MRI-based bone marrow radiomics for predicting cytogenetic abnormalities in multiple myeloma.

AIM: To develop a radiomics signature applied to magnetic resonance imaging (MRI)-images to predict cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS: Patients with newly diagnosed MM were enrolled retrospectively from March 2019 to September 2022. They were categorised into the high-risk cytogenetics (HRC) group and standard-risk cytogenetics (SRC) group. The patients were allocated randomly at a ratio of 7:3 into training and validation cohorts. Volumes of interest (VOI) was drawn manually on fat suppression T2-weighted imaging (FS-T2WI) and copied to the same location of the T1-weighted imaging (T1WI) sequence. Radiomics features were extracted from two sequences and selected by reproducibility and redundant analysis. The least absolute shrinkage selection operation (LASSO) algorithm was applied to build the radiomics signatures. The performance of the radiomics signatures to distinguish HRC with SRC was evaluated by ROC curves. The area under the curve (AUC), specificity, and sensitivity were also calculated.

RESULTS: A total of 105 MM patients were enrolled in this study. The four and 11 most significant and relevant features were selected separately from T1WI and FS-T2WI sequences to build the radiomics signatures based on the training cohort. Compared to the T1WI sequence, the radiomics signature based on the FS-T2WI sequence achieved better performance with AUCs of 0.896 and 0.729 in the training and validation cohorts respectively. A sensitivity of 0.833, specificity of 0.667, and Youden index of 0.500 were achieved for the FS-T2WI radiomics signature in the validation cohort.

CONCLUSIONS: The radiomics signature based on MRI provides a non-invasive and convenient tool to predict cytogenetic abnormalities in MM patients.