Large Language Model Ensemble for Automated TNM Staging from Radiology Reports

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ID: 320958
2026
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Abstract
Abstract Motivation Accurate TNM staging from lung cancer radiology reports is crucial for treatment planning and prognosis assessment. Manual staging processes are time-consuming and subject to inter-observer variability. Large language models (LLMs) offer opportunities to automate TNM staging with enhanced interpretability and clinical reasoning. Results We developed two complementary systems for automated TNM staging from English radiology reports. System I employs GPT-4o with reasoning-based few-shot learning and multi-step voting. System II integrates multiple LLMs (GPT-4o and Gemini-2) using DSPy framework with MIPROv2 optimization. In NTCIR-18 RadNLP 2024 English main task, our approaches achieved first (joint accuracy: 0.6543) and second place (joint accuracy: 0.6296), demonstrating superior performance in T, N, and M classification with accuracies of 0.7037/0.9136/0.8889 and 0.7284/0.9383/0.8395, respectively. Availability and Implementation Source code freely available at https://github.com/nlptmu/multi-expert-tnm-staging under MIT license. An archival snapshot of the version used in this study is deposited on Zenodo at https://doi.org/10.5281/zenodo.20338561. Implemented in Python 3.12+ with PyTorch 2.6 and DSPY 3.0, supporting Linux. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7168289848 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wen-Chao Yeh, Yi-Shin Chen, Wen-Lian Hsu, Shuntaro Yada, Yung-Chun Chang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag399
URL
https://doi.org/10.1093/bioinformatics/btag399
Keywords Keywords not found

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