Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.
随着最新版PI-RADS的应用，临床显著性前列腺癌（csPCa）的早期检测已显著改善。然而，惰性病变（iPCa）的过度诊断问题依然存在，而影像组学已成为潜在的解决方案。本系统性综述旨在评估基于PI-RADS v2和/或2.1标准的前列腺MRI检查中，手工与深度学习影像组学在区分csPCa、iPCa及良性病变方面的作用。通过检索PubMed、Cochrane及Web of Science数据库筛选相关研究，并采用诊断准确性研究质量评估工具2（QUADAS-2）、影像组学质量评分（RQS）及医学影像人工智能清单（CLAIM）进行质量评价。从411篇文献中最终纳入14项相关研究。结果显示手工与深度学习影像组学方法在csPCa检测中表现良好，但在少数进行对比评估的研究中，其性能与放射科医师（PI-RADS评估）相比无显著差异。此外，现有研究存在异质性和局限性，质量分析显示可能存在偏倚。未来研究需解决这些问题以推动临床适用性，同时需开展前瞻性研究并与放射科医师（PI-RADS评估）进行对比，以更深入理解其应用潜力。
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