Machine Learning-Based Survival Prediction Tool for Adrenocortical Carcinoma

Basit Öğe Kaydı
dc.authoridAltieri, Barbara/0000-0003-2616-3249
dc.authoridElhassan, Yasir/0000-0002-2735-6053
dc.authoridSAYGILI, Emre Sedar/0000-0003-0022-5704
dc.authoridRonchi, Cristina/0000-0001-5020-2071
dc.authoridPrete, Alessandro/0000-0002-4821-0336
dc.contributor.authorSaygili, Emre Sedar
dc.contributor.authorElhassan, Yasir S.
dc.contributor.authorPrete, Alessandro
dc.contributor.authorLippert, Juliane
dc.contributor.authorAltieri, Barbara
dc.contributor.authorRonchi, Cristina L.
dc.date.accessioned2025-05-29T02:57:38Z
dc.date.available2025-05-29T02:57:38Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractContext Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with difficult to predict clinical outcomes. The S-GRAS score combines clinical and histopathological variables (tumor stage, grade, resection status, age, and symptoms) and showed good prognostic performance for patients with ACC.Objective To improve ACC prognostic classification by applying robust machine learning (ML) models.Method We developed ML models to enhance outcome prediction using the published S-GRAS dataset (n = 942) as the training cohort and an independent dataset (n = 152) for validation. Sixteen ML models were constructed based on individual clinical variables. The best-performing models were used to develop a web-based tool for individualized risk prediction.Results Quadratic Discriminant Analysis, Light Gradient Boosting Machine, and AdaBoost Classifier models exhibited the highest performance, predicting 5-year overall mortality (OM), and 1-year and 3-year disease progression (DP) with F1 scores of 0.79, 0.63, and 0.83 in the training cohort, and 0.72, 0.60, and 0.83 in the validation cohort. Sensitivity and specificity for 5-year OM were at 77% and 77% in the training cohort, and 65% and 81% in the validation cohort, respectively. A web-based tool (https://acc-survival.streamlit.app) was developed for easily applicable and individualized risk prediction of mortality and disease progression.Conclusion S-GRAS parameters can efficiently predict outcome in patients with ACC, even using a robust ML model approach. Our web app instantly estimates the mortality and disease progression for patients with ACC, representing an accessible tool to drive personalized management decisions in clinical practice.
dc.description.sponsorshipEU COST Action CA20122 [CA20122]; EU COST Action
dc.description.sponsorshipWe thank our specialist nurse Miriam Asia and all the core members of the University Hospitals Birmingham Adrenal Tumor Multidisciplinary Team for the management of patients with adrenocortical carcinoma. We also thank the EU COST Action CA20122 Harmonisation for supportive networking (www.goharmonisation.com).
dc.identifier.doi10.1210/clinem/dgaf096
dc.identifier.issn0021-972X
dc.identifier.issn1945-7197
dc.identifier.pmid39950976
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1210/clinem/dgaf096
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30107
dc.identifier.wosWOS:001433230100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherEndocrine Soc
dc.relation.ispartofJournal of Clinical Endocrinology & Metabolism
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectmodel
dc.subjectadrenal cancer
dc.subjectmortality
dc.subjectprognosis
dc.subjectprecision medicine
dc.titleMachine Learning-Based Survival Prediction Tool for Adrenocortical Carcinoma
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu