A Clinical Fusion Model Based on Radiomics Features and Deep Learning for Predicting CDKN2A/B Homozygous Deletion Status in IDH-mutant Diffuse Astrocytoma

Purpose: To construct a fusion model for predicting CDKN2A/B homozygous deletion status in patients with isocitrate dehydrogenase (IDH)-mutant diffuse astrocytoma by combining the radiomics features and deep learning (DL). Methods: A total of 200 IDH-mutant astrocytoma (103 CDKN2A/B homozygous deletion (HD) and 97 CDKN2A/B non-homozygous deletion (NHD)) patients were retrospectively enrolled in the training cohort (n = 140) and the external test cohort (n = 60) for the prediction of CDKN2A/B homozygous deletion status in patients with IDH-mutant astrocytoma. DL model was constructed by SE-Net model, radiomics features of different regions (edema, tumor and overall lesion) were extracted using Pyradiomics, and radiomics model was built by selecting 4 features in the edema region and 7 features in the tumor region by the least absolute shrinkage and selection operator (LASSO). Finally, a fusion model was jointly constructed by the DL model, radiomics model, and clinical features. The predictive performance of the 3 models was evaluated using calibration curves and decision curves, and compared with the fusion model. Results: Based on the results of the different models, we finally selected a fusion model consisting of DL model, radiomics model, and clinical features. The fusion model showed the best performance with an area under the curve (AUC) of 0.958 in the training cohort and 0.914 in the test cohort. Conclusions: The clinical fusion model based on radiomics features and DL features showed good performance in predicting CDKN2A/B homozygous deletion status in patients with IDH-mutant diffuse astrocytoma. Key Points: 1) Used DL and radiomics to non-invasively predict the CDKN2A/B homozygous deletion status. 2) The model can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma patients. 3) Our result improved classification accuracy and demonstrated better performance in the fusion model.