Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features-Reference-Cited by-同舟云学术

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

Published:2019-02-24 Issue:11 Volume:60 Page:1543-1552
ISSN:0284-1851
Container-title:Acta Radiologica
language:en
Short-container-title:Acta Radiol

Author:

Cui En-Ming¹^ORCID,Lin Fan²,Li Qing³,Li Rong-Gang³,Chen Xiang-Meng¹,Liu Zhuang-Sheng¹,Long Wan-Sheng¹

Affiliation:

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

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

Link

http://journals.sagepub.com/doi/pdf/10.1177/0284185119830282

Reference34 articles.

1. Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation

2. Angiomyolipoma: imaging findings in lesions with minimal fat.

3. Incidence of Benign Pathologic Lesions at Partial Nephrectomy for Presumed RCC Renal Masses: Japanese Dual-Center Experience with 176 Consecutive Patients

4. Incidence of benign pathologic findings at partial nephrectomy for solitary renal mass presumed to be renal cell carcinoma on preoperative imaging

5. Angiomyolipoma with minimal fat: Differentiation from papillary renal cell carcinoma by helical CT

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