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Background <p>Clinical Tumor, Node, and Metastasis (cTNM) classification is vital for predicting treatment efficacy and prognosis in patients with cancer; however, its utilization in real-world data (RWD) research is hindered by its frequent storage as free text. While chest CT reports include essential information for labeling cT, the extraction process is typically manual and time-consuming.</p> Methods <p>We developed a natural language processing (NLP) system capable of labeling clinical T (cT) classifications from chest computed tomography reports. This NLP system employs a combination of deep learning and rule-based algorithms, adhering to the 9th Edition of the TNM Classification for Lung Cancer. The system initially identifies reports containing sufficient information for cT classification and subsequently assigns the appropriate cT substage.</p> Results <p>In our training and test sets of 284 and 165 reports, 83% and 85% contained sufficient information for labeling cT substage, respectively. The NLP system demonstrated effective extraction capabilities, achieving weighted F1 scores of 0.96 (0.94–0.98) and 0.93 (0.89–0.97) for the training and test sets, respectively. Among reports accurately predicted to contain adequate information, the cT substage classification achieved weighted F1 scores of 0.97 (0.94–0.99) and 0.95 (0.92–0.99) for the training and test sets, respectively.</p> Conclusions <p>This system significantly alleviates annotation costs and enhances the feasibility of utilizing cT data from RWD.</p>

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Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Nobuo Nishijima,
  • Kento Sugimoto,
  • Shozo Konishi,
  • Shoya Wada,
  • Haruhiko Hirata,
  • Masahiro Yanagawa,
  • Takahiro Tsuboyama,
  • Ayumi Fujii,
  • Taizo Murata,
  • Naoki Mihara,
  • Katsuki Okada,
  • Yasushi Matsumura,
  • Noriyuki Tomiyama,
  • Toshihiro Takeda

摘要

Background

Clinical Tumor, Node, and Metastasis (cTNM) classification is vital for predicting treatment efficacy and prognosis in patients with cancer; however, its utilization in real-world data (RWD) research is hindered by its frequent storage as free text. While chest CT reports include essential information for labeling cT, the extraction process is typically manual and time-consuming.

Methods

We developed a natural language processing (NLP) system capable of labeling clinical T (cT) classifications from chest computed tomography reports. This NLP system employs a combination of deep learning and rule-based algorithms, adhering to the 9th Edition of the TNM Classification for Lung Cancer. The system initially identifies reports containing sufficient information for cT classification and subsequently assigns the appropriate cT substage.

Results

In our training and test sets of 284 and 165 reports, 83% and 85% contained sufficient information for labeling cT substage, respectively. The NLP system demonstrated effective extraction capabilities, achieving weighted F1 scores of 0.96 (0.94–0.98) and 0.93 (0.89–0.97) for the training and test sets, respectively. Among reports accurately predicted to contain adequate information, the cT substage classification achieved weighted F1 scores of 0.97 (0.94–0.99) and 0.95 (0.92–0.99) for the training and test sets, respectively.

Conclusions

This system significantly alleviates annotation costs and enhances the feasibility of utilizing cT data from RWD.