Rectal wall MRI radiomics in prostate cancer patients: prediction of and correlation with early rectal toxicity

Purpose: To investigate MRI radiomic analysis to assess IMRT associated rectal wall changes and also for predicting radiotherapy induced rectal toxicity.

Material and methods: At first, a machine learning radiomic analysis was applied on T2-weighted (T2W) and apparent diffusion coefficient (ADC) rectal wall MR images of prostate cancer patients' pre- and post-IMRT to predict rectal toxicity. Next, Wilcoxon singed ranked test was performed to find radiomic features with significant changes pre- and post-IMRT. A logistic regression classifier was used to find correlation between features with significant changes and radiation toxicity. Area under the curve (AUC) of receiver operating characteristic (ROC) curve was used in two levels of study for finding performances.

Results: AUC_mean, 0.68 ± 0.086 and 0.61 ± 0.065 were obtained for pre- and post-IMRT T2 radiomic models, respectively. For ADC radiomic models, AUC_mean was 0.58 ± 0.034 for pre-IMRT and was 0.56 ± 0.038 for post-IMRT. Wilcoxon-signed rank test revealed that 9 T2 radiomic features vary significantly post-IMRT. The AUC of logistic-regression was in the range of 0.46-0.58 for single significant features and was 0.81 when all significant features were combined.

Conclusions: Pre-IMRT MR image radiomic features could predict rectal toxicity in prostate cancer patients. Radiotherapy associated complications may be assessed by studying the changes in the MR radiomic features.