{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:35:17Z","timestamp":1786649717823,"version":"build-2736575974"},"reference-count":41,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>\n                      Early and accurate detection of clinically significant prostate cancer (PCa) is crucial for effective patient management. Traditional diagnostic methods, including systematic biopsy guided by transrectal ultrasound, have limitations in detecting significant cancers. Multiparametric magnetic resonance imaging (mpMRI) has shown promise in overcoming these limitations, but it remains operator-dependent and may miss some significant cases. This study explores the integration of mpMRI with\n                      <jats:sup>68<\/jats:sup>\n                      Ga-prostate-specific membrane antigen positron emission tomography\/computed tomography (\n                      <jats:sup>68<\/jats:sup>\n                      Ga-PSMA PET\/CT) to enhance diagnostic accuracy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>\n                      This study included 15 patients with suspected PCa, who underwent mpMRI and\n                      <jats:sup>68<\/jats:sup>\n                      Ga-PSMA PET\/CT imaging, followed by histopathological confirmation after radical prostatectomy. Imaging data were analyzed using advanced machine learning (ML) models, including support vector machine, random forest, logistic regression, and k-nearest neighbors, to assess diagnostic accuracy. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated to compare the performance of the imaging modalities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      MpMRI demonstrated an AUC of 0.91, with a sensitivity of 77% and specificity of 89% at the selected threshold (Prostate Imaging-Reporting and Data System 4). PET\/CT alone showed an AUC of 0.89, with higher sensitivity (90%) but lower specificity (72%). The combination of mpMRI and PET\/CT did not significantly improve the overall diagnostic performance, as indicated by a net reclassification index of \u22123% (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.84).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>\n                      While\n                      <jats:sup>68<\/jats:sup>\n                      Ga-PSMA PET\/CT improves sensitivity in detecting PCa, its lower specificity limits the incremental benefit when combined with mpMRI. ML models show potential in optimizing diagnostic approaches, but further research with larger cohorts is needed to confirm these findings and refine the integration of imaging modalities.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_48_25","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:01:34Z","timestamp":1751515294000},"page":"329-336","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Prostate Cancer Detection: Integrating Multiparametric Magnetic Resonance Imaging and 68Ga-prostate-specific Membrane Antigen Positron Emission Tomography\/Computed Tomography with Machine Learning"],"prefix":"10.4103","volume":"50","author":[{"given":"Mohammad Hossein","family":"Sadeghi","sequence":"first","affiliation":[{"name":"Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Bagheri","sequence":"additional","affiliation":[{"name":"Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran"},{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Rajaeinejad","sequence":"additional","affiliation":[{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Afshar","family":"Ardalan","sequence":"additional","affiliation":[{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ismail","family":"Karami","sequence":"additional","affiliation":[{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahryar","family":"Sadeghi","sequence":"additional","affiliation":[{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Mosadeghkhah","sequence":"additional","affiliation":[{"name":"Radiation Sciences Research Center (ARSRC), Aja University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sedigheh","family":"Sina","sequence":"additional","affiliation":[{"name":"Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran"},{"name":"Radiation Research Center, School of Mechanical Engineering, Shiraz University, Shiraz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farnaz","family":"KhajehRahimi","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Abu Ali Sina Hospital, Shiraz University of Paramedical Sciences, Shiraz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahboobeh","family":"Sheiki","sequence":"additional","affiliation":[{"name":"Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran"},{"name":"Department of Nuclear Medicine, Abu Ali Sina Hospital, Shiraz University of Paramedical Sciences, Shiraz, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"R1-20250703","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41391-025-00941-4","article-title":"Best of 2024 in prostate cancer and prostatic diseases","volume":"28","author":"De 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