Predictive features of pre-operative computed tomography and magnetic resonance imaging for advanced disease in renal cell carcinoma

ARCHIVIO ITALIANO DI UROLOGIA E ANDROLOGIA(2022)

引用 1|浏览6
暂无评分
摘要
Objective: We evaluated predictive features of pre-operative computed tomography and magnetic resonance imaging for advanced disease in renal cell carcinoma. Materials and methods: 92 patients with pathologically confirmed diagnosis of renal cell carcinoma were included in our study. Patients were divided into two groups according to preoperative imaging as computed tomography (CT) (55 patients) and magnetic resonance imaging (MRI) (37 patients). Within the imaging groups, the patients were divided into two groups according to pathological tumor stage: 1-2 (pT1-2) versus >= pT3a. It was evaluated whether there was a difference between the two groups in terms of the presence of pre-operative imaging (CT and MRI) features. Predictive value of these features for >= pT3a disease was evaluated both for CT and MRI. Results: The cut-off value for the Gerota's fascia thickness in predicting >= pT3a disease was calculated as 0.205 cm. Positive predictive value (PPV) for Gerota's fascia thickness was 52.4% (31.0-73.7) and 66.7% (40.0-93.3) for CT and MRI respectively. The PPV value for renal capsule invasion was 75.0% (53.8-96.2) and 90.0% (71.4-108.6) for CT and MRI respectively. PPV of perirenal fat invasion for CT and MRI was 69.2% (44.1-94.3) and 81.8% (59.0-104.6) respectively. Conclusion: Renal capsular invasion and perirenal fat invasion are reliable signs for locally advanced (>= pT3a) renal cell carcinoma both in CT and MRI. Gerota's fascia thickness has relatively low PPV value for prediction of locally advanced disease. Presence of enlarged collateral vessels, tumor necrosis, perinephric stranding are not reliable signs. For all predictors MRI seems more reliable than CT.
更多
查看译文
关键词
Renal cell carcinoma, Computed tomography, Magnetic resonance imaging, Predictive features
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要