Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1080/09553002.2018.1492756

Type

Journal article

Journal

International Journal of Radiation Biology

Publication Date

02/09/2018

Volume

94

Pages

829 - 837