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Prediction of the Depth of Tumor Invasion in Gastric Cancer: Potential Role of CT Radiomics

医学 队列 无线电技术 放射科 阶段(地层学) 回顾性队列研究 癌症 外科肿瘤学 内科学 古生物学 生物
作者
Yue Wang,Wei Liu,Yu Yang,Jingjuan Liu,Lin Jiang,Huadan Xue,Jing Lei,Zhengyu Jin,Jianchun Yu
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:27 (8): 1077-1084 被引量:36
标识
DOI:10.1016/j.acra.2019.10.020
摘要

Rationale and Objectives The aim of this study was to investigate the value of computed tomography (CT) radiomics for the differentiation between T2 and T3/4 stage lesions in gastric cancer. Materials and methods A total of 244 consecutive patients with pathologically proven gastric cancer were retrospectively included and split into a training cohort (171 patients) and a test cohort (73 patients). Preoperative arterial phase and portal phase contrast enhanced CT images were retrieved for tumor segmentation and feature extraction by using a dedicated postprocessing software. The random forest method was used to build the classifier models. Results The performance of single phase radiomics models were favorable in the differentiation between T2 and T3/4 stage tumors. Arterial phase-based radiomics model exhibited areas under the curve of 0.899 (95% CI: 0.812–0.955) in the training cohort and 0.825 (95% CI: 0.718–0.904) in the test cohort. Portal phase-based radiomics model showed areas under the curve of 0.843 (95% CI: 0.746–0.914) and 0.818 (95% CI: 0.711–0.899) in the training and test cohort, respectively. Conclusion CT radiomics approach has a potential role in differentiation between T2 and T3/4 stage tumors in gastric cancer. The aim of this study was to investigate the value of computed tomography (CT) radiomics for the differentiation between T2 and T3/4 stage lesions in gastric cancer. A total of 244 consecutive patients with pathologically proven gastric cancer were retrospectively included and split into a training cohort (171 patients) and a test cohort (73 patients). Preoperative arterial phase and portal phase contrast enhanced CT images were retrieved for tumor segmentation and feature extraction by using a dedicated postprocessing software. The random forest method was used to build the classifier models. The performance of single phase radiomics models were favorable in the differentiation between T2 and T3/4 stage tumors. Arterial phase-based radiomics model exhibited areas under the curve of 0.899 (95% CI: 0.812–0.955) in the training cohort and 0.825 (95% CI: 0.718–0.904) in the test cohort. Portal phase-based radiomics model showed areas under the curve of 0.843 (95% CI: 0.746–0.914) and 0.818 (95% CI: 0.711–0.899) in the training and test cohort, respectively. CT radiomics approach has a potential role in differentiation between T2 and T3/4 stage tumors in gastric cancer.
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