforming ChatGPT 3.5 (59.6%, 329/552) and junior radiologists (77.0%, 425/552; Conclusion: Among the three LLMs, ChatGPT 5 demonstrated the highest accuracy, stability, and efficiency in automated TNM staging from PET-CT reports, achieving performance comparable to or slightly exceeding junior radiologists. Its advantages in T staging and lung cancer evaluation highlight its clinical utility as a potential decision-support tool.
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