Huang Weicai, Zhou Kangneng, Jiang Yuming, Chen Chuanli, Yuan Qingyu, Han Zhen, Xie Jingjing, Yu Shitong, Sun Zepang, Hu Yanfeng, Yu Jiang, Liu Hao, Xiao Ruoxiu, Xu Yikai, Zhou Zhiwei, Li Guoxin
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Front Oncol. 2020 Aug 20;10:1416. doi: 10.3389/fonc.2020.01416. eCollection 2020.
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. 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 and stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. 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. 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.
本研究旨在评估基于计算机断层扫描（CT）的影像组学特征能否预测胃癌（GC）的腹膜转移（PM），并建立一种列线图用于术前预测PM状态。我们收集了两个癌症中心955例连续的病理T4期胃癌患者的CT图像，回顾性分析影像组学特征，然后在训练队列和两个验证队列中开发并验证了由292个定量图像特征构建的预测模型。应用套索回归模型选择特征并构建影像组学特征。通过多变量逻辑回归分析建立预测模型。通过将影像组学特征与临床和分期相结合来开发影像组学列线图。使用校准、鉴别和临床实用性来评估列线图的性能。在训练和验证队列中，PM状态与影像组学特征显著相关。发现在多变量逻辑分析中，影像组学特征是腹膜转移的独立预测因子。对于训练队列、内部验证队列和外部验证队列，影像组学特征预测PM的受试者操作特征曲线（AUC）下面积分别为0.751（95%CI，0.703 - 0.799）、0.802（95%CI，0.691 - 0.912）和0.745（95%CI，0.683 - 0.806）。此外，对于训练队列、内部验证队列和外部验证队列，影像组学列线图预测PM的AUC分别为0.792（95%CI，0.748 - 0.836）、0.870（95%CI，0.795 - 0.946）和0.815（95%CI，0.763 - 0.867）。基于CT的影像组学特征可以预测腹膜转移，并且影像组学列线图可为术前预测GC患者的PM状态做出有意义的贡献。
