{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T20:03:00Z","timestamp":1793995380905,"version":"4.8.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Leo Mintz grant from the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Existing sonographic approaches offer limited predictive accuracy. Combining fetal MRI, ultrasound and clinical data may improve perinatal prognostication.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>To evaluate whether integrating prenatal MRI, ultrasound, and clinical features using machine learning (ML) improves prediction of adverse perinatal outcomes in FGR or small-for-gestational-age (SGA) pregnancies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>This single-center study included prospectively enrolled FGR\/SGA and retrospectively included appropriate-for-gestational-age cases, with follow-up through neonatal discharge. Twenty-seven features from MRI, ultrasound, and clinical data were used in the final analysis. Seven ML classifiers were trained using stratified 5-fold cross-validation to predict composite adverse neonatal outcomes (CANO) and non-reassuring fetal status (NRFS). Sensitivity and specificity of the top-performing model (based on area under the curve [AUC]) were compared to standard biometric thresholds (estimated fetal weight and\/or abdominal circumference &amp;lt;10th\/&amp;lt;3rd centiles). Multiparametric, MRI-only, and ultrasound-only models were compared, along with reduced models using 4 features for CANO and 2 for NRFS.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>One hundred thirty-one participants were included (60 FGR\/SGA, 71 appropriate-for-gestational-age). The random forest method achieved the highest AUC for predicting CANO (0.912; 95% confidence interval [CI], 0.83\u20130.99) and NRFS (0.834; 95% CI, 0.76\u20130.91). For CANO, the multiparametric model demonstrated a 25% higher sensitivity (P\u2009=\u20090.005) and 17% higher specificity (P\u2009&amp;lt;\u20090.001) compared with the 3rd centile threshold, and improved specificity over the 10th centile threshold by 29% (P\u2009&amp;lt;\u20090.001). Sensitivity did not differ significantly from the 10th centile threshold (P\u2009=\u20090.366). For NRFS, specificity increased by 26% and 40% over the 3rd and 10th centile thresholds, respectively (P\u2009&amp;lt;\u20090.001), without significant differences in sensitivity (P\u2009=\u20091). No statistically significant differences were observed between the multiparametric, ultrasound-only, and MRI-only models (P\u2009\u2265\u20090.826), or between full and reduced models (P\u2009\u2265\u20090.313).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>ML-based models integrating multimodal data may improve risk stratification for predicting adverse perinatal outcomes in FGR\/SGA pregnancies.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/radadv\/umaf030","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T17:39:24Z","timestamp":1756748364000},"source":"Crossref","is-referenced-by-count":9,"title":["Predicting adverse perinatal outcomes in small-for-gestational-age fetuses using MRI, ultrasound, and clinical data"],"prefix":"10.1093","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9406-6523","authenticated-orcid":false,"given":"Aviad","family":"Rabinowich","sequence":"first","affiliation":[{"name":"Sagol Brain Institute, Tel Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]},{"name":"Department of Radiology, Tel Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]},{"name":"Gray Faculty of Medical and Health Sciences, Tel Aviv University , Tel Aviv, 6997801,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Olender","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The Hebrew University of Jerusalem , Jerusalem, 9190401,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Netanell","family":"Avisdris","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]},{"name":"School of Computer Science and Engineering, The Hebrew University of Jerusalem , Jerusalem, 9190401,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yair","family":"Wexler","sequence":"additional","affiliation":[{"name":"School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel Aviv University , Tel Aviv, 6997801,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-1498","authenticated-orcid":false,"given":"Bossmat","family":"Yehuda","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]},{"name":"Sagol School of Neuroscience, Tel Aviv University , Tel Aviv, 6997801,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon","family":"Vanetik","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences, Tel Aviv University , Tel Aviv, 6997801,","place":["Israel"]},{"name":"Department of Pediatrics, Dana Dwek Children\u2019s Hospital, Tel Aviv Sourasky Medical Center , Tel-Aviv, 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6997801,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bella","family":"Specktor-Fadida","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Sciences, University of Haifa , Haifa, 3103301,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daphna","family":"Link-Sourani","sequence":"additional","affiliation":[{"name":"Sagol Brain Institute, Tel Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacky","family":"Herzlich","sequence":"additional","affiliation":[{"name":"Gray Faculty of Medical and Health Sciences, Tel Aviv University , Tel Aviv, 6997801,","place":["Israel"]},{"name":"Neonatal Intensive Care Unit, Dana Dwek Children\u2019s Hospital, Tel-Aviv Sourasky Medical Center , Tel Aviv, 6423906,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karina","family":"Krajden 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