Deep learning based risk stratification for treatment management of multiple myeloma with sequential MRI scans

Abstract

We are developing a decision support tool for treatment response monitoring of multiple myeloma (MM) disease in spinal MRI scan images. This study investigated the feasibility of using deep learning to stratify the risks for patients who underwent bone marrow transplant (BMT) and assessed its prognostic value in predicting time to progression (TTP) after BMT. We combined a convolutional neural network (CNN) with a recurrent neural network (RNN), referred to as C-RNN model, to classify the low and high risk groups of patients with pre- and post-BMT MRI scans. The CNN was used as the encoder with the pair of pre- and post-BMT MR images as input, and the RNN was used as the decoder to receive time-sequence vectors output from the CNN encoder for classification of patients with high and low risk of progression within a certain time. With IRB approval, 63 pairs of pre- and post-BMT T1W sagittal view of MRI scans and the time to progression (TTP) within 5 years of follow up for each patient were collected retrospectively from 63 MM patients at our institution. With respect to the TTP censored at 24 months, 41 and 22 patients were separated to the low risk and high risk groups as reference standard. Our C-RNN was trained and validated with 5-fold cross-validation. The results showed that the C-RNN achieved an average test AUC of 0.801±0.037. The Kaplan-Meier analysis showed that the high risk group patients identified by the C-RNN model had significantly shorter TTP than those low risk patients (P<0.05 by log-rank test).