Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison

Abstract

Abstract Background: Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomic studies have been considered a new method to predict these side effects. This study was performed by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods for predicting radiation-induced rectal toxicity. Methods: Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Clinical and dosimetric data were gathered, and radiation toxicity was assessed using Common Terminology Criteria for Adverse Events (CTCAE). Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance. Results:The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%. The top-performing model was KNN, with an AUC of 0.86, accuracy rates of 79%, sensitivity rates of 63%, and specificity rates of 91%, respectively. Conclusions: This research showed that as a biomarker for predicting radiation-induced rectal toxicity, MR images outperform CT images.