{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T22:48:23Z","timestamp":1789426103725,"version":"build-2803163510"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"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":[[2021,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To discriminate between patients with MI\u2009\u2265\u200950% using a machine learning\u2010based model combining texture features and descriptors from preoperatively MR images.<\/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>One hundred forty\u2010three women with endometrial cancer were included. The series was split into training (<jats:italic>n<\/jats:italic>\u00a0=\u2009107, 46 with MI\u2009\u2265\u200950%) and test (<jats:italic>n<\/jats:italic>\u00a0=\u200936, 16 with MI\u2009\u2265\u200950%) cohorts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequences<\/jats:title><jats:p>Fast spin echo T2\u2010weighted (T2W), diffusion\u2010weighted (DW), and T1\u2010weighted gradient echo dynamic contrast\u2010enhanced (DCE) sequences were obtained at 1.5 or 3\u2009T magnets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Tumors were manually segmented slice\u2010by\u2010slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash\u2010in slope, wash\u2010out slope, initial area under the curve at 60\u2009sec and at 90\u2009sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single\u2010sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Test<\/jats:title><jats:p>Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI\u2009\u2265\u200950% in the test cohort (accuracy\u00a0=\u00a086.1% and AUROC\u00a0=\u00a087.1%). The rest of diagnostic models showed a worse accuracy (accuracy\u00a0=\u00a041.67%\u201363.89% and AUROC\u00a0=\u00a041.43%\u201363.13%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The model combining the texture features from T2W and ADC map images with the semi\u2010quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence\u2009Level<\/jats:title><jats:p>4<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical\u2009Efficacy<\/jats:title><jats:p>Stage\u20093<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27625","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T12:59:20Z","timestamp":1617281960000},"page":"987-995","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Machine Learning\u2010Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer"],"prefix":"10.1002","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4781-6368","authenticated-orcid":false,"given":"Alejandro","family":"Rodr\u00edguez\u2010Ortega","sequence":"first","affiliation":[{"name":"Biomedical Imaging Research Group (GIBI2<sup>30<\/sup>), Hospital Universitario y Polit\u00e9cnico e Instituto de Investigaci\u00f3n Sanitaria La Fe Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Alegre","sequence":"additional","affiliation":[{"name":"Radiology Department, Hospital Universitario y Polit\u00e9cnico La Fe Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00edctor","family":"Lago","sequence":"additional","affiliation":[{"name":"Gynecologic Oncology Department, Hospital Universitario y Polit\u00e9cnico La Fe Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Miguel","family":"Carot\u2010Sierra","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia. 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