Literature DB >> 27235451

Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma.

Saelin Oh1, Deuk Jae Sung1, Kyung Sook Yang2, Ki Choon Sim1, Na Yeon Han1, Beom Jin Park1, Min Ju Kim1, Sung Bum Cho1.   

Abstract

Background Identification of clinical features to determine the aggressive potential of tumors is highly warranted to stratify patients for adequate treatment. Computed tomography (CT) imaging features of clear cell renal cell carcinoma (ccRCC) may contribute to personalized risk assessment. Purpose To assess the correlation between CT imaging features and Fuhrman grade of ccRCC, and to identify the predictors of high Fuhrman grade in conjunction with tumor size. Material and Methods CT scans of 169 patients with 173 pathologically proven ccRCCs were retrospectively reviewed in consensus by two radiologists for the presence of intratumoral necrosis and intratumoral cyst and tumor size. Histologic grade was classified as either low (Fuhrman grade I or II) or high (Fuhrman grade III or IV). Statistical significance was evaluated by using univariate, multivariate regression, receiver operating characteristic (ROC) curve, and Spearman correlation analyses. Results On CT, 20 of the 173 tumors had intratumoral cysts, 60 had intratumoral necrosis, and 93 showed entirely solid tumors. The odds of high grade were higher with intratumoral necrosis and entirely solid tumor than with intratumoral cyst ( P < 0.03). Intratumoral necrosis showed a significantly high odds ratio of 25.73 for high Fuhrman grade. The ROC curve showed a threshold tumor size of 36 mm to predict high Fuhrman grade for overall tumors (area under the ROC curve, 0.70). In ccRCCs with intratumoral necrosis or cyst, tumor size did not significantly correlate with Fuhrman grade. Conclusion Intratumoral necrosis on CT was a strong and independent predictor of biologically aggressive ccRCCs, irrespective of tumor size.

Entities:  

Keywords:  Fuhrman grade; Multidetector computed tomography; clear cell renal cell carcinoma

Mesh:

Year:  2016        PMID: 27235451     DOI: 10.1177/0284185116649795

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  12 in total

1.  CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.

Authors:  Yu Deng; Erik Soule; Aster Samuel; Sakhi Shah; Enming Cui; Michael Asare-Sawiri; Chandru Sundaram; Chandana Lall; Kumaresan Sandrasegaran
Journal:  Eur Radiol       Date:  2019-05-24       Impact factor: 5.315

2.  2-[18F]FDG PET/CT parameters associated with WHO/ISUP grade in clear cell renal cell carcinoma.

Authors:  Yanyan Zhao; Caixia Wu; Wei Li; Xueqi Chen; Ziao Li; Xuhe Liao; Yonggang Cui; Guangyu Zhao; Meng Liu; Zhanli Fu
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-08-19       Impact factor: 9.236

3.  Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features.

Authors:  Dongzhi Cen; Li Xu; Siwei Zhang; Zhiguang Chen; Yan Huang; Ziqi Li; Bo Liang
Journal:  Eur Radiol       Date:  2019-03-15       Impact factor: 5.315

4.  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

5.  Performance of CT radiomics in predicting the overall survival of patients with stage III clear cell renal carcinoma after radical nephrectomy.

Authors:  Dong Han; Nan Yu; Yong Yu; Taiping He; Xiaoyi Duan
Journal:  Radiol Med       Date:  2022-07-14       Impact factor: 6.313

6.  Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.

Authors:  Shengsheng Lai; Lei Sun; Jialiang Wu; Ruili Wei; Shiwei Luo; Wenshuang Ding; Xilong Liu; Ruimeng Yang; Xin Zhen
Journal:  Cancer Manag Res       Date:  2021-02-04       Impact factor: 3.989

7.  Factors associated with postoperative renal sinus invasion and perinephric fat invasion in renal cell cancer: treatment planning implications.

Authors:  Dong Ni; Xin Ma; Hong-Zhao Li; Yu Gao; Xin-Tao Li; Yu Zhang; Qing Ai; Qing-Bo Huang; Jun-Yao Duan; Xu Zhang
Journal:  Oncotarget       Date:  2017-12-15

8.  Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.

Authors:  Xiaoqing Sun; Lin Liu; Kai Xu; Wenhui Li; Ziqi Huo; Heng Liu; Tongxu Shen; Feng Pan; Yuqing Jiang; Mengchao Zhang
Journal:  Medicine (Baltimore)       Date:  2019-04       Impact factor: 1.817

9.  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

10.  Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas.

Authors:  Ping Wang; Xu Pei; Xiao-Ping Yin; Jia-Liang Ren; Yun Wang; Lu-Yao Ma; Xiao-Guang Du; Bu-Lang Gao
Journal:  Sci Rep       Date:  2021-07-02       Impact factor: 4.379

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