Discriminating minimal residual disease status in multiple myeloma based on MRI: utility of radiomics and comparison of machine-learning methods

Abstract

To explore the possibility of discriminating minimal residual disease (MRD) status in multiple myeloma (MM) based on magnetic resonance imaging (MRI) and identify optimal machine-learning methods to optimise the clinical treatment regimen. A total of 83 patients were analysed retrospectively. They were divided randomly into training and validation cohorts. The regions of interest were segmented and radiomics features were extracted and analysed on two sequences, including T1-weighted imaging (WI) and fat saturated (FS)-T2WI, and then radiomics models were built in the training cohort and evaluated in the validation cohort. Clinical characteristics were calculated to build a traditional model. A combined model was also built using the clinical characteristics and radiomics features. Classification accuracy was assessed using area under the curve (AUC) and F1 score. In the training cohort, only the bone marrow (BM) infiltrate ratio (p=0.005) was retained after univariate and multivariable logistic regression analysis. In T1WI, the linear support vector machine (SVM) achieved the best performance compared to other classifiers, with AUCs of 0.811 and 0.708 and F1 scores of 0.792 and 0.696 in the training and validation cohorts, respectively. Similarly, in FS-T2WI sequence, linear SVM achieved the best performance with AUCs of 0.833 and 0.800 and F1 score of 0.833 and 0.800. The combined model constructed by the FS-T2WI-linear SVM and BM infiltrate ratio outperformed the traditional model (p=0.050 and 0.012, Delong test), but showed no significant difference compared with the radiomics model (p=0.798 and 0.855). The linear SVM-based machine-learning method can offer a non-invasive tool for discriminating MRD status in MM.