Real-world application of large language models for automated TNM staging using unstructured gynecologic oncology reports.

ABSTRACT: Manual data entry in cancer registries is both time-consuming and prone to error. Although large language models (LLMs) offer promising solutions, prior studies have frequently relied on preprocessed datasets or required complex fine-tuning, limiting their applicability in clinical settings. Here, we assessed the performance of out-of-the-box LLMs on TNM classification tasks using only prompt engineering, without data anonymization or model fine-tuning. We identified manual registry error rates of 5.5-17.0% in a real-world gynecologic cancer registry. Both a cloud-based LLM (Gemini 1.5; T- and N-stage accuracy: 0.994 and 0.993, respectively) and the top-performing local model (Qwen2.5 72B; T- and N-stage accuracy: 0.971 and 0.923, respectively) outperformed existing manual entries in extra

SUBMITTER: Ishida K 

PROVIDER: S-EPMC12630577 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature