Lifen Yan1, Zaiyi Liu1, Guangyi Wang1, Yanqi Huang1, Yubao Liu1, Yuanxin Yu1, Changhong Liang2. 1. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhong Shan Er Lu, Guangzhou, Guangdong Province 510080, China. 2. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhong Shan Er Lu, Guangzhou, Guangdong Province 510080, China. Electronic address: cjr.lchh@vip.163.com.
Abstract
RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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%). RESULTS: 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. CONCLUSIONS: TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.
RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: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%). RESULTS: 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. CONCLUSIONS: TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.
Authors: Yulia Lakhman; Harini Veeraraghavan; Joshua Chaim; Diana Feier; Debra A Goldman; Chaya S Moskowitz; Stephanie Nougaret; Ramon E Sosa; Hebert Alberto Vargas; Robert A Soslow; Nadeem R Abu-Rustum; Hedvig Hricak; Evis Sala Journal: Eur Radiol Date: 2016-12-05 Impact factor: 5.315
Authors: Matthew S Davenport; Hersh Chandarana; Nicole E Curci; Ankur Doshi; Samuel D Kaffenberger; Ivan Pedrosa; Erick M Remer; Nicola Schieda; Atul B Shinagare; Andrew D Smith; Zhen J Wang; Shane A Wells; Stuart G Silverman Journal: Abdom Radiol (NY) Date: 2018-09