Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images.

Journal: NPJ precision oncology
Published Date: Jul 23, 2025

Abstract

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefore, this study aims to develop a deep learning model using portal venous phase CT images to accurately distinguish EGC without lymph node metastasis. This study included 3164 patients with gastric cancer (GC) who underwent radical surgery at two medical centers in China from 2006 to 2019. Moreover, 2.5D radiomic data and multi-instance learning (MIL) were novel approaches applied in this study. By basing the selection of features on 2.5D radiomic data and MIL, the ResNet101 model combined with the XGBoost model represented a satisfactory performance for diagnosing pT1N0 GC. Furthermore, the 2.5D MIL-based model demonstrated a markedly superior predictive performance compared to traditional radiomics models and clinical models. We first constructed a deep learning prediction model based on 2.5D radiomics and MIL for effectively diagnosing pT1N0 GC patients, which provides valuable information for the individualized treatment selection.

Authors

  • Jingyang He
    Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang.
  • Jingli Xu
    Department of Gastric Surgery.
  • Wujie Chen
    Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou.
  • Mengxuan Cao
    Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang.
  • Jiaqing Zhang
    From the State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science (J.Z., A.J., X.H., Y.Z., X.Q., X.T., L.L., Y.L.), Guangzhou, China; Guangdong Provincial Clinical Research Center for Ocular Diseases (J.Z., A.J., X.H., Y.Z., X.Q., X.T., L.L., Y.L.), Guangzhou, China.
  • Qing Yang
    School of Nursing, Chengdu Medical College, Chengdu, China.
  • Enze Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Ruolan Zhang
    Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang.
  • Yahang Tong
    Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Yanqiang Zhang
    Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology, Weihai 264209, China. 15732031132@163.com.
  • Chen Gao
    Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
  • Qianyu Zhao
    Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang.
  • Zhiyuan Xu
    Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13210.
  • Lijing Wang
    School of Aeronautic Science and Engineering, Beihang University, Beijing, China.
  • Xiangdong Cheng
    Department of Gastric Surgery.
  • Guoliang Zheng
    Department of Gastric Surgery, Liaoning Cancer Hospital, Shenyang, China.
  • Siwei Pan
    Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Can Hu
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals