Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada.
Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Ontario, Canada.
J Magn Reson Imaging. 2021 Jun;53(6):1632-1645. doi: 10.1002/jmri.27191. Epub 2020 May 15.
Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T and T relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
临床实践中使用前列腺影像报告和数据系统（PI-RADS）报告前列腺 MRI。PI-RADS 旨在尽可能标准化前列腺 MRI 的采集、解读、报告，最终实现前列腺 MRI 的性能标准化。PI-RADS 主要依赖于对 MR 成像发现的主观分析，很少包含定量特征。PI-RADS 的缺点主要有：观察者间的一致性低至中度，以及对过渡区有临床意义的肿瘤的检测准确性不高。因此，人们对更定量地分析前列腺 MR 成像发现产生了兴趣。定量 MR 成像特征包括：肿瘤大小和体积、肿瘤与包膜接触的长度、肿瘤表观扩散系数（ADC）指标、肿瘤 T1 和 T2 弛豫时间、肿瘤形状和纹理分析，这些特征都有助于改善前列腺 MRI 上检测到的观察结果的特征描述，并区分肿瘤的病理分级和分期。定量分析可能会提高癌症检测的诊断准确性，并可能成为一种无创手段来预测患者的预后并指导治疗。由于定量分析前列腺 MRI 较少依赖于单个使用者的评估，因此它还可以提高观察者间的一致性。使用人工神经网络对半自动和全自动分析定量（放射组学）MRI 特征代表了定量前列腺 MRI 的下一步发展，目前正在积极研究中。需要通过高质量的多中心研究来验证定量前列腺 MRI 在临床上检测有临床意义的前列腺癌的诊断准确性。本文综述了定量前列腺 MRI 的进展，强调了现有和新兴技术的优势和局限性，并讨论了在临床实践中使用定量评估时评估前列腺 MRI 的机会和挑战。证据水平：5 技术功效：2 级。
