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We propose a novel prompt engineering approach called Fine-Grained Prompting (FGP) for TNM staging to enhance the performance of large language models for extracting and classifying TNM staging information from radiology reports. FGP divides the TNM staging definitions into subtasks and integrates their responses to predict the TNM stage by shortening prompts and simplifying tasks. FGP demonstrates a superior performance compared to basic prompt engineering, showing an 18.5% improvement in T accuracy for lung cancer. Furthermore, an evaluation of clinician TNM staging time for lung cancer using an application software based on FGP results showed that the time efficiency more than doubled compared to standard manual processes. These findings highlight the potential of FGP to address existing challenges and set a new standard for AI-assisted cancer staging, ultimately enhancing clinical efficiency and patient outcomes.