{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:49:26Z","timestamp":1789498166802,"version":"build-2803163510"},"reference-count":30,"publisher":"The Endocrine Society","issue":"10","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU COST Action CA20122"},{"DOI":"10.13039\/100020277","name":"The Society of Endocrinology and Metabolism of T\u00fcrkiye","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020277","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Context<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To improve ACC prognostic classification by applying robust machine learning (ML) models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Method<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1210\/clinem\/dgaf096","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T15:25:25Z","timestamp":1739373925000},"page":"e3185-e3192","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning\u2013Based Survival Prediction Tool for Adrenocortical Carcinoma"],"prefix":"10.1210","volume":"110","author":[{"given":"Emre Sedar","family":"Saygili","sequence":"first","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Canakkale Onsekiz Mart University , Canakkale 17020 ,","place":["Turkey"]},{"name":"Department of Metabolism and Systems Science, College of Medicine and Health, University of Birmingham , Birmingham B15 2TT 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