{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:11:12Z","timestamp":1776273072634,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"vor","delay-in-days":247,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81971575"],"award-info":[{"award-number":["81971575"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Purpose<\/jats:italic>. To identify significant radiomics features based on MRI and establish effective models for predicting the response to bortezomib\u2010based regimens. <jats:italic>Materials and Methods<\/jats:italic>. In total, 95 MM patients treated with bortezomib\u2010based therapy were enrolled, including 77 with bortezomib, cyclophosphamide, and dexamethasone (BCD) and 18 with bortezomib, lenalidomide, and dexamethasone (VRD). Based on T1\u2010weighted imaging (T1WI) and T2\u2010weighted imaging with fat suppression (T2WI\u2010fs), radiomics features were extracted and then selected. The random forest (RF), <jats:italic>k<\/jats:italic>\u2010nearest neighbor, support vector machine, logistic regression, decision tree, and Bayes models were built using the selected features. The predictive power of six models for response to BCD and VRD regimens were evaluated. The correlation between the selected features and progression\u2010free survival (PFS) was also analyzed. <jats:italic>Results<\/jats:italic>. Four wavelet features were correlated with BCD treatment response. The six models all showed predictive power for BCD regimen (AUC: 0.84\u20100.896 in the training set, 0.801\u20100.885 in the validation set), and RF performed relatively better than others. Nevertheless, all the BCD\u2010based models were incapable of predicting the VRD treatment response. The wavelet\u2010HLH_firstorder_kurtosis was also associated with PFS (log\u2010rank <jats:italic>P<\/jats:italic> = 0.019). <jats:italic>Conclusion<\/jats:italic>. The four wavelet features were valuable biomarkers for predicting the response to BCD regimen. The six models based on these features showed predictive power, and RF was the best. One wavelet feature was also a survival\u2010related biomarker. MRI\u2010based radiomics had the potential to guide clinicians in MM management.<\/jats:p>","DOI":"10.1155\/2022\/6911246","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:50:45Z","timestamp":1662425445000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Radiomics Models Based on Magnetic Resonance Imaging for Prediction of the Response to Bortezomib\u2010Based Therapy in Patients with Multiple 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