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Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET\/MRI scans to predict increased prostatic PSMA uptake based on\u00a0the axial T2-weighted sequence alone.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Material and methods<\/jats:title>\n                <jats:p>All patients undergoing simultaneous PSMA PET\/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>One hundred and fifty-four PSMA PET\/MRI scans were available (133 [<jats:sup>68<\/jats:sup>Ga]Ga-PSMA-11 and 21 [<jats:sup>18<\/jats:sup>F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (<jats:italic>n<\/jats:italic>\u2009=\u2009124) and a test cohort (<jats:italic>n<\/jats:italic>\u2009=\u200930). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5\u2009\u00b1\u200915.6%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11547-024-01820-z","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T10:02:15Z","timestamp":1714730535000},"page":"901-911","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI"],"prefix":"10.1007","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2842-0301","authenticated-orcid":false,"given":"Riccardo","family":"Laudicella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-6103","authenticated-orcid":false,"given":"Albert","family":"Comelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moritz","family":"Schwyzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-1731","authenticated-orcid":false,"given":"Alessandro","family":"Stefano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2542-3611","authenticated-orcid":false,"given":"Ender","family":"Konukoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Messerli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio","family":"Baldari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8866-8010","authenticated-orcid":false,"given":"Daniel","family":"Eberli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8029-8692","authenticated-orcid":false,"given":"Irene A.","family":"Burger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"1820_CR1","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","volume":"389","author":"HU Ahmed","year":"2017","unstructured":"Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, Collaco-Moraes Y, Ward K, Hindley RG, Freeman A, Kirkham AP, Oldroyd R, Parker C, Emberton M, PROMIS Study Group (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. 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The study was approved by the institutional review board (2020\u201302861).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All staging patients signed a general informed consent for retrospective studies, while biopsy guidance patients signed the specific written informed consent of the prospective study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors affirm that human research participants provided informed consent for publication of the images in Figs.\u00a0, , and .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}