Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

作者：Xiaoping Yi, Qiao Xiao, Feiyue Zeng, Hongling Yin, Zan Li, Cheng Qian, Cikui Wang, Guangwu Lei, Qingsong Xu, Chuanquan Li, Minghao Li, Guanghui Gong, Chi‐Shing Zee, Xiao Guan, Longfei Liu, Bihong T. Chen · 发表于：Frontiers in Oncology · 年份：2021 · DOI：10.3389/fonc.2020.570396 · 被引用次数：60 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Renal cell carcinoma treatment、Advanced X-ray and CT Imaging

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery. METHODS: Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis. RESULTS: A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in...