Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images
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 ga...
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-01055-9 |
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| author | Jingyang He Jingli Xu Wujie Chen Mengxuan Cao Jiaqing Zhang Qing Yang Enze Li Ruolan Zhang Yahang Tong Yanqiang Zhang Chen Gao Qianyu Zhao Zhiyuan Xu Lijing Wang Xiangdong Cheng Guoliang Zheng Siwei Pan Can Hu |
| author_facet | Jingyang He Jingli Xu Wujie Chen Mengxuan Cao Jiaqing Zhang Qing Yang Enze Li Ruolan Zhang Yahang Tong Yanqiang Zhang Chen Gao Qianyu Zhao Zhiyuan Xu Lijing Wang Xiangdong Cheng Guoliang Zheng Siwei Pan Can Hu |
| author_sort | Jingyang He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ab979a75715243fdbaea0f5b99673d2f |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-ab979a75715243fdbaea0f5b99673d2f2025-08-20T03:42:23ZengNature Portfolionpj Precision Oncology2397-768X2025-07-019111110.1038/s41698-025-01055-9Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT imagesJingyang He0Jingli Xu1Wujie Chen2Mengxuan Cao3Jiaqing Zhang4Qing Yang5Enze Li6Ruolan Zhang7Yahang Tong8Yanqiang Zhang9Chen Gao10Qianyu Zhao11Zhiyuan Xu12Lijing Wang13Xiangdong Cheng14Guoliang Zheng15Siwei Pan16Can Hu17Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesZhejiang Hospital of Traditional Chinese MedicineDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Cancer Hospital of China Medical University, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & InstituteDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesDepartment of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesAbstract 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.https://doi.org/10.1038/s41698-025-01055-9 |
| spellingShingle | Jingyang He Jingli Xu Wujie Chen Mengxuan Cao Jiaqing Zhang Qing Yang Enze Li Ruolan Zhang Yahang Tong Yanqiang Zhang Chen Gao Qianyu Zhao Zhiyuan Xu Lijing Wang Xiangdong Cheng Guoliang Zheng Siwei Pan Can Hu Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images npj Precision Oncology |
| title | Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images |
| title_full | Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images |
| title_fullStr | Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images |
| title_full_unstemmed | Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images |
| title_short | Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images |
| title_sort | development of a deep learning model for t1n0 gastric cancer diagnosis using 2 5d radiomic data in preoperative ct images |
| url | https://doi.org/10.1038/s41698-025-01055-9 |
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