Differentiation of fat‐poor angiomyolipoma from clear cell renal cell carcinoma in contrast‐enhanced MDCT images using quantitative feature classification

Purpose To develop a computer‐aided classification system to differentiate benign fat‐poor angiomyolipoma (fp‐ AML ) from malignant clear cell renal cell carcinoma (cc RCC ) using quantitative feature classification on histogram and texture patterns from contrast‐enhanced multidetector computer tomography ( CE MDCT ) images. Methods A dataset including 50 CE MDCT images of 25 fp‐ AML and 25 cc RCC patients was used. From these images, the tumors were manually segmented by an expert radiologist to define the regions of interest ( ROI ). A feature classification system was proposed for separating two types of renal masses, using histogram and texture features and machine learning classifiers. First, 64 quantitative image features, including histogram features based on basic histogram characteristics, percentages of pixels above the thresholds, percentile intensities, and texture features based on gray‐level co‐occurrence matrices ( GLCM ), gray‐level run‐length matrices ( GLRLM ), and local binary patterns ( LBP ), were extracted from each ROI . A number of feature selection methods including stepwise feature selection ( SFS ), ReliefF selection, and principal component analysis ( PCA ) transformation, were applied to select the group of useful features. Finally, the feature classifiers including logistic regression, k nearest neighbors ( kNN ), support vector machine ( SVM ), and random forest ( RF ), were trained on the selected features to differentiate benign fp‐ AML from malignant cc RCC . Each combination of feature selection and classification methods was tested using a fivefold cross‐validation method and evaluated using accuracy, sensitivity, specificity, positive predictive values ( PPV ), negative predictive values ( NPV ), and area under receiver operating characteristic curve ( AUC ). Results In feature selection, the features commonly selected by different feature selection methods were assessed. From three selection methods, three histogram features including maximum intensity, percentages of pixels above the thresholds 210 and 230, and one texture feature of GLCM sum entropy, were jointly selected as key features to distinguish two types of renal masses. In feature classification, kNN and SVM classifiers with ReliefF feature selection demonstrated the best performance among other choices of feature selection and classification methods, where ReliefF+ kNN and ReliefF+ SVM achieved the accuracy of 72.3 ± 4.6% and 72.1 ± 4.2%, respectively. Conclusions We propose a computer‐aided classification system for distinguishing fp‐ AML from cc RCC using machine learning classifiers with quantitative texture features. Our contribution is to investigate the proper combination between the quantitative features and classification systems on the CE MDCT images. In experiments, it can be demonstrated that (a) the features based on histogram characteristics on bright intensity region and texture patterns on inhomogeneity inside masses were selected as key features to classify fp‐ AML and cc RCC , and (b) the proper combination of feature selection and classification methods achieved high performance in differentiating benign from malignant masses. The proposed classification system can be used to assess the useful features associated with the malignancy for renal masses in CE MDCT images.