A comparative study on MRI-based radiomics model selection for a high-risk cytogenetics prediction in multiple myeloma

We aim to develop a radiomics model based on MRI to predict high-risk cytogenetics (HRC) status in patients with multiple myeloma (MM) and identify optimal machine learning methods. We retrospectively analyzed 89 patients’ (37 HRC, 52 non-HRC) radiomics features extracted from fat suppression T2W and TIW image. The following classification methods, including, support vector machine, random forest, logistic regression (LR) and decision tree were used to construct radiomics models. LR model showed the highest performance with an AUC of 0.82 ± 0.02. Radiomics model based on LR classifier can be used to predict HRC status in patients with MM effectively.