Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis

Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we developed a deep learning-based method for the automated extraction of radiomics features from Positron Emission Computed Tomography (PET) and Magnetic Resonance (MR) images to predict ARR in patients with MS. Methods Twenty-five patients with a definite diagnosis of Relapsing-Remitting MS (RRMS) were enrolled in this study. We designed a multi-branch fully convolutional neural network to segment lesions from PET/MR images. After that, radiomics features were extracted from the obtained lesion volume of interest. Three feature selection methods were used to retain features highly correlated with ARR. We combined four classifiers with different feature selection methods to form twelve models for ARR classification. Finally, the model with the best performance was chosen. Results Our network achieved precise automatic lesion segmentation with a Dice Similarity Coefficient (DSC) of 0.81 and a precision of 0.86. Radiomics features from lesions filtered by Recursive Feature Elimination (RFE) achieved the best performance in the Support Vector Machines (SVM) classifier. The classification model performance was best when radiomics from both PET and MR were combined to predict ARR, with high accuracy at 0.88 and Area Under the ROC curves (AUC) at 0.96, which outperformed MR or PET-based model and clinical indicators-based model. Conclusion Our automatic segmentation masks can replace manual ones with excellent performance. Furthermore, the deep learning and PET/MR radiomics-based model in our research is an effective tool in assisting ARR classification of MS patients.