Xiaoping Yi1, Qiao Xiao2, Feiyue Zeng1, Hongling Yin3, Zan Li4, Cheng Qian4, Cikui Wang2, Guangwu Lei1, Qingsong Xu5, Chuanquan Li5, Minghao Li2, Guanghui Gong3, Chishing Zee6, Xiao Guan2, Longfei Liu2, Bihong T Chen7. 1. Department of Radiology, Xiangya Hospital, Central South University, Changsha, China. 2. Department of Urology, Xiangya Hospital, Central South University, Changsha, China. 3. Department of Pathology, Xiangya Hospital, Central South University, Changsha, China. 4. Xiangya School of Medicine, Central-South University, Changsha, China. 5. School of Mathematics and Statistics, Central South University, Changsha, China. 6. Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States. 7. Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States.
Abstract
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 differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively. CONCLUSION: We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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 differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively. CONCLUSION: We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
Authors: Borje Ljungberg; Karim Bensalah; Steven Canfield; Saeed Dabestani; Fabian Hofmann; Milan Hora; Markus A Kuczyk; Thomas Lam; Lorenzo Marconi; Axel S Merseburger; Peter Mulders; Thomas Powles; Michael Staehler; Alessandro Volpe; Axel Bex Journal: Eur Urol Date: 2015-01-21 Impact factor: 20.096
Authors: Raphael Sexauer; Shan Yang; Thomas Weikert; Julien Poletti; Jens Bremerich; Jan Adam Roth; Alexander Walter Sauter; Constantin Anastasopoulos Journal: Invest Radiol Date: 2022-04-02 Impact factor: 10.065