Development and validation of models to predict serosal invasion in advanced gastric cancer using the enhanced CT imaging-based radiomics features and clinical features(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第12期
- Page:
- 1518-1522
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Development and validation of models to predict serosal invasion in advanced gastric cancer using the enhanced CT imaging-based radiomics features and clinical features
- Author(s):
- WAN Cuixia1; 3; CHEN Xiangguang3; YANG Zhiqi3; DONG Ting4; ZHANG Sheng3; JIANG Guihua2; 4
- 1. Meizhou Clinical Medical College of Guangdong Medical University, Meizhou 514031, China 2.Guangdong Medical University, Zhanjiang 524023, China 3. Department of Radiology, Meizhou Peoples Hospital (Meizhou Academy of Medical Sciences), Meizhou 514031, China 4. Department of Radiology, Guangdong Second Provincial General Hospital, Guangzhou 510317, China
- Keywords:
- Keywords: gastric cancer serosal invasion clinicopathological feature radiomics feature CT
- PACS:
- R735.2；R816.5
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.12.010
- Abstract:
- Abstract: Objective To explore the predictive value of the enhanced CT imaging-based radiomics model and the clinical model for the serosal invasion in advanced gastric cancer. Methods The data were collected from 351 patients with advanced gastric cancer who underwent abdominal enhanced CT examination within 2 weeks before surgery, and the patients were randomly divided into a training group (n=247) and a validation group (n=104) in a ratio of 7:3. The 3 190 radiomics features which were extracted from the arterial and venous phase CT images using A.K software were dimensionally reduced for constructing a radiomics model. The pathological features between serosal invasion positive and negative groups were compared, and the significant features were used to establish a clinical model. The models performance was evaluated using receiver operating characteristic curve. Results In the training and validation groups, N staging and M staging were different in serosal invasion positive and negative groups (P<0.05). A total of 14 radiomic features were ultimately selected from the arterial and venous phase images. In the validation group, the diagnostic efficacy of the radiomic model for predicting serosal invasion in advanced gastric cancer was higher than that of the clinical model based on the combination of N staging and M staging (AUC: 0.854 vs 0.793). Conclusion Both the radiomics model based on the enhanced CT imaging and the clinical model based on the combination of N staging and M staging can successfully predict serosal invasion in advanced gastric cancer, but the former performs better.
Last Update: 2023-12-27