Yan Lifen, Liu Zaiyi, Wang Guangyi, Huang Yanqi, Liu Yubao, Yu Yuanxin, Liang Changhong
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhong Shan Er Lu, Guangzhou, Guangdong Province 510080, China.
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhong Shan Er Lu, Guangzhou, Guangdong Province 510080, China.
Acad Radiol. 2015 Sep;22(9):1115-21. doi: 10.1016/j.acra.2015.04.004. Epub 2015 May 29.
To retrospectively evaluate the diagnostic performance of texture analysis (TA) for the discrimination of angiomyolipoma (AML) with minimal fat, clear cell renal cell cancer (ccRCC), and papillary renal cell cancer (pRCC) on computed tomography (CT) images and to determine the scanning phase, which contains the strongest discriminative power.
Patients with pathologically proved AMLs (n = 18) lacking visible macroscopic fat at CT and patients with pathologically proved ccRCCs (n = 18) and pRCCs (n = 14) were included. All patients underwent CT scan with three phases (precontrast phase [PCP], corticomedullary phase [CMP], and nephrographic phase [NP]). The selected images were analyzed and classified with TA software (MaZda). Texture classification was performed for 1) minimal fat AML versus ccRCC, 2) minimal fat AML versus pRCC, and 3) ccRCC versus pRCC. The classification results were arbitrarily divided into several levels according to the misclassification rates: excellent (misclassification rates ≤10%), good (10%< misclassification rates ≤20%), moderate (20%< misclassification rates ≤30%), fair (30%< misclassification rates ≤40%), and poor (misclassification rates ≥40%).
Excellent classification results (error of 0.00%-9.30%) were obtained with nonlinear discriminant analysis for all the three groups, no matter which phase was used. On comparison of the three scanning phases, we observed a trend toward better lesion classification with PCP for minimal fat AML versus ccRCC, CMP, and NP images for ccRCC versus pRCC and found similar discriminative power for minimal fat AML versus pRCC.
TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.
回顾性评估纹理分析（TA）在计算机断层扫描（CT）图像上鉴别微小脂肪血管平滑肌脂肪瘤（AML）、透明细胞肾细胞癌（ccRCC）和乳头状肾细胞癌（pRCC）的诊断性能，并确定具有最强鉴别力的扫描期相。
纳入经病理证实的CT上无可见宏观脂肪的AML患者（n = 18）以及经病理证实的ccRCC患者（n = 18）和pRCC患者（n = 14）。所有患者均接受了三期CT扫描（平扫期[PCP]、皮质髓质期[CMP]和肾盂期[NP]）。使用TA软件（MaZda）对所选图像进行分析和分类。针对以下情况进行纹理分类：1）微小脂肪AML与ccRCC；2）微小脂肪AML与pRCC；3）ccRCC与pRCC。根据错误分类率将分类结果任意分为几个等级：优秀（错误分类率≤10%）、良好（10%＜错误分类率≤20%）、中等（20%＜错误分类率≤30%）、尚可（30%＜错误分类率≤40%）和差（错误分类率≥40%）。
无论使用哪一期相，对所有三组采用非线性判别分析均获得了优秀的分类结果（误差为0.00% - 9.30%）。比较三个扫描期相时，我们观察到对于微小脂肪AML与ccRCC，PCP期的病变分类趋势更好；对于ccRCC与pRCC，CMP期和NP期图像的分类趋势更好；对于微小脂肪AML与pRCC，发现其鉴别力相似。
TA可能是鉴别微小脂肪AML、ccRCC和pRCC的可靠定量方法。
