{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(1)"],"submitter":["Alshwayyat S"],"pubmed_abstract":["<h4>Background</h4>One in every 41 women develops malignant melanoma in their lifetime, with noncutaneous melanomas arising in areas such as the genitourinary (GU) system being particularly rare and aggressive. We used machine learning (ML) to build prognostic models for vaginal (VaM) and vulvar (VuM) melanomas and developed the first predictive web-based tool for survival in these cancers.<h4>Methods</h4>We leveraged the SEER database (2000-2020) to assemble our cohort and extract relevant clinical and demographic variables. Prognostic factors were screened using univariate and multivariate Cox proportional hazards regression analyses. Subsequently, we developed five machine-learning classifiers to predict 5-year survival. The discrimination of each model was assessed using the area under"],"journal":["Future science OA"],"pagination":["2540747"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12452445"],"repository":["biostudies-literature"],"pubmed_title":["Web-based predictive tool for vaginal and vulvar melanomas: a machine learning study."],"pmcid":["PMC12452445"],"pubmed_authors":["Alshwayyat M","Talafha M","Alshwayyat TA","Haddadin S","Haddadin Z","Muhaidat J","Alshwayyat S","Hanifa H"],"additional_accession":[]},"is_claimable":false,"name":"Web-based predictive tool for vaginal and vulvar melanomas: a machine learning study.","description":"<h4>Background</h4>One in every 41 women develops malignant melanoma in their lifetime, with noncutaneous melanomas arising in areas such as the genitourinary (GU) system being particularly rare and aggressive. We used machine learning (ML) to build prognostic models for vaginal (VaM) and vulvar (VuM) melanomas and developed the first predictive web-based tool for survival in these cancers.<h4>Methods</h4>We leveraged the SEER database (2000-2020) to assemble our cohort and extract relevant clinical and demographic variables. Prognostic factors were screened using univariate and multivariate Cox proportional hazards regression analyses. Subsequently, we developed five machine-learning classifiers to predict 5-year survival. The discrimination of each model was assessed using the area under","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-03T19:15:54.594Z","creation":"2026-04-30T03:11:21.148Z"},"accession":"S-EPMC12452445","cross_references":{"pubmed":["40961395"],"doi":["10.1080/20565623.2025.2540747"]}}