Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.
Authors
Affiliations (10)
Affiliations (10)
- Department of Medical Imaging, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
- Department of Gastric surgery, Institutes of Basic Medicine and Cancer (IBMC), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, 310022, China.
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
- Department of Research Collaboration, R&D Center, Hangzhou Deepwise & League of PHD Technology Co., Ltd, Hangzhou, 311101, China.
- Department of Colorectal and Anal Surgery, Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, China.
- The First People's Hospital of Lin'an District, Hangzhou City, Hangzhou, Zhejiang Province, 311399, China.
- Department of Medical Imaging, Zhejiang Cancer Hospital, Hangzhou, 310022, China. [email protected].
- Department of Gastric surgery, Institutes of Basic Medicine and Cancer (IBMC), Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, 310022, China. [email protected].
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China. [email protected].
- Department of Medical Imaging, Zhejiang Cancer Hospital, Hangzhou, 310022, China. [email protected].
Abstract
In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial. We retrospectively collected 256 patients with AEG from two centres. The radiomics features were extracted from the preoperative diagnostic CT images, and the feature selection method and machine learning method were applied to reduce the feature size and establish the predictive imaging features. By comparing the three machine learning methods, the best radiomics nomogram was selected, and the average AUC was obtained by 20 repeats of fivefold cross-validation for comparison. The fusion model was constructed by logistic regression combined with clinical factors. On this basis, ROC curve, calibration curve and decision curve of the fusion model are constructed. The predictive efficacy of fusion model for tumour invasion depth was higher than that of radiomics nomogram, with an AUC of 0.764 vs. 0.706 in the test set, P < 0.001, internal validation set 0.752 vs. 0.697, P < 0.001, and external validation set 0.756 vs. 0.687, P < 0.001, respectively. The predictive efficacy of the lymph node metastasis fusion model was higher than that of the radiomics nomogram, with an AUC of 0.809 vs. 0.732 in the test set, P < 0.001, internal validation set 0.841 vs. 0.718, P < 0.001, and external validation set 0.801 vs. 0.680, P < 0.001, respectively. We have developed a fusion model combining radiomics and clinical risk factors, which is crucial for the accurate preoperative diagnosis and treatment of AEG, advancing precision medicine. It may also spark discussions on the imaging feature differences between AEG and GC (Gastric cancer).