Ao Weiqun, Cheng Guohua, Lin Bin, Yang Rong, Liu Xuebin, Zhou Sheng, Wang Wenqi, Fang Zhaoxing, Tian Fengjuan, Yang Guangzhao, Wang Jian
Department of Radiology, Tongde Hospital of Zhejiang Province Hangzhou, Zhejiang, China.
Jianpei Technology Hangzhou, Zhejiang, China.
Am J Cancer Res. 2021 Jun 15;11(6):3123-3134. eCollection 2021.
Our study aimed to explore the value of applying the CT-based radiomic nomogram for predicting recurrence and/or metastasis (RM) of gastric stromal tumors (GSTs). During the past ten years, a total of 236 patients with GST were analyzed retrospectively. According to the postoperative follow-up classification, the patients were divided into two groups, namely non-recurrence/metastasis group (non-RM) and RM group. All the cases were randomly divided into primary cohort and validation cohort according to the ratio of 7:3. Standardized CT images were segmented by radiologists using ITK-SNAP software manually. Texture features were extracted from all segmented lesions, then radiomic features were selected and the radiomic nomogram was built using least absolute shrinkage and selection operator (LASSO) method. The clinical features with the greatest correlation with RM of GST were selected by univariate analysis, and used as parameters to build the clinical feature model. Eventually, model of radiomic and clinical features were fitted to construct the clinical + radiomic feature model. The performance of each model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). A total of 1223 features were extracted from all the segmentation regions of each case, and features were selected via the least absolute shrinkage and LASSO binary logistic regression model. After deletion of redundant features, four key features were obtained, which were used as the parameters to build a radiomic signature. The AUCs of radiomic nomogram in primary cohort and validation cohort were 0.816 and 0.946, respectively. The AUCs of clinical + radiomic feature model in primary cohort and validation cohort were 0.833 and 0.937, respectively. Using DeLong test, the differences of AUC values between radiomic nomogram and clinical + radiomic feature model in primary cohort (P = 0.840) and validation cohort (P = 0.857) were not statistically significant. To sum up, CT-based radiomic nomogram is of great potential in predicting the RM of GST non-invasively before operation.
我们的研究旨在探讨基于CT的放射组学列线图在预测胃间质瘤（GST）复发和/或转移（RM）方面的价值。在过去十年中，对236例GST患者进行了回顾性分析。根据术后随访分类，将患者分为两组，即非复发/转移组（非RM）和RM组。所有病例按7:3的比例随机分为训练队列和验证队列。放射科医生使用ITK-SNAP软件手动分割标准化CT图像。从所有分割病变中提取纹理特征，然后选择放射组学特征，并使用最小绝对收缩和选择算子（LASSO）方法构建放射组学列线图。通过单因素分析选择与GST的RM相关性最大的临床特征，并将其用作构建临床特征模型的参数。最终，将放射组学和临床特征模型进行拟合，构建临床+放射组学特征模型。通过受试者操作特征（ROC）曲线下面积（AUC）评估每个模型的性能。从每个病例的所有分割区域中总共提取了1223个特征，并通过最小绝对收缩和LASSO二元逻辑回归模型选择特征。删除冗余特征后，获得了四个关键特征，将其用作构建放射组学特征的参数。放射组学列线图在训练队列和验证队列中的AUC分别为0.816和0.946。临床+放射组学特征模型在训练队列和验证队列中的AUC分别为0.833和0.937。使用DeLong检验，放射组学列线图与临床+放射组学特征模型在训练队列（P = 0.840）和验证队列（P = 0.857）中的AUC值差异无统计学意义。综上所述，基于CT的放射组学列线图在术前无创预测GST的RM方面具有很大潜力。
