{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T22:50:08Z","timestamp":1782600608263,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685908","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,8]]},"abstract":"<jats:p>Patients with metastatic castration-resistant prostate cancer (mCRPC) have a 5-year survival rate of approximately 30%. Accurate prediction of survival in these patients is critical for optimal choice of patient treatment. This study aimed to develop a nomogram to accurately predict overall survival in mCRPC patients in routine clinical practice. We developed a nomogram based on a Cox proportional hazards model with predictors including treatment groups, ALP, LDH, albumin, hemoglobin, and PSA. Model performance was evaluated by AUC, calibration curves, and C-index with internal validation via bootstrapping. High ALP, high LDH, high PSA, low Albumin, and low hemoglobin were significantly associated with an increased risk of death. The nomogram showed good predictive accuracy, with a C-index of 0.637 and AUC of 0.736, 0.686, and 0.712 for 1-, 2-, and 3-year survival predictions. Calibration plots showed strong alignment between predicted and observed survival. The nomogram can be successfully used in clinical practice.<\/jats:p>","DOI":"10.3233\/shti250070","type":"book-chapter","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:30:45Z","timestamp":1744119045000},"source":"Crossref","is-referenced-by-count":2,"title":["Predicting Survival in Metastatic Castration-Resistant Prostate Cancer Patients: Development of a Prognostic Nomogram"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-0292","authenticated-orcid":false,"given":"Xingyue","family":"Huo","sequence":"first","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manish","family":"Kohli","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Envisioning the Future of Health Informatics and Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250070","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:30:45Z","timestamp":1744119045000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"ISBN":["9781643685908"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250070","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]}}}