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• W3208472118 abstract "Purpose/Objective(s)This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer patients treated with radiotherapy.Materials/MethodsContrast-enhanced CT images were collected for patients with confirmed ORN diagnosis at MD Anderson Cancer Center between 2008 and 2018. The ORN region of interest (ROI) was segmented manually in each image. The control ROIs of the contralateral health mandible were generated by a Python script then adjusted manually in each image. An open-source software was then used to extract the radiomic features from both ORN and control ROIs after the application of intrinsic filters. The pairwise correlation filter was used to remove radiomic features whose pairwise correlation was ≥0.99. Filter algorithms were then used to further reduce the number of radiomic features. After that, wrapper and embedded methods were applied on the resulting radiomic features. Finally, Gini importance and Recursive Feature Elimination (RFE) were used to select the final radiomic features for the predictive model. The support vector machine (SVM) with linear kernel was used for the binary classification of ORN and normal mandibular bone. The performance of the model was evaluated using the Area Under Curve (AUC).Results150 patients with radiologically established ORN were included. The mean age was 62.3 years (range 27-82). The mean time between the end of RT and ORN detection was 32.6 months. A total of Initial 1316 radiomic features were considered. The pairwise correlation omitted 432 features with a correlation ≥ 0.99. The RFE based on the Gini index selected 5 radiomics features in our HNC cohort. We validated this binary classification model using 5-fold cross-validation. During this validation, the range of AUC was (0.84–0.95) & the average AUC was 0.90. This AUC range reflect the high performance of the final classifier in the differentiation between ORN and normal mandible using CECT images.ConclusionRadiomic features were successfully used to build a model to discriminate ORN and normal mandibular bone in head and neck cancer patients. Our statistical model achieved satisfying prediction accuracy and can be potentially used for ORN prediction purpose upon external validation. This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer patients treated with radiotherapy. Contrast-enhanced CT images were collected for patients with confirmed ORN diagnosis at MD Anderson Cancer Center between 2008 and 2018. The ORN region of interest (ROI) was segmented manually in each image. The control ROIs of the contralateral health mandible were generated by a Python script then adjusted manually in each image. An open-source software was then used to extract the radiomic features from both ORN and control ROIs after the application of intrinsic filters. The pairwise correlation filter was used to remove radiomic features whose pairwise correlation was ≥0.99. Filter algorithms were then used to further reduce the number of radiomic features. After that, wrapper and embedded methods were applied on the resulting radiomic features. Finally, Gini importance and Recursive Feature Elimination (RFE) were used to select the final radiomic features for the predictive model. The support vector machine (SVM) with linear kernel was used for the binary classification of ORN and normal mandibular bone. The performance of the model was evaluated using the Area Under Curve (AUC). 150 patients with radiologically established ORN were included. The mean age was 62.3 years (range 27-82). The mean time between the end of RT and ORN detection was 32.6 months. A total of Initial 1316 radiomic features were considered. The pairwise correlation omitted 432 features with a correlation ≥ 0.99. The RFE based on the Gini index selected 5 radiomics features in our HNC cohort. We validated this binary classification model using 5-fold cross-validation. During this validation, the range of AUC was (0.84–0.95) & the average AUC was 0.90. This AUC range reflect the high performance of the final classifier in the differentiation between ORN and normal mandible using CECT images. Radiomic features were successfully used to build a model to discriminate ORN and normal mandibular bone in head and neck cancer patients. Our statistical model achieved satisfying prediction accuracy and can be potentially used for ORN prediction purpose upon external validation." @default.
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• W3208472118 date "2021-11-01" @default.
• W3208472118 modified "2023-09-27" @default.
• W3208472118 title "Radiomic Correlates of Mandibular Osteoradionecrosis After Radiation Treatment of Head and Neck Cancer Patients" @default.
• W3208472118 doi "https://doi.org/10.1016/j.ijrobp.2021.07.566" @default.
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