Urso Luca, Badrane Ilham, Manco Luigi, Castello Angelo, Lancia Federica, Collavino Jeanlou, Crestani Alessandro, Castellani Massimo, Cittanti Corrado, Bartolomei Mirco, Giannarini Gianluca
Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy.
Nuclear Medicine Unit, Onco-Hematology Department, University Hospital of Ferrara, 44124 Ferrara, Italy.
J Clin Med. 2025 May 9;14(10):3318. doi: 10.3390/jcm14103318.
: PSMA PET is essential tool in the management of prostate cancer (PCa) patients in various clinical settings of disease. The tremendous growth of the implementation of radiomics and artificial intelligence (AI) in medical imaging techniques has led to an increasing interest in their application in prostate-specific membrane antigen (PSMA) PET. The aim of this article is to systemically review the current literature that explores radiomics and AI analyses of staging PSMA PET towards its potential application in clinical practice. : A systematic research of the literature on three international databases (PubMed, Scopus, and Web of Science) identified a total of 166 studies. An initial screening excluded 68 duplicates and 72 articles relevant to other topics. Finally, 21 studies met the inclusion criteria. : The literature suggests that radiomic analysis could improve the characterization of tumor aggressiveness, the prediction of extra-capsular extension, and seminal vesicles involvement. Moreover, AI models could contribute to predicting BCR after radical treatment. Limitations regarding heterogeneous objectives of investigation, and methodological standardization of radiomics analysis still represent the main obstacle to overcome in order to see these technology break through into daily clinical practice.
前列腺特异性膜抗原(PSMA)正电子发射断层扫描(PET)是在前列腺癌(PCa)患者疾病的各种临床情况下进行管理的重要工具。医学成像技术中放射组学和人工智能(AI)应用的迅猛发展,使得人们对其在前列腺特异性膜抗原(PSMA)PET中的应用兴趣日益浓厚。本文旨在系统回顾当前探索PSMA PET分期的放射组学和AI分析及其在临床实践中潜在应用的文献。
对三个国际数据库(PubMed、Scopus和Web of Science)进行系统的文献检索,共识别出166项研究。初步筛选排除了68篇重复文章和72篇与其他主题相关的文章。最终,21项研究符合纳入标准。
文献表明,放射组学分析可以改善肿瘤侵袭性的特征描述、预测包膜外侵犯和精囊受累情况。此外,AI模型有助于预测根治性治疗后的生化复发(BCR)。研究目标的异质性以及放射组学分析的方法标准化方面的局限性,仍然是这些技术突破进入日常临床实践需要克服的主要障碍。