Andrei Gafita, Jennifer A. Schroeder, Francesco Ceci, Jorge D. Oldan, Amir H. Khandani, Frederic E. Lecouvet, Lilja B. Solnes, Steven P. Rowe
{"title":"Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT","authors":"Andrei Gafita, Jennifer A. Schroeder, Francesco Ceci, Jorge D. Oldan, Amir H. Khandani, Frederic E. Lecouvet, Lilja B. Solnes, Steven P. Rowe","doi":"10.2967/jnumed.124.268071","DOIUrl":null,"url":null,"abstract":"<p>In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)–targeted PET for the staging and restaging of men with prostate cancer (PC). To date, there have been regulatory approvals for PSMA PET for purposes of initial staging, recurrence, and establishing eligibility for PSMA-targeted radiopharmaceutical therapy. Conventional imaging modalities, including bone scan and CT, are inadequate for identifying sites of PC in a variety of clinical scenarios. Further, current standardized response assessment approaches based on either conventional imaging or PET radiotracers that lack sensitivity for PC are inappropriate for response assessment in men with PC. There is currently no specific regulatory approval for the use of PSMA PET for response assessment. In the context of the use of PSMA-targeted radiopharmaceutical therapy and other cytotoxic therapeutic approaches, both the PSMA PET progression criteria and RECIP 1.0 have been shown to have value and to provide prognostic information. However, the role of those criteria is less clear for patients who are being treated with agents targeting the androgen signaling axis, given variable changes in PSMA expression. Ultimately, there may be key roles for machine learning and artificial intelligence in identifying imaging biomarkers based on changes in PSMA PET uptake during therapy.</p>","PeriodicalId":22820,"journal":{"name":"The Journal of Nuclear Medicine","volume":"70 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Nuclear Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2967/jnumed.124.268071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)–targeted PET for the staging and restaging of men with prostate cancer (PC). To date, there have been regulatory approvals for PSMA PET for purposes of initial staging, recurrence, and establishing eligibility for PSMA-targeted radiopharmaceutical therapy. Conventional imaging modalities, including bone scan and CT, are inadequate for identifying sites of PC in a variety of clinical scenarios. Further, current standardized response assessment approaches based on either conventional imaging or PET radiotracers that lack sensitivity for PC are inappropriate for response assessment in men with PC. There is currently no specific regulatory approval for the use of PSMA PET for response assessment. In the context of the use of PSMA-targeted radiopharmaceutical therapy and other cytotoxic therapeutic approaches, both the PSMA PET progression criteria and RECIP 1.0 have been shown to have value and to provide prognostic information. However, the role of those criteria is less clear for patients who are being treated with agents targeting the androgen signaling axis, given variable changes in PSMA expression. Ultimately, there may be key roles for machine learning and artificial intelligence in identifying imaging biomarkers based on changes in PSMA PET uptake during therapy.