X Chen1, B Wan1, D Yang1, H Zhao1, W Tan2. 1. Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China. 2. Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China. twl@smu.edu.cn.
Abstract
PURPOSE: To assess a nomogram including the computed tomography (CT) score and body mass index (BMI) that was constructed to predict indolent diseases in a cohort of patients with renal masses. MATERIALS AND METHODS: The data collected from patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN) between January 2012 and September 2016 were analyzed. Two urologic surgeons and a radiologist reviewed the images to determine the CT score. Postoperative pathological assessment was performed to categorize renal masses as either indolent or aggressive. The BMI and CT scores were included in the nomogram to identify the risk of indolent disease. The performance of the novel model was assessed by using discrimination, calibration plots, and decision curve analysis (DCA). RESULTS: Two hundred and two participants (with 202 masses) who underwent RN or PN were included; 37% of the masses were indolent. The predictive performances of the nomogram revealed areas under the curve of 0.866 for masses of all cases and 0.808 for cT1 masses without visible fat. DCA revealed that the nomogram was moderately clinically useful. The calibration plots showed a reasonable calibration and systematic overestimation of indolent disease based on nomogram predictions. CONCLUSIONS: The CT score nomogram discriminated well between indolent and aggressive renal masses. The model had a reasonable calibration in our cohort for discriminating indolent from aggressive lesions. Further research to validate and assess the nomogram is required.
PURPOSE: To assess a nomogram including the computed tomography (CT) score and body mass index (BMI) that was constructed to predict indolent diseases in a cohort of patients with renal masses. MATERIALS AND METHODS: The data collected from patients undergoing partial nephrectomy (PN) or radical nephrectomy (RN) between January 2012 and September 2016 were analyzed. Two urologic surgeons and a radiologist reviewed the images to determine the CT score. Postoperative pathological assessment was performed to categorize renal masses as either indolent or aggressive. The BMI and CT scores were included in the nomogram to identify the risk of indolent disease. The performance of the novel model was assessed by using discrimination, calibration plots, and decision curve analysis (DCA). RESULTS: Two hundred and two participants (with 202 masses) who underwent RN or PN were included; 37% of the masses were indolent. The predictive performances of the nomogram revealed areas under the curve of 0.866 for masses of all cases and 0.808 for cT1 masses without visible fat. DCA revealed that the nomogram was moderately clinically useful. The calibration plots showed a reasonable calibration and systematic overestimation of indolent disease based on nomogram predictions. CONCLUSIONS: The CT score nomogram discriminated well between indolent and aggressive renal masses. The model had a reasonable calibration in our cohort for discriminating indolent from aggressive lesions. Further research to validate and assess the nomogram is required.
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