Spinal MRI-Based Radiomics Analysis to Predict Treatment Response in Multiple Myeloma

Abstract

The aim of this study was to explore the clinical utility of spinal magnetic resonance imaging-based radiomics to predict treatment response (TR) in patients with multiple myeloma (MM).Methods: A total of 123 MM patients (85 in the training cohort and 38 in the test cohort) with complete response (CR) (n = 40) or non-CR (n = 83) were retrospectively enrolled in the study. Key feature selection and data dimension reduction were performed using the least absolute shrinkage and selection operator regression. A nomogram was built by combining radiomic signatures and independent clinical risk factors. The prediction performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Treatment response was assessed by determining the serum and urinary levels of M-proteins, serum-free light chain ratio, and the percentage of bone marrow plasma cells.Results: Thirteen features were selected to build a radiomic signature.The International Staging System (ISS) stage was selected as an independent clinical factor. The radiomic signature and nomogram showed better calibration and higher discriminatory capacity (AUC of 0.929 and 0.917 for the radiomics and nomogram in the training cohort, respectively, and 0.862 and 0.874 for the radiomics and nomogram in the test cohort, respectively) than the clinical model (AUC of 0.661 and 0.674 in the training and test cohort, respectively). Decision curve analysis confirmed the clinical utility of the radiomics model.Conclusions: Nomograms incorporating a magnetic resonance imagingbased radiomic signature and ISS stage help predict the response to chemotherapy for MM and can be useful in clinical decision-making.

Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study

“…Radiomics features may quantify structural characteristics of bone marrow changes in MRI images and may be implemented as a complementary prognosis evaluation tool [ 25 ]. Some studies have shown MRI-based or PET/CT-based radiomics features may provide valuable information for image-based assessment of MRD and prediction of the therapy response [ 16 , 17 , 18 , 26 ]. Jamet B [ 27 ] tried to evaluate the potential prognostic value of textural features extracted from FDG-PET/CT in MM framework in addition to conventional PET-derived metabolic features and usual clinical/biological/genetic parameters.…”

“…The segmentation in a further study with using only the “clean” bone without discs might provide more valuable information on MM prognosis. Another research group combined the automatic BM segmentation with a subsequent radiomics analysis to automatically perform comprehensive, bone-by-bone phenotyping of the BM from wb-MR images which correctly exclude intervertebral discs [ 26 ]. This also brings inspiration for our future work.…”