Gumuyang Zhang1, Zhe Wu2, Xiaoxiao Zhang1, Lili Xu1, Li Mao3, Xiuli Li3, Yu Xiao4, Zhigang Ji5, Hao Sun6, Zhengyu Jin7. 1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China. 2. Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, , Liaoning Province, China. 3. Deepwise AI Lab, Deepwise Inc., Beijing, China. 4. Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China. 5. Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China. 6. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China. sunhao_robert@126.com. 7. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China. jinzy@pumch.cn.
Abstract
OBJECTIVES: This study investigated the feasibility of a computed tomography (CT)-based radiomics prediction model to evaluate muscle invasive status in bladder cancer. METHODS: Patients who underwent CT urography at two medical centers from October 2014 to May 2020 and had bladder urothelial carcinoma confirmed by postoperative histopathology were retrospectively enrolled. In total, 441 cases were collected and randomized into a training cohort (n = 293), an internal testing cohort (n = 73), and an external testing cohort (n = 75). The images were first filtered, and then, 1218 features were extracted. The best features related to muscle invasiveness of bladder cancer were identified by ANOVA. A prediction model was built by using the logistic regression method. Statistical analysis was performed by plotting the receiver operating characteristic curve. Indicators of the diagnostic performance of the prediction model, including sensitivity, specificity, accuracy, and area under curve (AUC), were evaluated. RESULTS: In the training, internal testing, and external testing cohorts, the prediction model diagnosed muscle-invasive bladder cancer with AUCs of 0.885 (95% confidence interval [95% CI] 0.841-0.929), 0.820 (95% CI 0.698-0.941), and 0.784 (95% CI 0.674-0.893), respectively. In the internal testing cohort, the sensitivity, specificity, and accuracy of the model were 0.667 (95% CI 0.387-0.870), 0.845 (95% CI 0.721-0.922), and 0.782 (95% CI 0.729-0.827), respectively. In the external testing cohort, the sensitivity, specificity, and accuracy of the model were 0.742 (95% CI 0.551-0.873), 0.750 (95% CI 0.594-0.863), and 0.782 (95% CI 0.729-0.827), respectively. CONCLUSIONS: CT-based radiomics prediction model can evaluate muscle invasiveness of bladder cancer before surgery with a good diagnostic performance. KEY POINTS: • CT-based radiomics model can evaluate muscle invasive status in bladder cancer. • The radiomics model shows good diagnostic performance to differentiate muscle-invasive bladder cancer from non-muscle-invasive bladder cancer. • This preoperative CT-based prediction method might complement MR evaluation of bladder cancer and supplement biopsy.
OBJECTIVES: This study investigated the feasibility of a computed tomography (CT)-based radiomics prediction model to evaluate muscle invasive status in bladder cancer. METHODS: Patients who underwent CT urography at two medical centers from October 2014 to May 2020 and had bladder urothelial carcinoma confirmed by postoperative histopathology were retrospectively enrolled. In total, 441 cases were collected and randomized into a training cohort (n = 293), an internal testing cohort (n = 73), and an external testing cohort (n = 75). The images were first filtered, and then, 1218 features were extracted. The best features related to muscle invasiveness of bladder cancer were identified by ANOVA. A prediction model was built by using the logistic regression method. Statistical analysis was performed by plotting the receiver operating characteristic curve. Indicators of the diagnostic performance of the prediction model, including sensitivity, specificity, accuracy, and area under curve (AUC), were evaluated. RESULTS: In the training, internal testing, and external testing cohorts, the prediction model diagnosed muscle-invasive bladder cancer with AUCs of 0.885 (95% confidence interval [95% CI] 0.841-0.929), 0.820 (95% CI 0.698-0.941), and 0.784 (95% CI 0.674-0.893), respectively. In the internal testing cohort, the sensitivity, specificity, and accuracy of the model were 0.667 (95% CI 0.387-0.870), 0.845 (95% CI 0.721-0.922), and 0.782 (95% CI 0.729-0.827), respectively. In the external testing cohort, the sensitivity, specificity, and accuracy of the model were 0.742 (95% CI 0.551-0.873), 0.750 (95% CI 0.594-0.863), and 0.782 (95% CI 0.729-0.827), respectively. CONCLUSIONS: CT-based radiomics prediction model can evaluate muscle invasiveness of bladder cancer before surgery with a good diagnostic performance. KEY POINTS: • CT-based radiomics model can evaluate muscle invasive status in bladder cancer. • The radiomics model shows good diagnostic performance to differentiate muscle-invasive bladder cancer from non-muscle-invasive bladder cancer. • This preoperative CT-based prediction method might complement MR evaluation of bladder cancer and supplement biopsy.