The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.
前列腺特异性膜抗原（PSMA）正电子发射断层扫描与计算机断层扫描（PET/CT）的临床整合，为前列腺癌（PC）预后评估中的先进数据分析技术提供了潜力。在这些工具中，放射组学的应用尤为突出，这是一种基于计算机的方法，用于提取和定量分析医学影像中的亚视觉特征。在此背景下，本综述旨在总结当前关于利用PSMA PET/CT衍生的放射组学进行前列腺癌风险分层的文献。我们对2017年至2023年间的相关文献进行了逐步检索。在23篇关于PSMA PET/CT衍生的前列腺放射组学研究中，前列腺癌诊断、活检格里森评分（GS）预测、不良病理预测以及治疗结果分别作为4项（17.4%）、5项（21.7%）、7项（30.4%）和7项（30.4%）研究的主要终点。在预测前列腺癌诊断方面，基于PSMA PET/CT的模型表现良好，受试者工作特征曲线下面积（ROC-AUC）值介于0.85至0.925之间。同样，在预测活检和手术病理结果方面，ROC-AUC值范围分别为0.719–0.84和0.84–0.95。最后，九项研究探讨了治疗后复发、进展或生存的预测，ROC-AUC值范围为0.698–0.90。在本综述纳入的23项研究中，有2项（8.7%）包含了外部验证。尽管基于PSMA PET/CT的放射组学模型在随访和经验方面尚不成熟，但这些结果显示了未来研究和探索的巨大潜力。然而，在考虑临床应用之前，必须优先对放射组学特征的可重复性和生物学验证进行严格验证。
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