Sun Zhaonan, Cui Yingpu, Xu Chunru, Yu Yanfei, Han Chao, Liu Xiang, Lin Zhiyong, Wang Xiangpeng, Li Changxin, Zhang Xiaodong, Wang Xiaoying
Department of Radiology, Peking University First Hospital, Peking University, Beijing, China.
Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Front Oncol. 2022 Jun 6;12:863534. doi: 10.3389/fonc.2022.863534. eCollection 2022.
To develop radiomics models to predict inferior vena cava (IVC) wall invasion by tumor thrombus (TT) in patients with renal cell carcinoma (RCC).
Preoperative MR images were retrospectively collected from 91 patients with RCC who underwent radical nephrectomy (RN) and thrombectomy. The images were randomly allocated into a training (n = 64) and validation (n = 27) cohort. The inter-and intra-rater agreements were organized to compare masks delineated by two radiologists. The masks of TT and IVC were manually annotated on axial fat-suppression T2-weighted images (fsT2WI) by one radiologist. The following models were trained to predict the probability of IVC wall invasion: two radiomics models using radiomics features extracted from the two masks (model 1, radiomics model_IVC; model 2, radiomics model_TT), two combined models using radiomics features and radiological features (model 3, combined model_IVC; model 4, combined model_TT), and one radiological model (model 5) using radiological features. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were applied to validate the discriminatory effect and clinical benefit of the models.
Model 1 to model 5 yielded area under the curves (AUCs) of 0.881, 0.857, 0.883, 0.889, and 0.769, respectively, in the validation cohort. No significant differences were found between these models ( = 0.108-0.951). The dicision curve analysis (DCA) showed that the model 3 had a higher overall net benefit than the model 1, model 2, model 4, and model 5.
The combined model_IVC (model 3) based on axial fsT2WI exhibited excellent predictive performance in predicting IVC wall invasion status.
建立放射组学模型，以预测肾细胞癌（RCC）患者肿瘤血栓（TT）对下腔静脉（IVC）壁的侵犯情况。
回顾性收集91例行根治性肾切除术（RN）和血栓切除术的RCC患者的术前磁共振成像（MR）图像。这些图像被随机分为训练组（n = 64）和验证组（n = 27）。组织评估者间和评估者内一致性，以比较两位放射科医生勾勒的掩码。由一位放射科医生在轴向脂肪抑制T2加权图像（fsT2WI）上手动标注TT和IVC的掩码。训练以下模型来预测IVC壁侵犯的概率：两个使用从两个掩码中提取的放射组学特征的放射组学模型（模型1，放射组学模型_IVC；模型2，放射组学模型_TT），两个使用放射组学特征和放射学特征的联合模型（模型3，联合模型_IVC；模型4，联合模型_TT），以及一个使用放射学特征的放射学模型（模型5）。应用受试者操作特征（ROC）曲线分析和决策曲线分析（DCA）来验证模型的鉴别效果和临床益处。
在验证队列中，模型1至模型5的曲线下面积（AUC）分别为0.881、0.857、0.883、0.889和0.769。这些模型之间未发现显著差异（P = 0.108 - 0.951）。决策曲线分析（DCA）表明，模型3的总体净效益高于模型1、模型2、模型4和模型5。
基于轴向fsT2WI的联合模型_IVC（模型3）在预测IVC壁侵犯状态方面表现出优异的预测性能。
