Lumbar MR-based radiomics nomogram for detecting minimal residual disease in patients with multiple myeloma.

objectivesMinimal residual disease (MRD) is a standard for assessing treatment response in multiple myeloma (MM). MRD negativity is considered to be the most powerful predictor of long-term good outcomes. This study aimed to develop and validate a radiomics nomogram based on magnetic resonance imaging (MRI) of the lumbar spine to detect MRD after MM treatment.

methodsA total of 130 MM patients (55 MRD negative and 75 MRD positive) who had undergone MRD testing through next-generation flow cytometry were divided into a training set (n = 90) and a test set (n = 40). Radiomics features were extracted from lumbar spinal MRI (T1-weighted images and fat-suppressed T2-weighted images) by means of the minimum redundancy maximum relevance method and the least absolute shrinkage and selection operator algorithm. A radiomics signature model was constructed. A clinical model was established using demographic features. A radiomics nomogram incorporating the radiomics signature and independent clinical factor was developed using multivariate logistic regression analysis.

resultsSixteen features were used to establish the radiomics signature. The radiomics nomogram included the radiomics signature and the independent clinical factor (free light chain ratio) and showed good performance in detecting the MRD status (area under the curve: 0.980 in the training set and 0.903 in the test set).

conclusionsThe lumbar MRI-based radiomics nomogram showed good performance in detecting MRD status in MM patients after treatment, and it is helpful for clinical decision-making. KEY POINTS: • The presence or absence of minimal residual disease status has a strong predictive significance for the prognosis of patients with multiple myeloma. • A radiomics nomogram based on lumbar MRI is a potential and reliable tool for evaluating minimal residual disease status in MM.