{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:53:43Z","timestamp":1790355223798,"version":"4.1.0"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Precision Radiation Oncology"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Positron emission tomography (PET) with gallium\u201068 prostate\u2010specific membrane antigen (\n                    <jats:sup>68<\/jats:sup>\n                    Ga\u2010PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and prognosis in PC patients. This review provides an overview of the current methods and challenges in the assessment of regional and total tumor volumes using\n                    <jats:sup>68<\/jats:sup>\n                    Ga\u2010PSMA PET. Traditional manual segmentation methods are time\u2010consuming processes that are further challenged by inter\u2010observer variability. Artificial intelligence (AI)\u2010based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning\u2010based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. This review discusses the principles underlying AI\u2010based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. Furthermore, the advantages of AI\u2010based segmentation are highlighted, such as improved reproducibility, faster processing times, and potential for personalized medicine. Despite these advancements, AI\u2010based segmentation faces significant challenges, including the need for standardized protocols, extensive validation studies, and seamless integration into clinical workflows. Addressing these limitations is essential for the widespread adoption of AI\u2010based segmentation in\n                    <jats:sup>68<\/jats:sup>\n                    Ga\u2010PSMA PET for PC, ultimately advancing the field and improving patient care.\n                  <\/jats:p>","DOI":"10.1002\/pro6.70014","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T08:31:34Z","timestamp":1746261094000},"page":"120-132","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning (DL)\u2010based advancements in prostate cancer imaging: Artificial intelligence (AI)\u2010based segmentation of\n                    <sup>68<\/sup>\n                    Ga\u2010PSMSA PET for tumor volume assessment"],"prefix":"10.1002","volume":"9","author":[{"given":"Sharjeel","family":"Usmani","sequence":"first","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC) University Medical city  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khulood","family":"Al Riyami","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC) University Medical city  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subash","family":"Kheruka","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC) University Medical city  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shah P","family":"Numani","sequence":"additional","affiliation":[{"name":"Department of Radiology Carl T Hayden VA Medical Center  Phoenix USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashid","family":"al Sukaiti","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC) University Medical city  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Medical University of Lublin  Lublin Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3821-4357","authenticated-orcid":false,"given":"Nadeem","family":"Pervez","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC) University Medical city  Muscat Oman"},{"name":"Internal Medicine College of Medicine and Health Sciences UAE University  Abu Dhabi United Arab 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