Document Type : Conference Proceedings
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Department of Community Medicine, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran. Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Department of Radiation Oncology, Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
Department of Radiology Technology, Allied Medicine Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Introduction: Rectal toxicity is a dose limiting issue in prostate cancer radiotherapy. Prediction of these effects may be used to tailor the therapy. The purpose of this work was to develop predictive radiomic models based on clinical, dosimetric and radiomic features extracted from rectal wall magnetic resonance image (MRI).
Materials and Methods: This study was conducted on 30 prostate cancer patients underwent intensity modulated radiation therapy (IMRT). Patient’s clinical and dosimetric parameters were collected and proctitis was assessed. All patients underwent MRI before and after IMRT with same protocol. Several radiomic features were extracted from rectal wall MR images including T2 weighted (T2W) and apparent diffusion coefficient (ADC) scans and robust features were found. Lasso regularization was used to create multivariable Elastic Net logistic regression model, based on clinical, dosimetric and robust features for prediction and feature selection, simultaneously. All models were cross-validated using repeated five-fold cross-validation and their performance were evaluated by area under the curve of the ROC curve (AUC), sensitivity and specificity.
Results: Eleven predictive models including one clinical and ten radiomic models were built. All models were found as high predictive performance (AUC≥0.83). T2 based radiomic models were more predictive rather than ADC models. Post-IMRT T2W radiomic model also showed a good predictive performance. Image features enhanced the all model’s predictive performance.
Conclusion: Rectal wall MR radiomic features integrating with clinical and dose-volume parameters could improve the prediction performance of radiotherapy induced proctitis. The authors have demonstrated the performance of radiomic features extracted from pre IMRT T2W and ADC MR Images for prediction of IMRT induced rectal toxicity. These results may be used to tailor the therapy in terms of patient and.