Preoperative CT-based radiomics for diagnosing muscle invasion of bladder cancer

Abstract

Abstract Background The primary objective of the research was to develop a method using radiomics-based computed tomography (CT) to predict muscle invasion in bladder cancer (BCa) before surgery. Methods A total of 269 patients with bladder cancer were divided into two groups; training group (n = 188 cases) and validation group (n = 81 cases). Radiomics characteristics were determined by analyzing the CT images of each patient. The least absolute shrinkage and selection operator (LASSO) technique was used for developing a radiomics signature. Furthermore, logistic regression (LR), the support vector machine (SVM), decision tree (DT), and Artificial Neural Network (ANN) models were applied to differentiate between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Their performance was determined using the area under the receiver operating characteristic curve (AUC-ROC). In addition, accuracy, specificity, and sensitivity evaluations were also conducted. Results The radiomics signature was found to be successful in its prediction. A total of 1036 radiomics features were found in the 269 patients, and out of those, 16 were selected as the best predictors of radiomics features. The results revealed that the ANN classifier had the best performance, with a validation set accuracy of 0.950. Conclusions The current work used machine learning and radiomics techniques to successfully construct a prediction model for muscle invasion in bladder cancer. The ANN model produced significant outcomes that may be used in clinical diagnosis or therapy.