{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T16:24:14Z","timestamp":1789662254372,"version":"4.0.1"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Accurate preoperative characterization of breast lesions is important for individualized management. The conventional monoexponential (Mono) model has limited ability to capture the complex microstructure of lesions. Non\u2010Gaussian diffusion models, including continuous\u2010time random walk (CTRW) and fractional\u2010order calculus (FROC), may overcome this limitation, but their value in predicting biomarkers and molecular subtypes remains unclear.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To comprehensively evaluate the diagnostic utility of the Mono, CTRW, and FROC models and their derived parameters for preoperative breast lesion characterization.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Prospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n 189 women (mean age, 47.4\u2009\u00b1\u200911.2\u2009years) with 238 histopathologically confirmed breast lesions (138 benign, 100 malignant).<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n \n 3.0\u2010T MRI; single\u2010shot spin\u2010echo echo\u2010planar imaging for multi\u2010\n b<\/jats:italic>\n \u2010value diffusion\u2010weighted imaging (10\n b<\/jats:italic>\n \u2010values ranging from 0 to 3000\u2009s\/mm\n 2<\/jats:sup>\n ).\n <\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Diffusion parameters were extracted from whole\u2010lesion volumes generated from slice\u2010wise segmentations by two independent, blinded breast radiologists. Multiparameter joint models were constructed using logistic regression.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Model performance was evaluated using repeated 5\u2010fold cross\u2010validation and patient\u2010level cluster bootstrap for AUC comparisons. A two\u2010sided\n p<\/jats:italic>\n \u2009<\u20090.05 was considered statistically significant, and Bonferroni\u2010adjusted thresholds were applied for multiple comparisons.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n For benign\u2013malignant differentiation, the CTRW and FROC models yielded higher cross\u2010validated AUCs than the Mono model (0.928, 0.918, and 0.865, respectively). For prognostic biomarkers, the FROC model achieved optimal predictive performance for HER2 status (AUC\u2009=\u20090.836). For molecular subtyping, the CTRW model significantly outperformed the Mono model in distinguishing HER2\u2010positive lesions (AUC\u2009=\u20090.848 vs. 0.674); however, all models showed limited performance for triple\u2010negative lesions, with all AUCs \u2264\u20090.502.<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n Non\u2010Gaussian diffusion models, particularly the CTRW model, provide additional value beyond the Mono model for preoperative breast lesion characterization.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 2.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70543","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T09:10:11Z","timestamp":1789549811000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of Continuous\u2010Time Random Walk and Fractional Order Calculus Diffusion Models for Preoperative Breast Lesion Characterization: Diagnosis, Biomarker Prediction and Molecular Subtyping"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9831-9966","authenticated-orcid":false,"given":"Yimeng","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaomin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhexuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5816-3662","authenticated-orcid":false,"given":"Xueyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shifang","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Information The Third Affiliated Hospital of Zhengzhou University Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjia","family":"Wang","sequence":"additional","affiliation":[{"name":"GE HealthCare MR Research Center China Beijing 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