Landman Jaime, Park Jae Young, Zhao Chenhui, Baker Molly, Hofmann Martin, Helmy Mohammad, Lall Chandana, Bozoghlanian Mari, Okhunov Zhamshid
From the Departments of *Urology and †Radiology, University of California, Irvine, Orange, CA; ‡Department of Urology, Korea University College of Medicine, Seoul, Republic of Korea; §Department of Urology, Ruijin Hospital Luwan Branch Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, People's Republic of China; and ∥Department of Obstetrics and Gynecology, University of California, Davis, Sacramento, CA.
J Comput Assist Tomogr. 2017 Sep/Oct;41(5):702-707. doi: 10.1097/RCT.0000000000000588.
The aim of this study was to assess the accuracy of computed tomography (CT) imaging in diagnosing perinephric fat (PNF) invasion in patients with renal cell carcinoma.
We retrospectively reviewed the medical records and preoperative CT images of 161 patients (105 men and 56 women) for pT1-pT3a renal cell carcinoma. We analyzed the predictive accuracy of CT criteria for PNF invasion stratified by tumor size. We determined the predictive value of CT findings in diagnosing PNF invasion using logistic regression analysis.
The overall accuracy of perinephric (PN) soft-tissue stranding, peritumoral vascularity, increased density of the PNF, tumoral margin, and contrast-enhancing soft-tissue nodule to predict PNF invasion were 56%, 59%, 35%, 80%, and 87%, respectively. Perinephric soft-tissue stranding and peritumoral vascularity showed high sensitivity but low specificity regardless of tumor size. A contrast-enhancing soft-tissue nodule showed low sensitivity but high specificity in predicting PNF invasion. Among tumors 4 cm or less, PN soft-tissue stranding showed 100% sensitivity and 70% specificity, and tumor margin showed 100% sensitivity and 98% specificity. Among CT criteria for PNF invasion, PN soft-tissue stranding was chosen as the only significant factor for assessing PNF invasion by logistic regression analysis.
Computed tomography does not seem to reliably predict PNF invasion. However, PN soft-tissue stranding was shown to be the only significant factor for predicting PNF invasion, which showed good accuracy with high sensitivity and high specificity in tumors 4 cm or less.
本研究旨在评估计算机断层扫描（CT）成像在诊断肾细胞癌患者肾周脂肪（PNF）侵犯方面的准确性。
我们回顾性分析了161例（105例男性和56例女性）pT1 - pT3a期肾细胞癌患者的病历和术前CT图像。我们分析了按肿瘤大小分层的CT标准对PNF侵犯的预测准确性。我们使用逻辑回归分析确定CT表现对诊断PNF侵犯的预测价值。
肾周（PN）软组织条索状影、肿瘤周围血管形成、PNF密度增加、肿瘤边缘及强化软组织结节预测PNF侵犯的总体准确率分别为56%、59%、35%、80%和87%。无论肿瘤大小，肾周软组织条索状影和肿瘤周围血管形成均表现出高敏感性但低特异性。强化软组织结节在预测PNF侵犯方面表现出低敏感性但高特异性。在直径4 cm及以下的肿瘤中，PN软组织条索状影的敏感性为100%，特异性为70%，肿瘤边缘的敏感性为100%，特异性为98%。在PNF侵犯的CT标准中，逻辑回归分析选择PN软组织条索状影作为评估PNF侵犯的唯一显著因素。
计算机断层扫描似乎不能可靠地预测PNF侵犯。然而，PN软组织条索状影被证明是预测PNF侵犯的唯一显著因素，在直径4 cm及以下的肿瘤中表现出良好的准确性，具有高敏感性和高特异性。
