{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:12:39Z","timestamp":1788757959753,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T00:00:00Z","timestamp":1778976000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Endocrinology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Individual responses to recombinant human growth hormone (rhGH) therapy vary widely among children with idiopathic growth hormone deficiency (iGHD) and idiopathic short stature (ISS), making accurate prediction of treatment outcomes clinically important. This study aimed to develop and compare machine learning (ML)\u2010based and conventional statistical models to predict short\u2010term growth response and mid\u2010parental height (MPH) attainment following rhGH therapy in iGHD and ISS patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective observational cohort study using a nationwide, real\u2010world registry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients<\/jats:title>\n                    <jats:p>A total of 2215 children (1877 with iGHD and 338 with ISS) treated with rhGH were identified from the Korean LG Growth Study database. All included patients had at least 1 year of follow\u2010up with available clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>\n                      Primary outcomes were 1\u2010 and 2\u2010year changes in height SDS (\u0394HSDS) and achievement of MPH SDS. Predictive models included multiple linear regression, logistic regression, Random Forest, eXtreme Gradient Boosting, and Elastic Net. Model performance was evaluated using\n                      <jats:italic>R<\/jats:italic>\n                      \u00b2, error metrics and area under the receiver operating characteristic curve. Model interpretability was assessed using SHAP values.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the iGHD group, ensemble ML models modestly outperformed linear regression for predicting 1\u2010year \u0394HSDS (\n                      <jats:italic>R<\/jats:italic>\n                      \u00b2\u2009\u2248\u20090.19 vs. 0.16), but predictive performance declined at 2 years across all models. Prediction of MPH attainment showed high specificity but very low sensitivity at 1 year, with no clear advantage of ML over logistic regression. In ISS patients, all models demonstrated poor predictive performance for both \u0394HSDS and MPH attainment, reflecting substantial clinical heterogeneity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML approaches provided limited but clinically meaningful improvements in predicting short\u2010term growth response in iGHD, while offering no clear benefit in ISS. These findings highlight both the potential and limitations of ML models based solely on routine clinical variables and underscore the need for integrating multimodal data to improve growth prediction, particularly in heterogeneous ISS populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/cen.70163","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T05:31:52Z","timestamp":1779082312000},"page":"322-330","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Growth Hormone Treatment Response and Machine Learning\u2010Based Prediction in Idiopathic GHD and ISS: Analysis of the Korean LG Growth Study"],"prefix":"10.1111","volume":"105","author":[{"given":"Jisun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Pediatrics, Inha University Hospital Inha University College of Medicine Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Young","family":"Joo","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Inha University Hospital Inha University College of Medicine Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Inha University Hospital Inha University College of Medicine Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung Ji","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Inha University Hospital Inha University College of Medicine Incheon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-0015","authenticated-orcid":false,"given":"Ji Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Inha University 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