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

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 patient...