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<p>Preoperative T staging of gastric cancer is critical for therapeutic stratification, yet conventional contrast-enhanced CT interpretation shows subjectivity and inconsistent reliability. We developed GTRNet, an interpretable end-to-end deep-learning framework that classifies T1–T4 from routine CT without manual segmentation or annotation. In a retrospective multicenter study of 1792 patients, CT images underwent standardized preprocessing and the largest axial tumor slice was used for training; performance was then tested in two independent external cohorts. GTRNet achieved high discrimination (AUC 0.86–0.95) and accuracy (81–85%) in internal and external tests, surpassing radiologists. Grad-CAM heatmaps localized attention to the gastric wall and serosa. Combining a deep-learning rad-score with tumor size, differentiation and Lauren subtype, we constructed a nomogram with good calibration and higher net clinical benefit than conventional approaches. This automated and interpretable pipeline may standardize CT-based staging and support preoperative decision-making and neoadjuvant-therapy selection.</p>

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Interpretable deep learning for multicenter gastric cancer T staging from CT images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月20日

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  • Guoliang Zheng,
  • Huan Wang,
  • Xiaomiao Chai,
  • Xin Xin,
  • Fuze Li,
  • Hongfei Li,
  • Yaoyang Ban,
  • Jinshi Wang,
  • Xinhui Qi,
  • Yingjie Li,
  • Zishuo Yan,
  • Fangning Guo,
  • Zhixue Jiang,
  • Dantong Zhu,
  • Yanqiang Zhang,
  • Zhendong Zheng,
  • Xin Zhang,
  • Jing Zhang,
  • Yan Zhao

摘要

Preoperative T staging of gastric cancer is critical for therapeutic stratification, yet conventional contrast-enhanced CT interpretation shows subjectivity and inconsistent reliability. We developed GTRNet, an interpretable end-to-end deep-learning framework that classifies T1–T4 from routine CT without manual segmentation or annotation. In a retrospective multicenter study of 1792 patients, CT images underwent standardized preprocessing and the largest axial tumor slice was used for training; performance was then tested in two independent external cohorts. GTRNet achieved high discrimination (AUC 0.86–0.95) and accuracy (81–85%) in internal and external tests, surpassing radiologists. Grad-CAM heatmaps localized attention to the gastric wall and serosa. Combining a deep-learning rad-score with tumor size, differentiation and Lauren subtype, we constructed a nomogram with good calibration and higher net clinical benefit than conventional approaches. This automated and interpretable pipeline may standardize CT-based staging and support preoperative decision-making and neoadjuvant-therapy selection.