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<p>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 extracting pathological T and N classifications. These models also achieved accuracies of 0.909 and 0.895 in clinical M classification, respectively. Our approach reflects real-world clinical workflows and offers a practical solution for enhancing data integrity in clinical registries using LLMs.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Kentaro Ishida,
  • Ryusuke Murakami,
  • Koji Yamanoi,
  • Kohei Hamada,
  • Koki Hasebe,
  • Azusa Sakurai,
  • Taito Miyamoto,
  • Rin Mizuno,
  • Mana Taki,
  • Ken Yamaguchi,
  • Junzo Hamanishi,
  • Kenichi Saito,
  • Kazumasa Kishimoto,
  • Goshiro Yamamoto,
  • Tomohiro Kuroda,
  • Masaki Mandai

摘要

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 extracting pathological T and N classifications. These models also achieved accuracies of 0.909 and 0.895 in clinical M classification, respectively. Our approach reflects real-world clinical workflows and offers a practical solution for enhancing data integrity in clinical registries using LLMs.