{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:00:59Z","timestamp":1769191259047,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011019","name":"Nemzeti Kutat\u00e1si Fejleszt\u00e9si \u00e9s Innov\u00e1ci\u00f3s Hivatal","doi-asserted-by":"publisher","award":["129166"],"award-info":[{"award-number":["129166"]}],"id":[{"id":"10.13039\/501100011019","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005881","name":"Emberi Eroforr\u00e1sok Miniszt\u00e9riuma","doi-asserted-by":"publisher","award":["EFOP-3.6.1-16-2016-00022"],"award-info":[{"award-number":["EFOP-3.6.1-16-2016-00022"]}],"id":[{"id":"10.13039\/501100005881","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005881","name":"Emberi Eroforr\u00e1sok Miniszt\u00e9riuma","doi-asserted-by":"publisher","award":["TAMOP 4.2.4.A\/2-11-1-2012-0001"],"award-info":[{"award-number":["TAMOP 4.2.4.A\/2-11-1-2012-0001"]}],"id":[{"id":"10.13039\/501100005881","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017864","name":"Nemzetgazdas\u00e1gi Miniszt\u00e9rium","doi-asserted-by":"publisher","award":["GINOP-2.3.3-15-2016-00007"],"award-info":[{"award-number":["GINOP-2.3.3-15-2016-00007"]}],"id":[{"id":"10.13039\/100017864","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009232","name":"Debreceni Egyetem","doi-asserted-by":"publisher","award":["Szodoray \u00d6szt\u00f6nd\u00edj"],"award-info":[{"award-number":["Szodoray \u00d6szt\u00f6nd\u00edj"]}],"id":[{"id":"10.13039\/501100009232","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>The tumor grade of endometrioid endometrial cancer is used as an independent marker of prognosis and a key component in clinical decision making. It is reported that between grades 1 and 3, however, the intermediate grade 2 carries limited information; thus, patients with grade 2 tumors are at risk of both under- and overtreatment. We used RNA-sequencing data from the TCGA project and machine learning to develop a model which can correctly classify grade 1 and grade 3 samples. We used the trained model on grade 2 patients to subdivide them into low-risk and high-risk groups. With iterative retraining, we selected the most relevant 12 transcripts to build a simplified model without losing accuracy. Both models had a high AUC of 0.93. In both cases, there was a significant difference in the relapse-free survivals of the newly identified grade 2 subgroups. Both models could identify grade 2 patients that have a higher risk of relapse. Our approach overcomes the subjective components of the histological evaluation. The developed method can be automated to perform a prescreening of the samples before a final decision is made by pathologists. Our translational approach based on machine learning methods could allow for better therapeutic planning for grade 2 endometrial cancer patients.<\/jats:p>","DOI":"10.3390\/cancers13174348","type":"journal-article","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T21:45:16Z","timestamp":1630273516000},"page":"4348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8345-1314","authenticated-orcid":false,"given":"P\u00e9ter","family":"Gargya","sequence":"first","affiliation":[{"name":"Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6163-7190","authenticated-orcid":false,"given":"B\u00e1lint L\u00e1szl\u00f3","family":"B\u00e1lint","sequence":"additional","affiliation":[{"name":"Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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