Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features

Acta Radiol. 2019 Nov;60(11):1543-1552. doi: 10.1177/0284185119830282. Epub 2019 Feb 24.

Authors

En-Ming Cui¹, Fan Lin², Qing Li³, Rong-Gang Li³, Xiang-Meng Chen¹, Zhuang-Sheng Liu¹, Wan-Sheng Long¹

Affiliations

¹ Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China.
² Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, PR China.
³ Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, PR China.

PMID: 30799634
DOI: 10.1177/0284185119830282

No abstract available

Keywords: Renal cell carcinoma; angiomyolipoma; computed tomography (CT); machine learning.

MeSH terms

Angiomyolipoma / diagnostic imaging*
Carcinoma, Renal Cell / diagnostic imaging*
Diagnosis, Differential
Female
Humans
Image Interpretation, Computer-Assisted / methods*
Kidney / diagnostic imaging
Kidney Neoplasms / diagnostic imaging*
Machine Learning*
Male
Middle Aged
Reproducibility of Results
Retrospective Studies
Tomography, X-Ray Computed / methods*