Computed Tomography of Neoplastic Infiltrating Renal Masses in Patients Without a Previous History of Cancer

对既往无癌症病史的患者进行肿瘤浸润性肾脏肿块的计算机断层扫描

阅读:1

Abstract

BACKGROUND/OBJECTIVES: Infiltrative renal masses, characterized by ill-defined margins and parenchymal invasion without forming a discrete mass, present a diagnostic challenge, particularly in patients without a prior history of malignancy. Differentiating among the most common malignant etiologies-renal cell carcinoma (RCC), urothelial carcinoma (UC), and lymphoma-is essential for guiding appropriate treatment. This study aimed to evaluate whether specific computed tomography (CT) features can assist in the differential diagnosis of these lesions. METHODS: A retrospective review was conducted on 68 patients with infiltrative renal masses presented at a tertiary hospital's oncologic urology committee between 2018 and 2022. Patients with prior malignancy or signs of infection were excluded. All cases underwent contrast-enhanced CT within three months of diagnosis and had histopathological confirmation. Imaging features such as necrosis, collecting system involvement, lymphadenopathy, and others were assessed and statistically analyzed. RESULTS: RCC was the most frequent diagnosis (68%), followed by UC (18%) and lymphoma (7.4%). Significant differences were observed in imaging features: necrosis was more common in RCC (87%) than in UC (25%) and lymphoma (20%), p < 0.001; collecting system involvement was universal in UC (100%) and less common in RCC (65%) and lymphoma (40%), p = 0.009; and lymphadenopathy was more frequent in lymphoma (80%) than in UC (67%) and RCC (35%), p = 0.038. Tumor size also varied significantly, with lymphomas presenting the largest median size (11 cm), followed by RCCs (8.2 cm) and UCs (5 cm), p < 0.001. CONCLUSIONS: CT imaging features, particularly necrosis, collecting system involvement, and lymphadenopathy, can aid in distinguishing among RCC, UC, and lymphoma in patients with infiltrative renal masses and no prior cancer history. These findings may support more accurate diagnoses and inform tailored therapeutic strategies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。