Contrast-Enhanced Computed Tomography–Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer

作者：Han Liu, Yiyun Wang, Yingqiao Liu, Dingyi Lin, Cangui Zhang, Yuyun Zhao, Li Chen, Yi Li, Jianyu Yuan, Chen Zhao, Jiang Yu, Wentao Kong, Tao Chen · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.882786 · 被引用次数：7 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Gastric Cancer Management and Outcomes、Ferroptosis and cancer prognosis

Objective: The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC). Material: RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers. Methods: The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated. Results: Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts. Conclusions: Th...