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上尿路尿路上皮癌的三维成像可提高诊断的检出率和准确率。

Three-dimensional imaging of upper tract urothelial carcinoma improves diagnostic yield and accuracy.

机构信息

Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.

Department of Urology, Keio University School of Medicine, Tokyo, Japan.

出版信息

JCI Insight. 2024 Jul 22;9(14):e175751. doi: 10.1172/jci.insight.175751.

Abstract

Upper tract urothelial carcinoma (UTUC) is a rare form of urothelial cancer with a high incidence of recurrence and a low survival rate. Almost two-thirds of UTUCs are invasive at the time of diagnosis; therefore, improving diagnostic methods is key to increasing survival rates. Histopathological analysis of UTUC is essential for diagnosis and typically requires endoscopy biopsy, tissue sectioning, and labeling. However, endoscopy biopsies are minute, and it is challenging to cut into thin sections for conventional histopathology; this complicates diagnosis. Here, we used volumetric 3-dimensional (3D) imaging to explore the inner landscape of clinical UTUC biopsies, without sectioning, revealing that 3D analysis of phosphorylated ribosomal protein S6 (pS6) could predict tumor grade and prognosis with improved accuracy. By visualizing the tumor vasculature, we discovered that pS6+ cells were localized near blood vessels at significantly higher levels in high-grade tumors than in low-grade tumors. Furthermore, the clustering of pS6+ cells was associated with shorter relapse-free survival. Our results demonstrate that 3D volume imaging of the structural niches of pS6 cells deep inside the UTUC samples improved diagnostic yield, grading, and prognosis prediction.

摘要

上尿路尿路上皮癌(UTUC)是一种罕见的尿路上皮癌,其复发率高,生存率低。几乎三分之二的 UTUC 在诊断时即为浸润性;因此,改善诊断方法是提高生存率的关键。UTUC 的组织病理学分析对诊断至关重要,通常需要内窥镜活检、组织切片和标记。然而,内窥镜活检标本很小,而且很难切成常规组织病理学的薄片;这使得诊断变得复杂。在这里,我们使用容积 3 维(3D)成像来探索临床 UTUC 活检的内部结构,而无需切片,结果显示,磷酸化核糖体蛋白 S6(pS6)的 3D 分析可以提高准确性,从而预测肿瘤分级和预后。通过可视化肿瘤血管,我们发现 pS6+细胞在高级别肿瘤中的血管附近的分布水平明显高于低级别肿瘤。此外,pS6+细胞的聚类与无复发生存期缩短有关。我们的研究结果表明,对 UTUC 样本中 pS6 细胞结构龛的 3D 容积成像提高了诊断的阳性率、分级和预后预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d02d/11383588/a4d379ef6bdc/jciinsight-9-175751-g103.jpg

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