{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:57:45Z","timestamp":1792799865571,"version":"4.5.0"},"reference-count":41,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National High Level Hospital Clinical Research Funding","award":["BJ-2023-235"],"award-info":[{"award-number":["BJ-2023-235"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82371932"],"award-info":[{"award-number":["82371932"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences","award":["2023-JKCS-22"],"award-info":[{"award-number":["2023-JKCS-22"]}],"id":[{"id":"https:\/\/ror.org\/02drdmm93","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) have shown potential in diagnosing clinically significant prostate cancer (csPCa). Histogram analysis enhances diagnostic accuracy by evaluating spatial heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To assess the performance of histogram analysis models utilizing relaxation maps from SyMRI in diagnosing csPCa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>A total of 124 men with a clinical suspicion of csPCa were enrolled prospectively between April 2018 and December 2019. From 124 patients, 224 ROIs were analyzed, including 97 csPCa lesions, 11 insignificant PCa, 59 non-cancerous peripheral zone (PZ) lesions, and 57 benign prostatic hyperplasia. The lesions were randomly divided into a training group and a validation group, in a ratio of 7:3. Histogram analysis models were constructed using SyMRI relaxation maps, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and their combination. We compared these with mean-value-based models using the same modalities. The diagnostic accuracy of these models in distinguishing csPCa from clinically insignificant disease (CIS) was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Histogram analysis models outperformed mean-value-based models in both training and validation groups. SyMRI-based histogram analysis models demonstrated diagnostic effectiveness comparable to DWI and ADC models. The combined model achieved the highest area under the curve values in the PZ (0.898; 95% confidence interval [CI]=0.763\u20130.999) and transition zone (TZ) (0.944; 95% CI=0.874\u20130.999). In the TZ, the combined model significantly outperformed the Prostate Imaging Reporting and Data System (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.019).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Histogram analysis of SyMRI relaxation maps is a valuable tool for differentiating csPCa from CIS. Combining SyMRI with DWI and ADC further improved diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851251349488","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:45Z","timestamp":1750220565000},"page":"1122-1131","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["SyMRI histogram analysis for diagnosing clinically significant prostate cancer"],"prefix":"10.1177","volume":"66","author":[{"given":"Hao","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Radiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China"},{"name":"Graduate School of Peking Union Medical College, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China"},{"name":"Peking University Fifth School of Clinical Medicine, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadong","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China"},{"name":"Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Urology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Beijing Hospital, National Center of Gerontology; 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