{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T02:23:18Z","timestamp":1791771798608,"version":"4.3.4"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop a cascaded deep semantic\u2010radiomic\u2010clinical (DRC) model for diagnosing PAS and its subtypes based on T2\u2010weighted MRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>361 pregnant women (mean age: 33.10\u2009\u00b1\u20094.37\u2009years), suspected of PAS, divided into segment training cohort (<jats:italic>N<\/jats:italic>\u2009=\u200940), internal training cohort (<jats:italic>N<\/jats:italic>\u2009=\u2009139), internal testing cohort (<jats:italic>N<\/jats:italic>\u2009=\u200960), and external testing cohort (<jats:italic>N<\/jats:italic>\u2009=\u2009122).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>Coronal T2\u2010weighted sequence at 1.5\u2009T and 3.0\u2009T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>AUC, ACC, Student's <jats:italic>t<\/jats:italic>\u2010test, the Mann\u2013Whitney U test, chi\u2010squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer\u2013Lemeshow test, decision curve analysis, DeLong test, and McNemar test. <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05 indicated a significant difference.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In PAS diagnosis, the DRC\u20101 outperformed than other models (AUC\u2009=\u20090.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC\u20102 model performed similarly with radiologists (<jats:italic>P<\/jats:italic>\u2009=\u20090.773 and 0.579 in the internal testing cohort and <jats:italic>P<\/jats:italic>\u2009=\u20090.429 and 0.874 in the external testing cohort, respectively).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29317","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T12:09:54Z","timestamp":1708690194000},"page":"2705-2715","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Learning Radiomic Analysis of <scp>MRI<\/scp> Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes"],"prefix":"10.1002","volume":"60","author":[{"given":"Changye","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Radiology The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital)  Dongguan Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6335-5309","authenticated-orcid":false,"given":"Jian","family":"Zhong","sequence":"additional","affiliation":[{"name":"Medical AI Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University  Shenzhen China"},{"name":"Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Wang","sequence":"additional","affiliation":[{"name":"Dongguan Maternal and Child Health Care Hospital  Dongguan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangyang","family":"Cao","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences Macao Polytechnic University  Macao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Medical AI Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University  Shenzhen China"},{"name":"Guangdong Key Laboratory of Biomedical Measurements and Ultrasound 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