{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T23:16:05Z","timestamp":1789168565715,"version":"build-2803163510"},"reference-count":33,"publisher":"Wiley","issue":"13","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"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":["The Prostate"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objective<\/jats:title>\n                    <jats:p>Metastatic hormone\u2010sensitive prostate cancer (mHSPC) exhibits heterogeneous progression patterns, with early progression to metastatic castration\u2010resistant prostate cancer (mCRPC) within 12 months indicating aggressive tumor biology and poor prognosis. Current risk stratification tools (CHAARTED, LATITUDE) offer limited individualized prediction. Machine learning approaches are increasingly applied to predict prostate cancer progression, but most models show modest performance (AUC 0.68\u20130.72), limited external validation, or require genomic variables unavailable in routine practice. This study aimed to develop and externally validate a novel RINH algorithm for predicting early mCRPC progression (\u2264\u200912 months) using exclusively clinical variables, positioning it as a superior alternative to conventional ML classifiers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This multicenter study enrolled 412 patients with de novo mHSPC from seven Spanish academic centers using mixed retrospective\u2010prospective data collection. Twenty clinical variables were recorded, including demographics, PSA, ISUP grade, metastatic localization, CHAARTED\/LATITUDE classifications, and treatment modalities. Following RINH\u2010based outlier exclusion (55 patients), 357 patients (29 with early progression, 8.1%) were used to train six ML algorithms: RINH, Logistic Regression, Linear Discriminant, Support Vector Machine, Random Forest, and Subspace Discriminant. A two\u2010tiered validation strategy integrated stratified fivefold cross\u2010validation across all centers and formal external validation using center 1 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009121, 19 events) for training and centers 2\u20137 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009207, 10 events) for independent testing. Performance metrics included AUC, sensitivity, specificity, accuracy, and F1\u2010score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Findings and Limitations<\/jats:title>\n                    <jats:p>Artificial intelligence and machine learning (ML) are transforming oncology, promising personalized risk stratification beyond traditional clinical criteria. In metastatic hormone\u2010sensitive prostate cancer (mHSPC), early progression to castration resistance (mCRPC) within 12 months signals aggressive biology and poor prognosis, yet current tools (CHAARTED, LATITUDE) offer limited individualized prediction. Multiple ML models have been proposed with variable success: most achieve modest performance (AUC 0.68\u20130.72), lack robust external validation, or rely on genomic variables inaccessible in routine practice. We propose a novel approach using the Rivality Index Neighborhood (RINH) algorithm, demonstrating superior predictive capacity in an initial multicenter validation with exclusively clinical variables. This study provides rigorous multicenter external validation, advancing toward implementable precision oncology tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Clinical Implications<\/jats:title>\n                    <jats:p>The RINH algorithm achieves superior predictive performance for early mCRPC progression using exclusively clinical variables, representing a significant advance toward implementable risk stratification. However, low reliability scores in external validation underscore that excellent performance metrics alone do not guarantee stability. Before clinical deployment, validation in substantially larger cohorts with higher progression events is essential. If validated, this model could enable personalized, risk\u2010adapted therapeutic strategies, refining patient selection for treatment intensification or de\u2010escalation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pros.70217","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:32:52Z","timestamp":1783017172000},"page":"1352-1366","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Variable\u2010Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation"],"prefix":"10.1002","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6659-3353","authenticated-orcid":false,"given":"Miguel \u00c1ngel","family":"G\u00f3mez\u2010Luque","sequence":"first","affiliation":[{"name":"Department of Urology Hospital Universitario Virgen del Rocio Seville Spain"},{"name":"Foundation for Health Research Management in Seville (FISEVI) Seville Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2286-9893","authenticated-orcid":false,"given":"Pedro","family":"De Pablos\u2010Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Urology Instituto Valenciano de Oncolog\u00eda (IVO) Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6348-8646","authenticated-orcid":false,"given":"Daniel Adolfo","family":"P\u00e9rez\u2010Fentes","sequence":"additional","affiliation":[{"name":"Urology Department Complejo Hospitalario Universitario de Santiago Santiago de Compostela Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8944-4294","authenticated-orcid":false,"given":"Natalia","family":"Picola\u2010Brau","sequence":"additional","affiliation":[{"name":"Department of Urology Hospital de Bellvitge 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