A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer

Tong,Yahan; Hu,Can; Cen,Xiaoping; Chen,Haiyan; Han,Zhe; Xu,Zhiyuan; Shi,Liang

doi:10.3892/mco.2024.2794

A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer

Authors:
- Yahan Tong
- Can Hu
- Xiaoping Cen
- Haiyan Chen
- Zhe Han
- Zhiyuan Xu
- Liang Shi
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Affiliations: Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China, Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang, Hangzhou, Zhejiang 310022, P.R. China, College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100000, P.R. China, Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, P.R. China
Published online on: October 21, 2024 https://doi.org/10.3892/mco.2024.2794
Article Number: 96
Copyright: © Tong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The objective of the present study was to build and validate a radio‑clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast‑enhanced computed tomography within one month before surgery. The focal area was mapped by ITK‑SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio‑clinical model was constructed by employing multi‑variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio‑clinical model, consisting of rad‑score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio‑clinical model. In conclusion, the radio‑clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.

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