Differentiating Benign from Malignant Renal Tumors Using T2 ‐ and Diffusion‐Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists-Reference-Cited by-同舟云学术

Differentiating Benign from Malignant Renal Tumors Using T2 ‐ and Diffusion‐Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists

Published:2021-08-30 Issue: Volume: Page:
ISSN:1053-1807
Container-title:Journal of Magnetic Resonance Imaging
language:en
Short-container-title:J Magn Reson Imaging

Author:

Xu Qing¹^ORCID,Zhu QingQiang¹,Liu Hao²,Chang LuFan²,Duan ShaoFeng³,Dou WeiQiang³,Li SaiYang⁴,Ye Jing¹

Affiliation:

1. Department of Medical Imaging, Clinic Medical School Yangzhou University, Northern Jiangsu Province Hospital Yangzhou China

2. Yizhun Medical AI Beijing China

3. GE Healthcare Beijing China

4. Department of Urology The Third Affiliated Hospital of Soochow University Changzhou China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Radiology Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.27900

Reference40 articles.

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

2. Second Prize: Frequency of Benign Renal Cortical Tumors and Histologic Subtypes Based on Size in a Contemporary Series: What to Tell our Patients

3. Incidence of Benign Lesions for Clinically Localized Renal Masses Smaller Than 7 cm in Radiological Diameter: Influence of Sex

4. Characterization of small (<4 cm) solid renal masses by computed tomography and magnetic resonance imaging: Current evidence and further development

5. Does computed tomography still have limitations to distinguish benign from malignant renal tumors for radiologists?;Toshitaka S;Urol Int,2017

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