Augmenting LLM with Prompt Engineering and Supervised Fine-Tuning in NSCLC TNM Staging: Framework Development and Validation (Preprint)

Key Points

• Improved TNM staging accuracy is achieved through supervised fine-tuning and prompt engineering techniques.
• The framework's validation shows a marked increase in prediction reliability for non-small cell lung cancer.
• Analysis using advanced machine learning models provides a groundbreaking approach to cancer staging.
• Clinical implementation could transform traditional staging practices and enhance patient management strategies.