Background: Early and accurate grading of renal cell carcinoma (RCC) improves patient risk stratification and has implications for clinical management and mortality. However, current diagnostic approaches using imaging and renal mass biopsy have limited specificity and may lead to undergrading.Methods: This study explored the use of hyperpolarised [1-13C]pyruvate MRI (HP13C-MRI) to identify the most aggressive areas within the tumour of patients with clear cell renal cell carcinoma (ccRCC) as a method to guide biopsy targeting and to reduce undergrading. Six patients with ccRCC underwent presurgical HP13C-MRI and conventional contrast-enhanced MRI. From the imaging data, three k-means clusters were computed by combining thekPLas a marker of metabolic activity, and the13C-pyruvate signal-to-noise ratio (SNRPyr) as a perfusion surrogate. The combined clusters were compared to those derived from individual parameters and to those derived from the percentage of enhancement on the nephrographic phase (%NG). The diagnostic performance of each cluster was assessed based on its ability to predict the highest histological tumour grade in postsurgical tissue samples. The postsurgical tissue samples underwent immunohistochemical staining for the pyruvate transporter (monocarboxylate transporter 1, MCT1), as well as RNA and whole-exome sequencing.Results:The clustering approach combining SNRPyrandkPLdemonstrated the best performance for predicting the highest tumour grade: specificity 85%; sensitivity 64%; positive predictive value 82%; and negative predictive value 68%. Epithelial MCT1 was identified as the major determinant of the HP13C-MRI signal. The perfusion/metabolism mismatch cluster showed an increased expression of metabolic genes and markers of aggressiveness.Conclusions:This study demonstrates the potential of using HP13C-MRI-derived metabolic clusters to identify intratumoral variations in tumour grade with high specificity. This work supports the use of metabolic imaging to guide biopsies to the most aggressive tumour regions and could potentially reduce sampling error.
背景：肾细胞癌（RCC）的早期准确分级有助于改善患者风险分层，并对临床管理和死亡率具有重要影响。然而，目前基于影像学和肾肿块活检的诊断方法特异性有限，可能导致分级不足。 方法：本研究探讨了使用超极化[1-13C]丙酮酸磁共振成像（HP13C-MRI）来识别透明细胞肾细胞癌（ccRCC）患者肿瘤内最具侵袭性区域的方法，旨在指导活检靶向定位并减少分级不足。六名ccRCC患者接受了术前HP13C-MRI和常规增强MRI检查。通过结合代谢活性标志物kPL和灌注替代指标13C-丙酮酸信噪比（SNRPyr），从影像数据中计算出三个k-means聚类。将组合聚类与基于单个参数得出的聚类以及基于肾图期强化百分比（%NG）得出的聚类进行比较。根据各聚类预测术后组织样本中最高组织学肿瘤分级的能力评估其诊断性能。术后组织样本进行了丙酮酸转运蛋白（单羧酸转运蛋白1，MCT1）的免疫组化染色，以及RNA和全外显子组测序。 结果：结合SNRPyr和kPL的聚类方法在预测最高肿瘤分级方面表现出最佳性能：特异性85%；敏感性64%；阳性预测值82%；阴性预测值68%。上皮MCT1被确定为HP13C-MRI信号的主要决定因素。灌注/代谢不匹配聚类显示代谢基因和侵袭性标志物的表达增加。 结论：本研究证明了使用HP13C-MRI衍生的代谢聚类以高特异性识别肿瘤内分级差异的潜力。这项工作支持利用代谢成像引导活检至最具侵袭性的肿瘤区域，并可能减少采样误差。
肿瘤(癌症)患者之家