CT-based radiomics to predict muscle invasion in bladder cancer

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.

Automated summary

This study investigated the feasibility of a CT-based radiomics prediction model to evaluate muscle invasiveness in bladder cancer. The study found that the prediction model had good diagnostic performance in diagnosing muscle-invasive bladder cancer.