{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:15:08Z","timestamp":1788441308188,"version":"build-2803163510"},"reference-count":44,"publisher":"IOP Publishing","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62571309"],"award-info":[{"award-number":["No. 62571309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shanghai Municipality","award":["No. 25ZR1401135"],"award-info":[{"award-number":["No. 25ZR1401135"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    To develop a two-stage diagnostic framework using pseudo-localization for patient-level diagnosis of clinically significant prostate cancer (csPCa) on multiparametric magnetic resonance imaging (mpMRI), enabling better utilization of examinations without manual lesion annotations.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We included 1494 mpMRI examinations from \u2018the Prostate Imaging: Cancer AI\u2019 (PI-CAI) dataset, partitioned at the patient level into 217 annotated csPCa, 202 unannotated csPCa, and 1075 non-csPCa cases. First, a prostate transformer U-Net (PTUnet) was trained on the 217 annotated cases via five-fold cross-validation to generate pseudo-locations for the 202 unannotated csPCa cases. These pseudo-locations, along with expert lesion annotations and non-csPCa labels, were used to train an anisotropic UX-Net (AUX-Net). The probability-ushered lesion selection (PULSE) procedure derived patient-level diagnostic probabilities. We evaluated PTUnet, AUX-Net, PULSE, and the pseudo-localization strategy against representative models and baselines, and analyzed the link between pseudo-localization quality and diagnostic performance.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    PTUnet achieved a dice similarity coefficient (DSC) of 64.96 \u00b1 4.22% and an average precision of 65.69 \u00b1 5.45%, the best overall pseudo-localization performance among evaluated models. All four diagnostic models showed higher area under the curves (AUC) with pseudo-locations. AUX-Net with PULSE achieved an AUC of 83.11%, sensitivity of 82.35%, specificity of 75.75%, and Youden\u2019s index of 58.10%, compared with the 80.96% AUC of AUX-Net without pseudo-locations. Pseudo-locations from nine different models all improved diagnostic AUC over the no-pseudo-location baseline, indicating the benefit is not model-specific. Furthermore, localization DSC was significantly and positively correlated with diagnostic AUC (Spearman\u2019s\n                    <jats:italic>\u03c1<\/jats:italic>\n                    = 0.800,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.0096), suggesting higher-quality pseudo-localization leads to better diagnosis.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The framework allows csPCa examinations without manual annotations to contribute to diagnostic model training. Results show pseudo-localization improves patient-level diagnosis across different architectures, and its quality is positively associated with downstream performance. The framework shows potential for non-invasive AI-assisted csPCa diagnosis and biopsy triage, pending further validation on independent larger-scale datasets and prospective clinical cohorts.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae9e7f","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:53:28Z","timestamp":1787698408000},"page":"175020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnosis of clinically significant prostate cancer in multiparametric MRI with pseudo-localization of suspected lesion"],"prefix":"10.1088","volume":"71","author":[{"given":"Xijun","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai University"},{"name":"Shanghai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongzong","family":"Liu","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai University"},{"name":"Shanghai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai University"},{"name":"Shanghai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haihao","family":"He","sequence":"additional","affiliation":[{"name":"Shanghai University"},{"name":"Shanghai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7041-643X","authenticated-orcid":true,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai University"},{"name":"Shanghai University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"pmbae9e7fbib1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41746-025-02193-x","type":"journal-article","article-title":"Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings","volume":"9","author":"Akatsuka","year":"2026","journal-title":"npj Digit. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-03-02","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-03","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}