{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:55:20Z","timestamp":1789390520767,"version":"build-2803163510"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Pediatr"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Prenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85).<\/jats:p>\n                  <jats:p>\n                    <jats:italic>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Conclusion<\/jats:italic>\n                    : Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:italic>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Trial registration<\/jats:italic>\n                    :\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/clinicaltrials.gov\/ct2\/show\/NCT04609163?term=NCT04609163&amp;draw=2&amp;rank=1\">https:\/\/clinicaltrials.gov\/ct2\/show\/NCT04609163?term=NCT04609163&amp;draw=2&amp;rank=1<\/jats:ext-link>\n                    ; Clinical Trial Identification no. NCT04609163.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:table-wrap>\n                      <jats:table>\n                        <jats:tbody>\n                          <jats:tr>\n                            <jats:td colspan=\"2\">\n                              <jats:bold>What is Known:<\/jats:bold>\n                              <jats:italic>\u2022\u00a0Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management.<\/jats:italic>\n                              <jats:italic>\u2022\u00a0The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator.<\/jats:italic>\n                            <\/jats:td>\n                          <\/jats:tr>\n                          <jats:tr>\n                            <jats:td colspan=\"2\">\n                              <jats:bold>What is New:<\/jats:bold>\n                              <jats:italic>\u2022\u00a0MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results.<\/jats:italic>\n                              <jats:italic>\u2022\u00a0A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.<\/jats:italic>\n                            <\/jats:td>\n                          <\/jats:tr>\n                        <\/jats:tbody>\n                      <\/jats:table>\n                    <\/jats:table-wrap>\n                  <\/jats:p>","DOI":"10.1007\/s00431-024-05476-9","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T01:02:21Z","timestamp":1709082141000},"page":"2285-2300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation"],"prefix":"10.1007","volume":"183","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8741-3478","authenticated-orcid":false,"given":"Luana","family":"Conte","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2650-0084","authenticated-orcid":false,"given":"Ilaria","family":"Amodeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1998-0286","authenticated-orcid":false,"given":"Giorgio","family":"De Nunzio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-9394","authenticated-orcid":false,"given":"Genny","family":"Raffaeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0386-4712","authenticated-orcid":false,"given":"Irene","family":"Borzani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9028-9597","authenticated-orcid":false,"given":"Nicola","family":"Persico","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2947-4627","authenticated-orcid":false,"given":"Alice","family":"Griggio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2120-0427","authenticated-orcid":false,"given":"Giuseppe","family":"Como","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6522-1259","authenticated-orcid":false,"given":"Donato","family":"Cascio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariarosa","family":"Colnaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-0299","authenticated-orcid":false,"given":"Fabio","family":"Mosca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4921-1437","authenticated-orcid":false,"given":"Giacomo","family":"Cavallaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"5476_CR1","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.siny.2017.11.002","volume":"22","author":"FM Russo","year":"2017","unstructured":"Russo FM, De Coppi P, Allegaert K et al (2017) Current and future antenatal management of isolated congenital diaphragmatic hernia. 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Int J Environ Res Public Health 15:2509. https:\/\/doi.org\/10.3390\/ijerph15112509","journal-title":"Int J Environ Res Public Health"}],"container-title":["European Journal of Pediatrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00431-024-05476-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00431-024-05476-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00431-024-05476-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:23:39Z","timestamp":1713745419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00431-024-05476-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["5476"],"URL":"https:\/\/doi.org\/10.1007\/s00431-024-05476-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3763993\/v1","asserted-by":"object"}]},"ISSN":["1432-1076"],"issn-type":[{"value":"1432-1076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]},"assertion":[{"value":"16 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The present study was done on the principles of good clinical practice and the Helsinki Declaration. This study was approved by the local ethics committee (Milan Area 2, Italy) with approval number\/ID 800_2020bis. However, due to the study retrospective nature, informed consent was waived by the Ethics Committee. The study was registered at ClinicalTrials.gov with the identifier NCT04609163.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Written informed consent was obtained from the parents.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}