{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T09:40:14Z","timestamp":1777887614929,"version":"3.51.4"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and internally validate an interpretable, non-invasive machine learning framework to predict detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study enrolled 538 urodynamically evaluated BPH patients. A rigorous multidimensional feature selection pipeline (LASSO, Boruta, and Recursive Feature Elimination) distilled 15 baseline clinical, anatomical, and uroflowmetry parameters into a parsimonious five-feature subset. Five supervised machine learning algorithms were trained and systematically compared. Shapley Additive exPlanations (SHAP) analysis was integrated for global and local interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The optimized XGBoost model demonstrated superior discriminatory performance (AUC\u202f=\u202f0.958), significantly outperforming traditional multivariable logistic regression (AUC\u202f=\u202f0.787). XGBoost consistently exhibited superior calibration and higher net clinical benefit across varied threshold probabilities. Crucially, SHAP global dependence plots revealed non-linear pathological trajectories, notably demonstrating a U-shaped risk profile for bladder wall thickness (BWT) that was not captured by classical linear statistical detection. Local SHAP visualizations effectively translated complex probabilistic outputs into individualized clinical reasoning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The interpretable XGBoost framework serves as a robust non-invasive risk stratification tool for DU, decoding complex non-linear clinical interactions. This algorithm holds significant potential to optimize preoperative patient selection and mitigate surgical failures in borderline clinical scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>Identifier 2026-048.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1835415","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:18:51Z","timestamp":1777609131000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-invasive prediction of detrusor underactivity in benign prostatic hyperplasia: an interpretable machine learning framework to optimize surgical selection"],"prefix":"10.3389","volume":"13","author":[{"given":"Long","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeming","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyong","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoshuang","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Panzhihua Central Hospital","place":["Panzhihua, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"416","DOI":"10.3390\/jcm10030416","article-title":"The investigative role of statins in ameliorating lower urinary tract symptoms (LUTS): a systematic review","volume":"10","author":"Russo","year":"2021","journal-title":"J Clin 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