RESUMO
Background: Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab. Purpose: We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab. Methods: 118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features. Results: 71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation). Conclusion: Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.