Pedrosa Ivan, Chou Mary T, Ngo Long, H Baroni Ronaldo, Genega Elizabeth M, Galaburda Laura, DeWolf William C, Rofsky Neil M
Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02118, USA.
Eur Radiol. 2008 Feb;18(2):365-75. doi: 10.1007/s00330-007-0757-0. Epub 2007 Sep 26.
To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.
对通过磁共振(MR)成像评估的恶性肾肿瘤进行特征分析,并研究MR成像特征与组织病理学结果之间的相关性。回顾性评估了79例恶性肾肿块的MR检查,并进行了特征分析。根据一种提议的MRI分类系统,将每个肾肿块归入八个类别之一。计算了MRI分类系统预测组织学亚型和核分级的敏感性和特异性。化学位移成像上的亚体素脂肪与透明细胞类型相关(p < 0.05);敏感性 = 42%,特异性 = 100%。大尺寸、肿瘤内坏死、腹膜后血管侧支和肾静脉血栓形成提示高级别透明细胞类型(p < 0.05)。小尺寸、周边位置、T2加权图像上肿瘤内信号强度低以及低水平强化与低级别乳头状癌相关(p < 0.05)。MRI分类系统诊断低级别透明细胞癌、高级别透明细胞癌、所有透明细胞癌、所有乳头状癌和移行细胞癌的敏感性和特异性分别为50%和94%、93%和75%、92%和83%、80%和94%以及100%和99%。MRI特征分析和提议的分类系统有助于预测肾肿块的组织学类型和核分级。