Abstract：
Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99mTc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed.
摘要：
长期以来，具有对比增强计算机断层扫描（CT）和磁共振成像（MRI）的解剖成像一直是肾脏肿块表征的主要手段。然而,这些模式往往无法充分表征不确定的，固体,增强肾脏肿块-除了一些例外，例如多参数MRI上透明细胞似然评分的发展。因此，分子成像方法作为解剖成像的替代方法已经获得了吸引力。使用99mTc-sestamibi单光子发射计算机断层扫描/CT进行线粒体成像是一种具有成本效益的非侵入性识别嗜酸性细胞瘤和其他惰性肾脏肿块的方法。在光谱的另一端，碳酸酐酶IX剂，最值得注意的是单克隆抗体girentuximab-可以用正电子发射断层扫描放射性核素如锆89标记-可有效识别可能是侵袭性透明细胞肾细胞癌的肾肿块.肾肿块活检,具有相对较高的非诊断率，并且不能明确表征许多嗜酸细胞肿瘤，尽管如此,在任何针对肾脏质量风险分层的算法中都可能发挥重要作用.分子成像和活检的结合在选定的患者与其他先进的成像方法，例如人工智能/机器学习和影像组学特征的抽象，为从不确定的肾脏肿块的风险分层中获得的信息提供了最佳的途径。随着这些方法的正确应用，良性和惰性肾脏肿块的不适当的积极治疗可能会减少。
