{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T16:32:26Z","timestamp":1784910746705,"version":"3.55.0"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T00:00:00Z","timestamp":1700352000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005627","name":"Thrasher Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005627","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":[[2024,8]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Hypothesis<\/jats:title><jats:p>Smaller, leaner fetuses are malnourished and will experience unfavorable outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Prospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>40 SGA fetuses, 26 (61.9%) females: 10\/40 (25%) had obstetric interventions due to non\u2010reassuring fetal status (<jats:styled-content style=\"fixed-case\">NRFS<\/jats:styled-content>), and 17\/40 (42.5%) experienced adverse neonatal events (<jats:styled-content style=\"fixed-case\">CANO<\/jats:styled-content>). Participants underwent <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content> between gestational ages 30\u2009+\u20092 and 37\u2009+\u20092.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3\u2010T, True Fast Imaging with Steady State Free Precession (TruFISP) and T<jats:sub>1<\/jats:sub>\u2010weighted two\u2010point Dixon (T<jats:sub>1<\/jats:sub>W Dixon) sequences.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Total body volume (TBV), fat signal fraction (FSF), and the fat\u2010to\u2010body volumes ratio (FBVR) were extracted from TruFISP and T<jats:sub>1<\/jats:sub>W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Univariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. 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>FBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2\u20130.76) and FSF (OR 0.95, CI 0.91\u20130.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56\u20130.86) and FSF (OR 0.96, CI 0.93\u20130.99) were linked to CANO. The FBVR sensitivity\/specificity for obstetric interventions was 85.7%\/87.5%, and the TBV sensitivity\/specificity for CANO was 82.35%\/86.4%. The sonographic criteria sensitivity\/specificity for obstetric interventions was 100%\/33.3% and insignificant for CANO (<jats:italic>P<\/jats:italic>\u2009=\u20090.145).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Reduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>2<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 5<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29141","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T06:24:40Z","timestamp":1700461480000},"page":"767-774","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Fetal <scp>MRI<\/scp>\u2010Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification"],"prefix":"10.1002","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9406-6523","authenticated-orcid":false,"given":"Aviad","family":"Rabinowich","sequence":"first","affiliation":[{"name":"Sagol Brain Institute, Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"Department of Radiology Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"Faculty of Medicine Tel\u2010Aviv University  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0719-1327","authenticated-orcid":false,"given":"Netanell","family":"Avisdris","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"School of Computer Science and Engineering The Hebrew University of Jerusalem  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bossmat","family":"Yehuda","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"Sagol School of Neuroscience Tel\u2010Aviv University  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayala","family":"Zilberman","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Tel\u2010Aviv University  Tel\u2010Aviv Israel"},{"name":"Department of Obstetrics and Gynecology, Lis Hospital for Women Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamir","family":"Graziani","sequence":"additional","affiliation":[{"name":"Department of Radiology Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"Faculty of Medicine Tel\u2010Aviv University  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bar","family":"Neeman","sequence":"additional","affiliation":[{"name":"Department of Radiology Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"},{"name":"Faculty of Medicine Tel\u2010Aviv University  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bella","family":"Specktor\u2010Fadida","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering The Hebrew University of Jerusalem  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dafna","family":"Link\u2010Sourani","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel\u2010Aviv Sourasky Medical Center  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yair","family":"Wexler","sequence":"additional","affiliation":[{"name":"School of Neurobiology, Biochemistry and Biophysics, The George S. 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