Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis-Reference-Cited by-同舟云学术

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

Published:2021-10-12 Issue: Volume: Page:
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Luo Shiwei,Wei Ruili,Lu Songlin,Lai Shengsheng,Wu Jialiang,Wu Zhe,Pang Xinrui,Wei Xinhua,Jiang Xinqing,Zhen Xin,Yang Ruimeng^ORCID

Funder

national natural science foundation of china

natural science foundation of guangdong province

science and technology project of guangzhou

special fund for the construction of high-level key clinical specialty (medical imaging) in guangzhou.

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-021-08322-w.pdf

Reference33 articles.

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3. Znaor A, Lortet-Tieulent J, Laversanne M, Jemal A, Bray F (2015) International variations and trends in renal cell carcinoma incidence and mortality. Eur Urol 67:519–530

4. Leibovich BC, Lohse CM, Crispen PL et al (2010) Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. J Urol 183:1309–1315

5. Ljungberg B, Albiges L, Abu-Ghanem Y et al (2019) European Association of Urology Guidelines on Renal Cell Carcinoma: the 2019 update. Eur Urol 75:799–810

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