CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

• Published in: Abdominal imaging Vol. 44; no. 7; pp. 2528 - 2534
• Main Authors: Lin, Fan, Cui, En-Ming, Lei, Yi, Luo, Liang-ping
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Artificial intelligence; Bladder; CARCINOMAS; Classifiers; Clear cell-type renal cell carcinoma; Computed tomography; COMPUTERIZED TOMOGRAPHY; DECISION TREE ANALYSIS; Decision trees; DIAGNOSIS; Diagnostic systems; Feature extraction; Gastroenterology; Hepatology; Imaging; Kidney cancer; KIDNEYS; LEARNING; Learning algorithms; Lesions; Machine learning; Mathematical models; Medical imaging; Medicine; Medicine & Public Health; PATIENTS; Quality; Radiology; RADIOLOGY AND NUCLEAR MEDICINE; Retroperitoneum; Tomography; Ureters; Texture analysis; Clear cell carcinoma; Fuhrman nuclear grade; Machine learning

Purpose To predict the Fuhrman grade of clear cell renal cell carcinoma (ccRCC) with a machine learning classifier based on single- or three-phase computed tomography (CT) images. Materials and methods Patients with pathologically proven ccRCC from February 1, 2009 to September 31, 2018 who were not...