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Prognostic models for locally advanced cervical cancer: external validation of the published models

dc.contributor.authorLora Pablos, David
dc.contributor.authorGómez De La Cámara, Agustín Ramón
dc.contributor.authorPedraza Fernández, Sara
dc.contributor.authorEnríquez De Salamanca Lorente, Rafael
dc.contributor.authorPérez-Regadera Gómez, José Fermín
dc.date.accessioned2025-01-24T12:03:57Z
dc.date.available2025-01-24T12:03:57Z
dc.date.issued2017-09
dc.description.abstractObjective: To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independient cohort. Methods: A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified regarding discrimination, calibration, measures of overall performance, and decision curve analyses. Results: The review identified 8 studies containing 13 prognostic models. Different (International Federation of Gynecology and Obstetrics [FIGO] stages, parametrium involvement, hydronephrosis, location of positive nodes, and race) but related cohorts with validation cohort (5-year overall survival [OS]=70%; 5-year disease-free survival [DFS]=64%; average age of 50; and over 79% squamous cell) were evaluated. The following models exhibited good external validity in terms of discrimination and calibration but limited clinical utility: the OS model at 3 year from Kidd et al.'s study (area under the receiver operating characteristic curve [AUROC]=0.69; threshold of clinical utility [TCU] between 36% and 50%), the models of DFS at 1 year from Kidd et al.'s study (AUROC=0.64; TCU between 24% and 32%) and 2 years from Rose et al.'s study (AUROC=0.70; TCU between 19% and 58%) and the distant recurrence model at 5 years from Kang et al.'s study (AUROC=0.67; TCU between 12% and 36%). Conclusion: The external validation revealed the statistical and clinical usefulness of 4 prognostic models published in the literature.
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.citationLora D, Gómez de la Cámara A, Fernández SP, Enríquez de Salamanca R, Gómez JF. Prognostic models for locally advanced cervical cancer: external validation of the published models. J Gynecol Oncol. 2017 May;28(5):e58. https://doi.org/10.3802/jgo.2017.28.e58
dc.identifier.doi10.3802/jgo.2017.28.e58
dc.identifier.essn2005-0399
dc.identifier.issn2005-0380
dc.identifier.officialurlhttps://doi.org/10.3802/jgo.2017.28.e58
dc.identifier.relatedurlhttps://m.ejgo.org/search.php?where=aview&id=10.3802/jgo.2017.28.e58&code=1114JGO&vmode=FT
dc.identifier.urihttps://hdl.handle.net/20.500.14352/116024
dc.issue.number5
dc.journal.titleJournal of Gynecologic Oncology
dc.language.isoeng
dc.page.final13
dc.page.initial1
dc.publisherAsian Society of Gynecologic Oncology
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu519.2
dc.subject.cdu616-006.04
dc.subject.cdu618.1
dc.subject.keywordUterine Cervical Neoplasms
dc.subject.keywordChemoradiotherapy
dc.subject.keywordPrognosis
dc.subject.keywordProportional Hazards Models
dc.subject.keywordValidation Studies
dc.subject.ucmEstadística
dc.subject.ucmGinecología y obstetricia
dc.subject.ucmOncología
dc.subject.unesco3201.01 Oncología
dc.subject.unesco3201.08 Ginecología
dc.subject.unesco1209.14 Técnicas de Predicción Estadística
dc.titlePrognostic models for locally advanced cervical cancer: external validation of the published models
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number28
dspace.entity.typePublication
relation.isAuthorOfPublication353fa834-f356-4174-bdb0-cbf7e3359647
relation.isAuthorOfPublicationcc2ced6e-edd7-4483-ad4b-a07dc42187b8
relation.isAuthorOfPublication405859b3-08ac-4840-be69-6a43a1d088bd
relation.isAuthorOfPublicationcf29a29e-c778-46f7-9c83-86f264038764
relation.isAuthorOfPublication.latestForDiscoverycc2ced6e-edd7-4483-ad4b-a07dc42187b8

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Supplementary 5. Graphical approaches to visualize the predictive ability of the evaluated models
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