Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

• Published in: European radiology Vol. 28; no. 4; pp. 1625 - 1633
• Main Authors: Feng, Zhichao, Rong, Pengfei, Cao, Peng, Zhou, Qingyu, Zhu, Wenwei, Yan, Zhimin, Liu, Qianyun, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2018; Springer Nature B.V
• Subjects: Angiomyolipoma; Angiomyolipoma - diagnostic imaging; Angiomyolipoma - pathology; Artificial intelligence; Carcinoma, Renal Cell - diagnostic imaging; Carcinoma, Renal Cell - pathology; Classifiers; Computed tomography; Diagnosis, Differential; Diagnostic Radiology; Diagnostic systems; Differentiation; Feature extraction; Female; Humans; Image processing; Image segmentation; Imaging; Internal Medicine; Interventional Radiology; Kidney cancer; Kidney Neoplasms - diagnostic imaging; Kidney Neoplasms - pathology; Learning algorithms; Machine learning; Male; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Oversampling; Radiology; Renal cell carcinoma; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Support Vector Machine; Support vector machines; Surgery; Texture; Tomography, X-Ray Computed - methods; Ultrasound; Urogenital; Texture analysis; Angiomyolipoma; Computed tomography; Renal cell carcinoma; Machine learning
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-017-5118-z

Objective To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). Methods This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. Results Of the 42 extracted features, 16 candidate features showed significant intergroup differences ( P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. Conclusion Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. Key Points • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf .