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.
本综述重点阐述肾细胞癌影像学领域的最新进展。文章首先探讨双能计算机断层扫描技术，该技术在肾脏肿块评估中展现出较高的诊断准确性。多项研究表明碘定量技术具有潜在优势，尤其有助于区分低衰减的实性强化肿块与高密度囊肿。通过明确肾脏肿块是否存在，双能CT可避免额外的影像学检查需求，从而降低医疗成本。该技术还能提供虚拟平扫图像，有助于减少辐射暴露。 随后，本文聚焦多参数磁共振成像在肾细胞癌组织学分型及良恶性鉴别方面的优势更新。提出的标准化分步阅片法有助于高精度识别透明细胞癌和乳头状肾细胞癌。对比增强超声可能成为实性和囊性肾脏肿块特征评估的重要诊断工具。结合司他比锝与前列腺特异性膜抗原的联合药物成像策略为肾细胞癌诊断与分期提供了新机遇，但其在风险分层中的作用仍需评估。 尽管放射组学与肿瘤纹理分析受限于可重复性不足且需标准化，但在识别预测肿瘤组织学、临床结局、总体生存期及治疗反应的新型生物标志物方面展现出潜力。这些技术具有广泛的应用前景，但仍处于研究阶段。人工智能在肿瘤分类、分级及预后评估方面已取得令人鼓舞的成果，预计将在治疗反应评估和推进个体化医疗中发挥重要作用。 本文继而聚焦近期更新的算法与指南。2019版Bosniak分类系统整合了磁共振成像技术，明确定义了既往模糊的影像学术语，使更多肿块可归入低风险类别。最新研究显示高风险类别的特异性得到提升，观察者间一致性也有所改善。透明细胞癌可能性评分系统为MRI实性肾脏肿块特征评估增添了标准化规范，近期研究验证了其良好的观察者间一致性。 最后，本文探讨了2017年美国泌尿外科学会肾脏肿块与局限性肾癌指南的核心影像学意义。
Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches
肿瘤(癌症)患者之家