Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis

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dc.authoridyordanov, angel/0000-0002-7719-382X
dc.authoridDi Giuseppe, Jacopo/0000-0002-6162-6299
dc.authoridCiavattini, Andrea/0000-0002-8037-4947
dc.authoridDi Giuseppe, Jacopo/0000-0002-6162-6299
dc.authoridAbdo, Mohammed S./0000-0001-9085-324X
dc.authoridCoronado, Pluvio J/0000-0003-0357-2015
dc.authoridFerrari, Federico/0000-0001-7065-2432
dc.authorwosidyordanov, angel/AAH-5570-2019
dc.authorwosidDi Giuseppe, Jacopo/HJP-5573-2023
dc.authorwosidOnal, Cem/HOC-5611-2023
dc.authorwosidCiavattini, Andrea/AAC-3266-2022
dc.authorwosidDi Giuseppe, Jacopo/IZP-9523-2023
dc.authorwosidAbdo, Mohammed S./AAF-6594-2019
dc.authorwosidkaraman, erbil/AFU-7129-2022
dc.contributor.authorShazly, Sherif A.
dc.contributor.authorCoronado, Pluvio J.
dc.contributor.authorYilmaz, Ercan
dc.contributor.authorMelekoglu, Rauf
dc.contributor.authorSahin, Hanifi
dc.contributor.authorGiannella, Luca
dc.contributor.authorCiavattini, Andrea
dc.date.accessioned2024-08-04T20:53:22Z
dc.date.available2024-08-04T20:53:22Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjectiveTo establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. MethodsA multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). ResultsOf 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. ConclusionThe Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.en_US
dc.identifier.doi10.1002/ijgo.14639
dc.identifier.endpage768en_US
dc.identifier.issn0020-7292
dc.identifier.issn1879-3479
dc.identifier.issue3en_US
dc.identifier.pmid36572053en_US
dc.identifier.scopus2-s2.0-85146999673en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage760en_US
dc.identifier.urihttps://doi.org/10.1002/ijgo.14639
dc.identifier.urihttps://hdl.handle.net/11616/101139
dc.identifier.volume161en_US
dc.identifier.wosWOS:000915340000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Gynecology & Obstetricsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDisease-free survivalen_US
dc.subjectOverall survivalen_US
dc.subjectUterine canceren_US
dc.titleEndometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosisen_US
dc.typeArticleen_US

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