Tanaka Hajime, Fujii Yasuhisa, Tanaka Hiroshi, Ishioka Junichiro, Matsuoka Yoh, Saito Kazutaka, Uehara Sho, Numao Noboru, Yuasa Takeshi, Yamamoto Shinya, Masuda Hitoshi, Yonese Junji, Kihara Kazunori
Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan.
Department of Radiology, Ochanomizu Surugadai Clinic, Tokyo, Japan.
Int J Urol. 2017 Jul;24(7):511-517. doi: 10.1111/iju.13354. Epub 2017 Jun 10.
To develop a stepwise diagnostic algorithm for fat-poor angiomyolipoma in small renal masses.
Two cohorts of small renal masses <4 cm without an apparent fat component that was pathologically diagnosed were included: 153 cases (18 fat-poor angiomyolipomas/135 renal cell carcinomas) for model development and 71 cases (seven fat-poor angiomyolipomas/59 renal cell carcinomas/5 oncocytomas) for validation. Dynamic contrast-enhanced computed tomography, magnetic resonance imaging and clinical findings were analyzed. Based on multivariate analysis, we developed two prediction models for fat-poor angiomyolipoma, the computed tomography model and the computed tomography + magnetic resonance imaging model, and a stepwise algorithm that proposes the sequential use of computed tomography and magnetic resonance imaging.
The computed tomography model, which was composed of female aged <50 years, high attenuation on unenhanced computed tomography, less enhancement than the normal renal cortex and homogeneity in the corticomedullary phase, differentiated tumors with none of the factors as the low angiomyolipoma-probability group, and the others were candidates for the computed tomography + magnetic resonance imaging model. The computed tomography + magnetic resonance imaging model, consisting of the first three factors of the computed tomography model, low signal intensity and absence of pseudocapsule on T2-weighted magnetic resonance imaging, re-stratified the tumors into low, intermediate and high angiomyolipoma-probability groups. The incidence of fat-poor angiomyolipoma in each group was 0%, 26% and 93%, respectively (area under the curve 0.981). External validation by two readers showed a high area under the curve (0.912 and 0.924) for each. The interobserver agreement was good (kappa score 0.77).
The present algorithm differentiates fat-poor angiomyolipoma in small renal masses with high accuracy by adding magnetic resonance imaging to computed tomography in selected patients.
为小肾肿块中的乏脂性血管平滑肌脂肪瘤制定一种逐步诊断算法。
纳入两组经病理诊断的直径<4 cm且无明显脂肪成分的小肾肿块：153例用于模型开发（18例乏脂性血管平滑肌脂肪瘤/135例肾细胞癌），71例用于验证（7例乏脂性血管平滑肌脂肪瘤/59例肾细胞癌/5例嗜酸细胞瘤）。分析动态对比增强计算机断层扫描、磁共振成像和临床资料。基于多变量分析，我们开发了两种乏脂性血管平滑肌脂肪瘤预测模型，即计算机断层扫描模型和计算机断层扫描 + 磁共振成像模型，以及一种建议依次使用计算机断层扫描和磁共振成像的逐步算法。
计算机断层扫描模型由年龄<50岁的女性、平扫计算机断层扫描时的高衰减、增强程度低于正常肾皮质以及皮髓质期的均匀性组成，将无这些因素的肿瘤鉴别为低血管平滑肌脂肪瘤概率组，其他则为计算机断层扫描 + 磁共振成像模型的候选对象。计算机断层扫描 + 磁共振成像模型由计算机断层扫描模型的前三个因素、T2加权磁共振成像上的低信号强度和无假包膜组成，将肿瘤重新分层为低、中、高血管平滑肌脂肪瘤概率组。每组中乏脂性血管平滑肌脂肪瘤的发生率分别为0%、26%和93%（曲线下面积为0.981）。两位阅片者的外部验证显示，每个模型的曲线下面积均较高（分别为0.912和0.924）。观察者间一致性良好（kappa值为0.77）。
本算法通过在选定患者中对计算机断层扫描增加磁共振成像，可高精度鉴别小肾肿块中的乏脂性血管平滑肌脂肪瘤。
