This paper is in the following e-collection/theme issue:

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1001) Computer-Aided Diagnosis (255) Imaging Informatics (308) Machine Learning (2095) Clinical Cancer Research (119) Innovations in Cancer Diagnostic and Decision Support (113) Gastroenterology and Hepatology (132)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56851, first published .
Development and Validation of a Computed Tomography–Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study

Development and Validation of a Computed Tomography–Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study

Development and Validation of a Computed Tomography–Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study

Authors of this article:

Jin Tao1, 2 Author Orcid Image ;   Dan Liu3 Author Orcid Image ;   Fu-Bi Hu4 Author Orcid Image ;   Xiao Zhang5 Author Orcid Image ;   Hongkun Yin6 Author Orcid Image ;   Huiling Zhang6 Author Orcid Image ;   Kai Zhang6 Author Orcid Image ;   Zixing Huang3 Author Orcid Image ;   Kun Yang1, 2 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

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  2. Dong J, Jiang X, Ding T, Wang X. Computed tomography vs. endoscopic ultrasonography for preoperative gastric cancer Staging: A meta-analysis. Journal of Radiation Research and Applied Sciences 2025;18(4):101855 View
  3. Alsallal M, Habeeb M, Vaghela K, Malathi H, Vashisht A, Sahu P, Singh D, Al-Hussainy A, Aljanaby I, Sameer H, Athab Z, Adil M, Yaseen A, Farhood B. Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications. Abdominal Radiology 2025 View
  4. Ren G, Wang X. Progress in multi-dimensional artificial intelligence applications in CT-based gastric cancer imaging. World Chinese Journal of Digestology 2025;33(9):685 View
  5. Liu Y, Zhang X, He W, Li Y, Hu F. A CT-based 2.5D deep learning model for preoperative T-staging in gastric cancer: a retrospective multicenter study. Abdominal Radiology 2025 View
  6. Zheng G, Wang H, Chai X, Xin X, Li F, Li H, Ban Y, Wang J, Qi X, Li Y, Yan Z, Guo F, Jiang Z, Zhu D, Zhang Y, Zheng Z, Zhang X, Zhang J, Zhao Y. Interpretable deep learning for multicenter gastric cancer T staging from CT images. npj Digital Medicine 2025 View