Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat-Reference-Cited by-同舟云学术

Role of quantitative computed tomography texture analysis in the prediction of adherent perinephric fat

Published:2018-04-19 Issue:10 Volume:36 Page:1635-1642
ISSN:0724-4983
Container-title:World Journal of Urology
language:en
Short-container-title:World J Urol

Author:

Khene Zine‐Eddine^ORCID,Bensalah Karim,Largent Axel,Shariat Shahrokh,Verhoest Gregory,Peyronnet Benoit,Acosta Oscar,DeCrevoisier Renaud,Mathieu Romain

Funder

French Young Urological Association

Publisher

Springer Science and Business Media LLC

Subject

Urology

Link

http://link.springer.com/article/10.1007/s00345-018-2292-9/fulltext.html

Reference26 articles.

1. Patard J-J, Rodriguez A, Rioux-Leclercq N, Guillé F, Lobel B (2002) Prognostic significance of the mode of detection in renal tumours. BJU Int 90(4):358–363

2. Ljungberg B, Bensalah K, Canfield S, Dabestani S, Hofmann F, Hora M et al (2015) EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol 67(5):913–924

3. Simhan J, Smaldone MC, Tsai KJ, Canter DJ, Li T, Kutikov A et al (2011) Objective measures of renal mass anatomic complexity predict rates of major complications following partial nephrectomy. Eur Urol 60(4):724–730

4. Mari A, Antonelli A, Bertolo R, Bianchi G, Borghesi M, Ficarra V et al (2017) Predictive factors of overall and major postoperative complications after partial nephrectomy: results from a multicenter prospective study (The RECORd 1 project). Eur J Surg Oncol EJSO 43(4):823–830

5. Kocher NJ, Kunchala S, Reynolds C, Lehman E, Nie S, Raman JD (2016) Adherent perinephric fat at minimally invasive partial nephrectomy is associated with adverse peri-operative outcomes and malignant renal histology. BJU Int 117(4):636–641

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning;International Journal of Medical Informatics;2024-07

2. Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends;Frontiers in Oncology;2023-09-01

3. Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey;State of the Art in Neural Networks and Their Applications;2023

4. A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis;BMC Urology;2022-09-12

5. Clinical Implication of Adherent Perinephric Fat in Robot-Assisted Partial Nephrectomy: Validation With Video Review;Frontiers in Surgery;2022-04-08