Literature DB >> 28686936

Differentiation of low- and high-grade clear cell renal cell carcinoma: Tumor size versus CT perfusion parameters.

Chao Chen1, Qinqin Kang2, Bing Xu2, Hairuo Guo3, Qiang Wei4, Tiegong Wang2, Hui Ye5, Xinhuai Wu6.   

Abstract

PURPOSE: To compare the utility of tumor size and CT perfusion parameters for differentiation of low- and high-grade clear cell renal cell carcinoma (RCC).
MATERIALS AND METHODS: Tumor size, Equivalent blood volume (Equiv BV), permeability surface-area product (PS), blood flow (BF), and Fuhrman pathological grading of clear cell RCC were retrospectively analyzed.
RESULTS: High-grade clear cell RCC had significantly higher tumor size and lower PS than low grade. Tumor size positively correlated with Fuhrman grade, but PS negatively did.
CONCLUSIONS: Tumor size and PS were significantly independent indexes for differentiating high-grade from low-grade clear cell RCC.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Clear cell renal cell carcinoma; Computed tomography; Fuhrman grade; Perfusion imaging; Size

Mesh:

Year:  2017        PMID: 28686936     DOI: 10.1016/j.clinimag.2017.06.010

Source DB:  PubMed          Journal:  Clin Imaging        ISSN: 0899-7071            Impact factor:   1.605


  7 in total

1.  Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT.

Authors:  Dong Han; Yong Yu; Nan Yu; Shan Dang; Hongpei Wu; Ren Jialiang; Taiping He
Journal:  Br J Radiol       Date:  2020-08-12       Impact factor: 3.039

2.  Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading.

Authors:  Israa Alnazer; Omar Falou; Pascal Bourdon; Thierry Urruty; Rémy Guillevin; Mohamad Khalil; Ahmad Shahin; Christine Fernandez-Maloigne
Journal:  J Med Imaging (Bellingham)       Date:  2022-09-13

3.  Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma.

Authors:  Jun Gu; Zexi He; Yinglong Huang; Ting Luan; Zhenjie Chen; Jiansong Wang; Mingxia Ding
Journal:  Dis Markers       Date:  2022-05-23       Impact factor: 3.464

4.  Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

Authors:  Shawn Haji-Momenian; Zixian Lin; Bhumi Patel; Nicole Law; Adam Michalak; Anishsanjay Nayak; James Earls; Murray Loew
Journal:  Abdom Radiol (NY)       Date:  2020-03

5.  CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma.

Authors:  Meiyi Yang; Xiaopeng He; Lifeng Xu; Minghui Liu; Jiali Deng; Xuan Cheng; Yi Wei; Qian Li; Shang Wan; Feng Zhang; Lei Wu; Xiaomin Wang; Bin Song; Ming Liu
Journal:  Front Oncol       Date:  2022-09-28       Impact factor: 5.738

6.  Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas.

Authors:  Xu Pei; Ping Wang; Jia-Liang Ren; Xiao-Ping Yin; Lu-Yao Ma; Yun Wang; Xi Ma; Bu-Lang Gao
Journal:  Front Oncol       Date:  2021-05-26       Impact factor: 6.244

7.  Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC).

Authors:  Jinyan Wei; Jianhong Zhao; Xueling Zhang; Dan Wang; Wenjuan Zhang; Zhiping Wang; Junlin Zhou
Journal:  PLoS One       Date:  2018-05-01       Impact factor: 3.240

  7 in total

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