这篇综述重点介绍了肾细胞癌 (RCC) 成像的最新进展。首先是双能计算机断层扫描 (DECT)，该技术在评估肾脏肿块方面表现出较高的诊断准确性。几项研究表明碘定量的潜在好处，特别是在区分低衰减、真正增强的实性肿块和高密度囊肿方面。通过确定是否存在肾脏肿块，DECT 可以避免额外的影像学研究，从而降低医疗费用。 DECT 还可以提供虚拟的未增强图像，有助于减少辐射暴露。然后，该综述提供了最新进展，重点介绍了多参数磁共振 (MR) 成像在肾细胞癌组织学亚型分析以及良恶性肾脏肿块鉴别中的优势。提出的标准化逐步读取图像有助于高精度识别透明细胞肾细胞癌和乳头状肾细胞癌。超声造影可能是表征实性和囊性肾肿块的一种有前途的诊断工具。使用塞他米比和 PSMA 的几种组合药物成像策略为 RCC 的诊断和分期提供了新的机会，但它们在风险分层中的作用需要评估。尽管放射组学和肿瘤纹理分析因再现性差和需要标准化而受到阻碍，但它们在识别新的生物标志物以预测肿瘤组织学、临床结果、总体生存率和治疗反应方面显示出了前景。它们具有广泛的潜在应用，但仍处于研究阶段。人工智能（AI）在肿瘤分类、分级和预后方面显示出令人鼓舞的结果。预计它将在评估治疗反应和推进个性化医疗方面发挥重要作用。然后审查的重点是最近更新的算法和指南。 2019 版 Bosniak 分类结合了 MRI，精确定义了以前模糊的成像术语，并允许将更大比例的肿块归入低风险类别。最近的研究报告称，高风险类别的特异性有所提高，读者间的一致性也更好。透明细胞似然评分增加了 MRI 上肾脏实性肿块表征的标准化，在最近的研究中得到了验证，观察者间的一致性很高。最后，该综述讨论了 2017 年 AUA 指南对肾脏肿块和局限性肾癌的关键影像学影响。

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.