Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Weicai Huang; Kangneng Zhou; Yuming Jiang; Chuanli Chen; Qingyu Yuan; Zhen Han; Jingjing Xie; Shitong Yu; Zepang Sun; Yanfeng Hu; Jiang Yu; Hao Liu; Ruoxiu Xiao; Yikai Xu; Zhiwei Zhou; Guoxin Li

doi:10.3389/fonc.2020.01416

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Front Oncol. 2020 Aug 20:10:1416. doi: 10.3389/fonc.2020.01416. eCollection 2020.

Authors

Weicai Huang¹, Kangneng Zhou², Yuming Jiang¹, Chuanli Chen³, Qingyu Yuan³, Zhen Han¹, Jingjing Xie⁴, Shitong Yu¹, Zepang Sun¹, Yanfeng Hu¹, Jiang Yu¹, Hao Liu¹, Ruoxiu Xiao², Yikai Xu³, Zhiwei Zhou^{5

6}, Guoxin Li¹

Affiliations

¹ Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
² School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
³ Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
⁴ Center for Drug and Clinical Research, Nanfang Hospital, Southern Medical University, Guangzhou, China.
⁵ Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
⁶ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

Abstract

Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

Keywords: gastric cancer; metastasis; nomogram; peritoneum; radiomics.