Liu Jianliang, Sandhu Kieran, Woon Dixon T S, Perera Marlon, Lawrentschuk Nathan
EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, Australia; Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia; University of Melbourne, Department of Surgery, Melbourne, Australia; Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia.
Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia.
Semin Nucl Med. 2025 May;55(3):371-376. doi: 10.1053/j.semnuclmed.2024.12.001. Epub 2025 Jan 31.
This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans for prostate cancer (PCa). A literature review was conducted on the Medline, Embase, Web of Science, and IEEE Xplore databases. The search focused on studies that utilizes AI to evaluate PSMA PET scans. Original English language studies published from inception to October 2024 were included, while case reports, series, commentaries, and conference proceedings were excluded. AI applications show promise in automating the detection of metastatic disease and anatomical segmentation in PSMA PET scans. AI was also able to predict response to PSMA-based theragnostic and aids in tumor burden segmentation, improving radiotherapy planning. AI could also differentiate intraprostatic PCa with higher histological grade and predict extra-prostatic extension. AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes. Larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.
本综述旨在提供人工智能（AI）在评估前列腺特异性膜抗原（PSMA）正电子发射断层扫描（PET）用于前列腺癌（PCa）方面效用的最新概述。对Medline、Embase、科学网和IEEE Xplore数据库进行了文献综述。搜索集中于利用AI评估PSMA PET扫描的研究。纳入了从开始到2024年10月发表的原始英语研究，而排除了病例报告、系列报道、评论和会议论文集。AI应用在PSMA PET扫描中自动检测转移性疾病和进行解剖分割方面显示出前景。AI还能够预测基于PSMA的治疗诊断反应，并辅助进行肿瘤负荷分割，改善放射治疗计划。AI还可以区分组织学分级较高的前列腺内PCa，并预测前列腺外扩展情况。AI在评估PCa的PSMA PET扫描方面具有潜力，特别是在检测转移、测量肿瘤负荷、检测前列腺内高级别癌症以及预测治疗结果方面。需要更大规模的多中心前瞻性研究来验证和提高这些AI模型的通用性。
