Low Enhancement on Multiphase Contrast-Enhanced CT Images: An Independent Predictor of the Presence of High Tumor Grade of Clear Cell Renal Cell Carcinoma

2014 ◽  
Vol 203 (3) ◽  
pp. W295-W300 ◽  
Author(s):  
Ye-Hua Zhu ◽  
Xun Wang ◽  
Jin Zhang ◽  
Yong-Hui Chen ◽  
Wen Kong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Independent Predictor ◽  
Tumor Grade ◽  
Cell Renal Cell Carcinoma ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

scholarly journals Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography

SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Kousei Ishigami ◽  
Leandro V. Leite ◽  
Marius G. Pakalniskis ◽  
Daniel K. Lee ◽  
Danniele G. Holanda ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Tumor Grade ◽  
Cell Renal Cell Carcinoma ◽  
Contrast Enhanced ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

scholarly journals Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Yan ◽  
Guangjie Yang ◽  
Jingjing Cui ◽  
Wenjie Miao ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clinical Factors ◽  
Cell Renal Cell Carcinoma ◽  
Incremental Value ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced

PurposeTo develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation.Materials and MethodsOne hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination.ResultsRad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808–0.940 vs. 0.803; 95% CI: 0.705–0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800–0.921 vs. 0.846; 95% CI: 0.777–0.915, P < 0.05).ConclusionsThe radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.

scholarly journals MP66-12 BIM EXPRESSION IN PERITUMORAL LYMPHOCYTES IS AN INDEPENDENT PREDICTOR OF SURVIVAL IN PATIENTS WITH METASTATIC CLEAR CELL RENAL CELL CARCINOMA

2018 ◽  
Vol 199 (4S) ◽  
Author(s):  
Bimal Bhindi ◽  
John Cheville ◽  
Christine Lohse ◽  
Ross Mason ◽  
Susan Harrington ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Independent Predictor ◽  
Cell Renal Cell Carcinoma

scholarly journals Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

2019 ◽  
Vol 18 ◽  
pp. 153601211988316 ◽  
Author(s):  
Guangjie Yang ◽  
Aidi Gong ◽  
Pei Nie ◽  
Lei Yan ◽  
Wenjie Miao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Texture Analysis ◽  
Renal Cell ◽  
Texture Features ◽  
Regions Of Interest ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
2D And 3D

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration ( P > .05). There was no significant difference in AUC between the 2 models ( P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.

Expression of theForkheadTranscription Factor FOXP1 is Associated with Tumor Grade and Ki67 Expression in Clear Cell Renal Cell Carcinoma

2011 ◽  
Vol 29 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Marieta I. Toma ◽  
Thomas Weber ◽  
Matthias Meinhardt ◽  
Stefan Zastrow ◽  
Marc-Oliver Grimm ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Tumor Grade ◽  
Cell Renal Cell Carcinoma ◽  
Ki67 Expression
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