{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:12:28Z","timestamp":1789827148916,"version":"4.0.1"},"reference-count":26,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Scientific Research Project of Hunan Provincial Health Commission","award":["20256441"],"award-info":[{"award-number":["20256441"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancer Biotherapy and Radiopharmaceuticals"],"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Particularly for patients with bone-dominant illness, targeted radionuclide treatments are becoming a viable treatment option for metastatic prostate cancer (PCa). Precision treatment selection and improved therapeutic outcomes may result from early detection of patients who are at high risk of developing bone metastases. To identify early-stage PCa patients at increased risk of postoperative bone metastasis who might benefit from targeted radionuclide therapy, this study sought to develop and validate a multimodal predictive model that integrated biparametric magnetic resonance imaging (bpMRI) radiomics features and serum bone turnover biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      A total of 143 patients with clinically localized PCa (cT1\u20132N0M0) receiving laparoscopic radical prostatectomy were included in this prospective single-center cohort analysis. Bone metabolism indicators, such as osteocalcin N-terminal mid-fragment and alkaline phosphatase, were measured in serum prior to surgery. bpMRI was used to extract radiomics variables that reflected the tumor microenvironment and perfusion characteristics (apparent diffusion coefficient mean and K\n                      <jats:sub>trans<\/jats:sub>\n                      mean). Serum and imaging biomarkers were integrated using a multivariate logistic regression model. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis, and Kaplan\u2013Meier survival analysis for bone metastasis-free survival were used to assess the model\u2019s performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      With an area under the ROC curve of 0.984, the integrated multimodal model outperformed individual biomarkers and imaging features by a significant margin (all\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001). In decision curve analysis, the model demonstrated significant clinical net benefit and strong calibration (Hosmer\u2013Lemeshow test\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      = 0.830). Patients were categorized into high-risk (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 53) and low-risk (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 90) groups according to risk stratification based on the model, with significantly different bone metastasis\u2013free survival results (log-rank\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      &lt; 0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>A reliable method for anticipating postoperative bone metastases in early-stage PCa is a multimodal framework that combines bpMRI radiomics with blood bone turnover indicators. This strategy may facilitate early treatment intervention in individuals with increased metastatic risk and support precision patient selection for targeted radionuclide therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10849785261458478","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:01:39Z","timestamp":1789632099000},"source":"Crossref","is-referenced-by-count":0,"title":["Precision Selection for Targeted Radionuclide Therapy in Prostate Cancer: Biparametric MRI Radiomics Combined with Serum Bone Turnover Biomarkers for Predicting Postoperative Bone Metastasis"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3696-5231","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University\/Hunan Cancer Hospital, Changsha, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University\/Hunan Cancer Hospital, Changsha, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University\/Hunan Cancer Hospital, Changsha, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University\/Hunan Cancer Hospital, Changsha, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Urological Surgery, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University\/Hunan Cancer Hospital, Changsha, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.09.040"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1042\/BSR20203531"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/jpm13050705"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12957-021-02141-5"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.863472"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.21873\/anticanres.16834"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/ird3.99"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1097\/RMR.0000000000000308"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.56434\/j.arch.esp.urol.20237606.46"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.841801"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3021387"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines13051160"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.31083\/j.fbl2905184"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-023-05574-5"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/jcmm.70221"},{"issue":"10","key":"e_1_3_2_18_1","first-page":"1508","article-title":"Beyond PSA: The future of prostate cancer diagnosis using artificial intelligence, novel biomarkers, and advanced imagery","volume":"15","author":"Al Barajraji M","year":"2025","unstructured":"17.Al Barajraji M, , Coscarella M, , Svistakov I, et al. Beyond PSA: The future of prostate cancer diagnosis using artificial intelligence, novel biomarkers, and advanced imagery. 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