Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information-Reference-Cited by-同舟云学术

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Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information

Published:2018-12 Issue:1 Volume:8 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Zhao Huiling,Cao Yuting,Wang Yue,Zhang Liya,Chen Chen,Wang Yaoyan,Lu Xiaofan,Liu Shengjie,Yan Fangrong

Funder

the National Social Science Fund (China) No.16BTJ021

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-018-35981-5.pdf

Reference15 articles.

1. Sejima, T. et al. Oncological and functional outcomes after radical nephrectomy for renal cell carcinoma: a comprehensive analysis of prognostic factors. J. International Journal of Urology. 20, 382–389 (2013).

2. Patard, J. J. et al. Use of the university of california los angeles integrated staging system to predict survival in renal cell carcinoma: an international multicenter study. J. J Clin Oncol. 22, 3316–3322 (2004).

3. Ficarra, V. et al. External validation of the mayo clinic stage, size, grade and necrosis (SSIGN) score to predict cancer specific survival using a European series of conventional renal cell carcinoma. J. J Urol. 175, 1235–1239 (2006).

4. Schmitt, N. & Ployhart, R. E. Estimates of cross-validity for stepwise regression and with predictor selection. J. Journal of Applied Psychology. 84, 50–57 (1999).

5. Bozdogan, H. Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. J. Psychometrika. 52, 345–370 (1987).

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