• Jianfang Liu, Piaoe Zeng, Wei Guo, Chunjie Wang, Yayuan Geng, Ning Lang, and Huishu Yuan.
• Department of Radiology, Peking University Third Hospital, Haidian District, Beijing, People's Republic of China.
• J Magn Reson Imaging. 2021 Oct 1; 54 (4): 1303-1311.

BackgroundRadiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM.PurposeTo develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients.Study TypeRetrospective.PopulationEighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]).Field Strength/SequenceA 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI).AssessmentOverall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T1 WI/T2 WI/two-sequence MRI (T1 WI and FS-T2 WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non-HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k-nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values.Statistical TestsMann-Whitney U-test, Chi-squared test, Z test, and DeLong method.ResultsThe LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82; P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05).ConclusionThe LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two-sequence MRI showed good performance in differentiating HRC and non-HRC statuses in MM.Evidence Level3 TECHNICAL EFFICACY: Stage 2.© 2021 International Society for Magnetic Resonance in Medicine.

13 keywords...

hide…