Comparison prediction models of bladder toxicity based on radiomic features of CT and MRI in patients with prostate cancer undergoing radiotherapy

Purpose: This study aimed to assess the radiomic features of computed tomography (CT) and magnetic resonance imaging (MRI) of the bladder wall before radiotherapy using machine learning (ML) methods to predict bladder radiotoxicity in patients with prostate cancer.

Methods: This study enrolled 70 patients with pathologically confirmed prostate cancer who were candidates for radiation therapy (RT). CT and MRI of the bladder wall before radiotherapy were used to extract radiomic features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Algorithms such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) have been used to develop models based on radiomic, dosimetry, and clinical parameters. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and accuracy were used to analyze the predictive power of all models.

Results: The RF and LR models based on the radiomic features of MRI and clinical/dosimetry parameters with an AUC of 0.95 and 0.93, and an accuracy of 86% and 86%, respectively, had the highest performance in the prediction of bladder radiation toxicity.

Conclusions: This study showed that, firstly, CT and MRI radiomic features of the bladder wall before treatment could be used to predict bladder radiotoxicity. Second, MRI is better than CT in predicting bladder toxicity caused by radiation. And thirdly, the performance of the predictive models based on the combination of radiomic, clinical, and dosimetry characteristics was improved.