Antolin Andreu, Roson Nuria, Mast Richard, Arce Javier, Almodovar Ramon, Cortada Roger, Maceda Almudena, Escobar Manuel, Trilla Enrique, Morote Juan
Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain.
Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
Cancers (Basel). 2024 Aug 24;16(17):2951. doi: 10.3390/cancers16172951.
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.
随着最新版前列腺影像报告和数据系统(PI-RADS)的出现,临床上具有显著意义的前列腺癌(csPCa)的早期检测有了实质性改善。然而,惰性病变(iPCa)的过度诊断仍然存在,而放射组学已成为一种潜在的解决方案。本系统评价的目的是评估手工制作的和深度放射组学在区分经PI-RADS v2和/或2.1评估的前列腺MRI上的csPCa病变与iPCa病变及良性病变中的作用。在PubMed、Cochrane和科学网数据库中进行文献检索以选择相关研究。使用诊断准确性研究质量评估2(QUADAS-2)、放射组学质量评分(RQS)和医学影像人工智能检查表(CLAIM)工具进行质量评估。从411篇出版物中总共筛选出14项相关研究。结果强调了手工制作的和深度放射组学方法在csPCa检测方面表现良好,但在少数评估的研究中与放射科医生(PI-RADS)相比没有显著差异。此外,在研究和质量分析中发现了异质性和局限性,这可能会导致偏差。未来的研究应解决这些问题以促进临床应用。需要进行前瞻性研究并与放射科医生(PI-RADS)进行比较,以更好地了解其潜力。