{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:55:20Z","timestamp":1789584920259,"version":"build-2803163510"},"reference-count":38,"publisher":"The Endocrine Society","issue":"8","license":[{"start":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T00:00:00Z","timestamp":1615593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Basic Science Research Program"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2020R1I1A1A01071648"],"award-info":[{"award-number":["2020R1I1A1A01071648"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008005","name":"Yonsei University College of Medicine","doi-asserted-by":"publisher","award":["6-2020-0224"],"award-info":[{"award-number":["6-2020-0224"]}],"id":[{"id":"10.13039\/501100008005","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Severance Hospital, Seoul, Korea.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients<\/jats:title>\n                    <jats:p>A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n\u2005=\u2005141) and test (n\u2005=\u200536) sets. Radiomic features (n\u2005=\u2005107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74\u20130.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgab159","type":"journal-article","created":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T12:21:58Z","timestamp":1615638118000},"page":"e3069-e3077","source":"Crossref","is-referenced-by-count":40,"title":["Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma"],"prefix":"10.1210","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8907-5401","authenticated-orcid":false,"given":"Yae Won","family":"Park","sequence":"first","affiliation":[{"name":"Department of Radiology and Research Institute of 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