A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma

Abstract

Abstract Background Renal cell carcinoma (RCC) is a heterogeneous group of kidney cancers. Renal capsule invasion is an essential factor for RCC staging. To develop radiomics models from CT images for the preoperative prediction of capsule invasion in RCC patients. Methods This retrospective study included patients with RCC admitted to the Chongqing University Cancer Hospital (01/2011–05/2019). We built a radiomics model to distinguish patients grouped as capsule invasion versus non-capsule invasion, using preoperative CT scans. We evaluated effects of three imaging phases, i.e., unenhanced phases (UP), corticomedullary phases (CMP), and nephrographic phases (NP). Five different machine learning classifiers were compared. The effects of tumor and tumor margins are also compared. Five-fold cross-validation and the area under the receiver operating characteristic curve (AUC) are used to evaluate model performance. Results This study included 126 RCC patients, including 46 (36.5%) with capsule invasion. CMP exhibited the highest AUC (AUC = 0.81) compared to UP and NP, when using the forward neural network (FNN) classifier. The AUCs using features extracted from the tumor region were generally higher than those of the marginal regions in the CMP (0.81 vs. 0.73) and NP phase (AUC = 0.77 vs. 0.76). For UP, the best result was obtained from the marginal region (AUC = 0.80). The robustness analysis on the UP, CMP, and NP achieved the AUC of 0.76, 0.79, and 0.77, respectively. Conclusions Radiomics features in renal CT imaging are associated with the renal capsule invasion in RCC patients. Further evaluation of the models is warranted.

Keywords

• Renal cell carcinoma
• Capsule invasion
• Computed tomography
• Radiomics; machine learning