Prediction of High‐Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods

作者：Jianfang Liu, Piaoe Zeng, Wei Guo, Chunjie Wang, Yayuan Geng, Ning Lang, Huishu Yuan · 发表于：Journal of Magnetic Resonance Imaging · 年份：2021 · DOI：10.1002/jmri.27637 · 被引用次数：34 · 研究领域：Multiple Myeloma Research and Treatments、Radiomics and Machine Learning in Medical Imaging、Glioma Diagnosis and Treatment

Background Radiomics 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. Purpose To develop and test a magnetic resonance imaging (MRI)‐based radiomics model for predicting an HRC status in MM patients. Study Type Retrospective. Population Eighty‐nine MM patients (HRC [ n : 37] and non‐HRC [ n : 52]). Field Strength/Sequence A 3.0 T; fast spin‐echo (FSE): T1‐weighted image (T1WI) and fat‐suppression T2WI (FS‐T2WI). Assessment Overall, 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 T 1 WI/T 2 WI/two‐sequence MRI (T 1 WI and FS‐T 2 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 Tests Mann–Whitney U‐test, Chi‐squared test, Z test, and DeLong method. R...