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定量前列腺磁共振成像,来自定量成像特刊。

Quantitative Prostate MRI, From the Special Series on Quantitative Imaging.

作者信息

Margolis Daniel J A, Chatterjee Aritrick, deSouza Nandita M, Fedorov Andriy, Fennessy Fiona M, Maier Stephan E, Obuchowski Nancy, Punwani Shonit, Purysko Andrei, Rakow-Penner Rebecca, Shukla-Dave Amita, Tempany Clare M, Boss Michael, Malyarenko Dariya

机构信息

Department of Radiology, Weill Cornell Medical College, New York, NY.

Department of Radiology, University of Chicago, Chicago, IL.

出版信息

AJR Am J Roentgenol. 2024 Oct 2. doi: 10.2214/AJR.24.31715.

Abstract

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer (csPCa) as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion, diffusion kurtosis, diffusion tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination, but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water imaging and hybrid-multidimensional MRI. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative size and shape features can be combined with the aforementioned techniques and be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms use-cases.

摘要

传统上,前列腺MRI依赖于定性解释。然而,定量分析有潜力显著提高其性能。扩散加权成像(DWI)中的表观扩散系数(ADC)可能是最广为人知的定量MRI生物标志物,对临床显著前列腺癌(csPCa)以及治疗后复发癌均显示出强大的鉴别价值。先进的扩散技术,包括体素内不相干运动、扩散峰度成像、扩散张量成像以及诸如受限谱成像等特定技术,据称具有更好的鉴别能力,但技术上更具挑战性。组织固有的T1和T2也具有诊断价值,更先进的技术可实现管腔内水成像和混合多维MRI。动态对比增强成像主要使用改良的Tofts模型,也显示出独立的鉴别价值。最后,定量的大小和形状特征可与上述技术相结合,并通过放射组学、纹理分析和人工智能进一步优化。最终哪种技术能在临床上广泛应用将取决于在众多平台用例上的验证情况。

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本文引用的文献

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