{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:49:59Z","timestamp":1792093799701,"version":"4.4.0"},"reference-count":35,"publisher":"The Endocrine Society","issue":"2","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100004319","name":"Pfizer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Acromegaly, caused by excess GH and IGF-1 due to pituitary adenomas, often necessitates first-generation somatostatin receptor ligands (fgSRLs) therapy when surgery fails. However, responses to fgSRLs therapy vary widely.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a machine learning (ML)-based calculator that predicts individual responses to fgSRLs therapy, enabling evidence-based acromegaly management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>A retrospective study (January 2010-July 2024) utilizing the Research Patient Data Registry to evaluate 10 ML algorithms and create a predictive calculator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Single-center study conducted at Mass General Brigham-affiliated hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients<\/jats:title>\n                    <jats:p>One hundred eleven acromegaly patients met inclusion criteria, classified as fgSRLs-responsive (n = 64) or fgSRLs-resistant (n = 47).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p>IGF-1 trajectories were analyzed using linear mixed-effects modeling. Ten ML algorithms were assessed to predict fgSRLs resistance. SHapley Additive exPlanations (SHAP) analysis identified key predictors for the development of a web-based clinical calculator.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main Outcome Measures<\/jats:title>\n                    <jats:p>Model performance was primarily evaluated using area under the receiver operating characteristic curve (AUROC), along with accuracy, precision, recall, specificity, F1 score, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The CatBoost model exhibited optimal performance based on AUROC 0.896 (95% confidence interval: 0.751-0.990), with accuracy 82.4%, precision 86.7%, specificity 88.2%, and F1 score 81.2%. Key predictors of fgSRLs resistance identified via SHAP analysis included pre-fgSRLs treatment GH, Knosp grade, pre-fgSRLs treatment IGF-1 index, T2-weighted magnetic resonance imaging density, and comorbidity burden. The model demonstrated excellent calibration (Brier score 0.131) and clinical utility via DCA. A web-based calculator was developed for clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The CatBoost-based calculator effectively predicts fgSRLs treatment response in acromegaly patients. Prospective validation is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgaf375","type":"journal-article","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T07:49:31Z","timestamp":1750492171000},"page":"484-496","source":"Crossref","is-referenced-by-count":6,"title":["Integrative Machine Learning Approach for Predicting Resistance to First-generation Receptor Ligands in Acromegaly"],"prefix":"10.1210","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-8752","authenticated-orcid":false,"given":"Wei","family":"Lin","sequence":"first","affiliation":[{"name":"Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Harvard Medical School , Boston, MA 02115 ,","place":["USA"]},{"name":"Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital , Fuzhou, Fujian 350001 ,","place":["P. 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