Serous neoplasms of the pancreas share many, but not all aspects of their microvascular and angiogenic profile with low-grade clear cell renal cell carcinomas

Context While biopsies are now increasingly being performed for the diagnosis of renal cortical neoplasms, the influence of the rendered pathological diagnoses on the clinical management is only rarely documented. Objectives To report our experience with consecutively performed renal biopsies and the potential impact of the diagnosis on subsequent clinical management. Design Material from needle biopsies performed consecutively at our institution between 2006 and 2011 was reviewed. The influence of the reported pathology results on the clinical management was determined from patient follow-up medical record review. Results In total, 218 percutaneous biopsies for renal masses were performed during this period. Among them, 181 (83%) yielded neoplastic tissue, including 81 clear cell renal cell carcinomas, 29 low-grade oncocytic neoplasms, 7 papillary renal cell carcinomas, 5 clear cell papillary renal cell carcinomas, 5 angiomyolipomas, and 14 urothelial carcinomas. Fourteen additional cases (6%) contained lesional material from clinically known nonneoplastic processes, for a total diagnostic yield of 89%. Twenty-three (11%) were nonrepresentative of lesional tissue. In 10 of these, repeat biopsies or resections established the diagnosis of renal tumors. Biopsy diagnosis was confirmed in 29 of 30 cases (97%) on subsequent nephrectomy. Following the biopsy diagnosis, there were significant differences in the clinical management; overall, 79% of clear cell renal cell carcinomas received therapeutic interventions, and 17% were put on active surveillance. In contrast, 77% of the benign or low-grade lesions were put on active surveillance. Conclusions Accurate and specific diagnosis can be rendered on renal core biopsy in most renal tumors, and the biopsy diagnosis can have a definitive role in their clinical management.

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Low-grade metastases from Clear Cell Renal Cell Carcinomas (CCRCC): A study of E- and N- Cadherin expression in 19 cases

10.26226/morressier.578f37fbd462b8028d890461 ◽

2016 ◽

Author(s):

Verónica Caamaño Villaverde

Keyword(s):

Renal Cell ◽

Clear Cell ◽

Low Grade ◽

Renal Cell Carcinomas

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Low-grade metastases in high-grade clear cell renal cell carcinomas

Annals of Diagnostic Pathology ◽

10.1016/j.anndiagpath.2015.10.011 ◽

2016 ◽

Vol 20 ◽

pp. 13-18 ◽

Cited By ~ 2

Author(s):

José I. López ◽

Lorena Mosteiro ◽

Rosa Guarch ◽

Gorka Larrinaga ◽

Rafael Pulido ◽

...

Keyword(s):

Renal Cell ◽

Clear Cell ◽

Low Grade ◽

High Grade ◽

Renal Cell Carcinomas

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

Frontiers in Oncology ◽

10.3389/fonc.2021.659969 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xu Pei ◽

Ping Wang ◽

Jia-Liang Ren ◽

Xiao-Ping Yin ◽

Lu-Yao Ma ◽

...

Keyword(s):

Renal Cell ◽

Clear Cell ◽

Multivariate Logistic Regression Model ◽

Low Grade ◽

Training Set ◽

Tree Model ◽

Renal Cell Carcinomas ◽

Svm Model ◽

Enhanced Computed Tomography ◽

Testing Set

PurposeThis study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.Materials and MethodsCT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.ResultsA total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.ConclusionRadiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.

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PD14-07 DIFFERENTIATION BETWEEN CLEAR CELL RENAL CELL CARCINOMAS AND ONCOCYTOMAS USING TEXTURE ANALYSIS OF CT IMAGES

The Journal of Urology ◽

10.1016/j.juro.2016.02.1004 ◽

2016 ◽

Vol 195 (4S) ◽

Cited By ~ 2

Author(s):

Vinay Duddalwar ◽

Xuejun Zhang ◽

Darryl Hwang ◽

Steven Cen ◽

Felix Yap ◽

...

Keyword(s):

Texture Analysis ◽

Renal Cell ◽

Clear Cell ◽

Ct Images ◽

Renal Cell Carcinomas

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Strength of molecular cytogenetic analyses for adjusting the diagnosis of renal cell carcinomas: a study of four cases with complete or hybrid morphology of clear cell carcinomas

Cancer Genetics and Cytogenetics ◽

10.1016/j.cancergencyto.2010.07.099 ◽

2010 ◽

Vol 203 (1) ◽

pp. 91

Author(s):

Laurence Bianchini ◽

Juliette Haudebourg ◽

Benjamin Hoch ◽

Thibault Fabas ◽

Fanny Burel-Vandenbos ◽

...

Keyword(s):

Renal Cell ◽

Clear Cell ◽

Renal Cell Carcinomas ◽

Molecular Cytogenetic ◽

Cytogenetic Analyses

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Value of intravoxel incoherent motion for differential diagnosis of renal tumors

Acta Radiologica ◽

10.1177/0284185118778884 ◽

2018 ◽

Vol 60 (3) ◽

pp. 382-387 ◽

Cited By ~ 6

Author(s):

Qingqiang Zhu ◽

Wenrong Zhu ◽

Jing Ye ◽

Jingtao Wu ◽

Wenxin Chen ◽

...

Keyword(s):

Renal Cell ◽

Clear Cell ◽

Threshold Value ◽

Intravoxel Incoherent Motion ◽

Renal Tumors ◽

Threshold Values ◽

Renal Cell Carcinomas ◽

Perfusion Fraction ◽

D Values ◽

Tumor Types

Background Few studies have reported on the use of intravoxel incoherent motion (IVIM) for renal tumors. Purpose To investigate the value of IVIM for distinguishing renal tumors. Material and Methods Thirty-one patients with clear cell renal cell carcinomas (CCRCCs), 13 patients with renal angiomyolipomas with minimal fat (RAMFs), eight patients with chromophobe renal cell carcinomas (ChRCCs), and ten patients with papillary renal cell carcinomas (PRCCs) were examined. The tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated. Results The D and f values were highest for CCRCCs, lowest for PRCCs, and intermediate for ChRCCs and RAMFs ( P < 0.05). The D values of CCRCCs differed significantly from those of ChRCCs and PRCCs ( P < 0.05). The D* values were highest for RAMFs, lowest for ChRCCs, and intermediate for CCRCCs and PRCCs ( P < 0.05). Statistically significant differences were observed between the D* values of CCRCCs and RAMFs ( P < 0.05). The D* values of the CCRCCs differed significantly from the D* values of the ChRCCs ( P < 0.05). Using the D and f values of 1.10 and 0.41, respectively, as the threshold values for differentiating CCRCCs from RAMFs, ChRCCs, and PRCCs, the best results had sensitivities of 81.0% and 66.8% and specificities of 85.7% and 81.0%, respectively. Using the D* value of 0.038 as the threshold value for differentiating RAMFs from CCRCCs, ChRCCs, and PRCCs, the best result obtained had a sensitivity of 90.5% and specificity of 76.2%. Conclusion IVIM may provide information for differentiating renal tumor types.

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