A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma

Objectives We aimed to investigate the feasibility of predicting high-risk cytogenetic abnormalities (HRCAs) in patients with multiple myeloma (MM) using a spinal MRI-based radiomics method. Materials and methods In this retrospective study, we analyzed the radiomic features of 248 lesions (HRCA [n = 111] and non-HRCA [n = 137]) using T1WI, T2WI, and fat suppression T2WI. To construct the radiomics model, the top nine most frequent radiomic features were selected using logistic regression (LR) machine-learning processes. A combined LR model incorporating radiomic features and basic clinical characteristics (age and sex) was also built. Fivefold external cross-validation was performed, and a comparative analysis of 10 random fivefold cross-validation sets was used to verify result stability. Model performance was compared by plotting receiver operating characteristic curves and the area under the curve (AUC). Results Comparable AUC values were observed between the radiomics model and the combined model in validation cohorts (AUC: 0.863 vs. 0.870, respectively, p = 0.206). The radiomics model had an AUC of 0.863, with a sensitivity of 0.789, a specificity of 0.787, a positive predictive value of 0.753, a negative predictive value of 0.824, and an accuracy of 0.788 in the validation cohort, which were comparable with the performance in the training cohorts. Conclusions Radiomic features of routine spinal MRI reflect differences between HRCAs and non-HRCAs in patients with MM. This MRI-based radiomics model might be a useful and independent tool to predict HRCAs in patients MM.

Automated summary

The radiomics model based on spinal MRI is effective in predicting high-risk cytogenetic abnormalities in patients with multiple myeloma (MM), showing comparable performance with the combined model incorporating basic clinical characteristics. This MRI-based radiomics model may serve as a useful and independent tool for predicting HRCAs in MM patients.