Accepted for/Published in: JMIR AI
Date Submitted: May 23, 2025
Date Accepted: Jan 29, 2026
Date Submitted to PubMed: Feb 4, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Augmenting LLM with Prompt Engineering and Supervised Fine-Tuning in NSCLC TNM Staging: Framework Development and Validation
ABSTRACT
Background:
Non-small cell lung cancer (NSCLC) staging is crucial for treatment decisions and prognosis. Given the complex staging criteria, innovative methods are needed to assist physicians.
Objective:
This study aims to develop a robust solution for TNM staging using the GLM-4-Air model.
Methods:
A dataset of de-identified real-world medical imaging reports annotated by senior physicians from 3A-Level hospitals in China was constructed. Based on this dataset, a combined strategy of prompt optimization and Low-Rank Adaptation (LoRA) fine-tuning was developed to enhance the foundational model GLM-4-Air.
Results:
Results showed considerable improvement, with T, N, M, and clinical staging accuracies reaching 92%, 86%, 92%, and 90%, respectively. In comparison, GPT-4o achieved accuracies of 87%, 70%, 78%, and 80% under the same conditions.
Conclusions:
In this study, our model shows superior performance in NSCLC TNM staging. This underscored the effectiveness of our approach and highlighted its potential to enhance diagnostic standardization.
Citation
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