{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:55:16Z","timestamp":1789390516477,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12090024"],"award-info":[{"award-number":["12090024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>MRI\u2010based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound\u2010based measurements. However, these are still limited by time\u2010consuming manual annotation in MRI data and the lack of mother\u2010based information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2\u2010weighted imaging (T2WI) and multifeature fusion.<\/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>274 pregnant women (29.5\u2009\u20094.0\u2009years) from two centers were included and randomly divided into training (N\u2009=\u2009119), internal test (N\u2009=\u200940), time\u2010independent validation (N\u2009=\u200943), and external validation (N\u2009=\u200972) sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p><jats:styled-content style=\"fixed-case\">1.5\u2010T<\/jats:styled-content>, <jats:styled-content style=\"fixed-case\">T2WI half\u2010Fourier<\/jats:styled-content> acquisition single\u2010shot turbo spin\u2010echo pulse sequence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>First, the placentas on T2WI were manually annotated, and a deep learning model was developed to automatically segment the placentas. Then, the radiomic features were extracted from the placentas and selected by three\u2010step feature selection. In addition, fetus\u2010based measurement features and mother\u2010based clinical features were obtained from ultrasound examinations and medical records, respectively. Finally, a hybrid model based on random forest was constructed by fusing these features, and further compared with models based on other machine learning methods and different feature combinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>The performances of placenta segmentation and FGR assessment were evaluated by Dice similarity coefficient (DSC) and the area under the receiver operating characteristic curve (AUROC), respectively. A <jats:italic>P<\/jats:italic>\u2010value &lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The placentas were automatically segmented with an average DSC of 90.0%. The hybrid model achieved an AUROC of 0.923, 0.931, and 0.880 on the internal test, time\u2010independent validation, and external validation sets, respectively. The mother\u2010based clinical features resulted in significant performance improvements for FGR assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The proposed hybrid model may be able to assess FGR with high accuracy. Furthermore, information complementation based on placental, fetal, and maternal features could also lead to better FGR assessment performance.<\/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.29399","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T11:30:25Z","timestamp":1713958225000},"page":"494-504","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on <scp>T2<\/scp>\u2010Weighted <scp>MRI<\/scp> and Multifeature Fusion"],"prefix":"10.1002","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9133-8880","authenticated-orcid":false,"given":"Ruikun","family":"Li","sequence":"first","affiliation":[{"name":"Department of Automation Shanghai Jiao Tong University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhen","family":"Song","sequence":"additional","affiliation":[{"name":"The International Peace Maternity and Child Health Hospital Shanghai Jiao Tong University School of Medicine  Shanghai China"},{"name":"Shanghai Key Laboratory of Embryo Original Diseases  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology Obstetrics and Gynecology Hospital of Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Wu","sequence":"additional","affiliation":[{"name":"The International 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