Document Type : Conference Proceedings
Authors
1
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
2
Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
3
Department of Radiation Oncology, Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4
Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
5
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
6
Department of Radiology Technology, Allied Medicine Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract
Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response.
Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imaging (MRI). A wide range of radiomic features from different feature sets were extracted from all images. Delta radiomics was calculated as relative changes of pre-post-IMRT image features. Four feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 5-fold cross-validation as the criterion for feature selection and classification. For IMRT response prediction, pre, post and Delta radiomic features were analyzed. Area under the curve (AUC) was calculated as model performance value. IMRT response was obtained by changes in ADC values
.
Results: For IMRT response prediction, 15 models were developed. Pre-ADC model, unBalance/Select from Model/Adaptive Boosting had the highest predictive performance (AUC, 0.78).
Conclusion:Radiomic models developed by MR Image features and machine learning approaches are noninvasive, easy and cost effective methods for personalized prostate cancer diagnosis and therapy.
Keywords
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