scholarly journals Clear Cell Carcinomas of the Mullerian System: Does the Pathogenesis Vary Depending on Their Nuclear Grade and Their Association with Endometriosis? An Immunohistochemical Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Ahmad Alduaij ◽  
Katrine Hansen ◽  
Tahreem A. Karim ◽  
Cunxian Zhang ◽  
Michelle M. Lomme ◽  
...  
Keyword(s):  
Clear Cell ◽  
Immunohistochemical Analysis ◽  
Nuclear Grade ◽  
High Grade ◽  
Ki 67 ◽  
High Nuclear Grade ◽  
Positive Immunostaining

Clear cell carcinomas (CCC) of the mullerian system are considered high grade tumors, but morphologically, the cells of CCC show both low and high grade features. The aims of the current study were to categorize CCC into low and high nuclear grade types, correlate their association with endometriosis, and then observe possible variations in pathogenesis based on their expression of p53 and Ki-67. We studied 41 pure mullerian CCCs and designated each as either a high (HNG) or low (LNG) nuclear grade tumor. Morphologically, 17 (41%) CCCs were LNG and 24 (59%) were HNG. Nine (38%) HNG and 2 (12%) LNG tumors showed positive immunostaining with p53. Endometriosis was associated with 8 (47%) LNG tumors and 8 (33%) HNG CCCs. Of the 11 cases with p53 alteration, 4 (1 LNG and 3 HNG) were associated with endometriosis. Conclusions: HNG CCCs, irrespective of their association with endometriosis, have alterations of p53. In general, LNG ovarian and endometrial CCCs, irrespective of their association with endometriosis/adenomyosis, are less likely to show p53 alteration. It appears that mullerian CCCs may have variable pathogenesis depending on their nuclear grade and association with endometriosis. A larger study is needed to validate these findings.

scholarly journals New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma

2020 ◽  
Vol Volume 11 ◽  
pp. 10921-10928 ◽  
Author(s):  
Zhan Feng ◽  
Shuangshuang Lou ◽  
Lixia Zhang ◽  
Liang Zhang ◽  
Wenting Lan ◽  
...  
Keyword(s):  
Renal Carcinoma ◽  
Clear Cell ◽  
Nuclear Grade ◽  
Clear Cell Renal Carcinoma ◽  
Centrality Index ◽  
High Nuclear Grade ◽  
Cell Renal Carcinoma

Utility of the Apparent Diffusion Coefficient for Distinguishing Clear Cell Renal Cell Carcinoma of Low and High Nuclear Grade

2010 ◽  
Vol 195 (5) ◽  
pp. W344-W351 ◽  
Author(s):  
Andrew B. Rosenkrantz ◽  
Benjamin E. Niver ◽  
Erin F. Fitzgerald ◽  
James S. Babb ◽  
Hersh Chandarana ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Nuclear Grade ◽  
Cell Renal Cell Carcinoma ◽  
High Nuclear Grade ◽  
Apparent Diffusion

642: High Nuclear Grade is Associated with Increased Growth Kinetics During Active Surveillance of Clear Cell Renal Carcinoma

2007 ◽  
Vol 177 (4S) ◽  
pp. 215-216 ◽  
Author(s):  
PaulL Crispen ◽  
John Dougherty ◽  
Richard E. Greenberg ◽  
David Y.T. Chen ◽  
Robert G. Uzzo
Keyword(s):  
Active Surveillance ◽  
Growth Kinetics ◽  
Renal Carcinoma ◽  
Clear Cell ◽  
Nuclear Grade ◽  
Clear Cell Renal Carcinoma ◽  
High Nuclear Grade ◽  
Cell Renal Carcinoma

Association of p16 and Ki-67 with Risk of Recurrence in Previously Treated Cervical High-Grade Squamous Intraepithelial Lesions

2021 ◽  
pp. 1-6
Author(s):  
Carlos Germán Arredondo-Gálvez ◽  
Denise Acuña-González ◽  
David Cantú-de-León ◽  
José Gregorio Chanona-Vilchis ◽  
Alejandro Avilés-Salas ◽  
...  
Keyword(s):  
Immunohistochemical Analysis ◽  
Time Frame ◽  
Control Group ◽  
High Grade ◽  
Ki 67 ◽  
Exact Test ◽  
Risk Of Recurrence ◽  
Logistic Regression Models ◽  
Previously Treated ◽  
P16 Expression

<b><i>Objective:</i></b> Our main objective was to assess the association between the markers p16 and Ki-67 and recurrence of disease in patients previously treated for cervical high-grade squamous intraepithelial lesion (HSIL). <b><i>Design:</i></b> This is a case-control study at the National Cancer Institute conducted between 2005 and 2015. Of the patients with a pathologically confirmed diagnosis of HSIL, 107 cases were selected. They were divided into 2 groups: 28 cases with recurrence after treatment and a control group of 79 patients without recurrence. We identified clinical, pathological, and treatment variables. <b><i>Methods:</i></b> Two experienced pathologists performed immunohistochemical analysis of biomarkers; they agreed on their interpretation, and we calculated the odds ratios (ORs) associated with recurrence. For group comparisons, we used the Wilcoxon signed-rank, χ<sup>2</sup>, or Fisher’s exact test, depending on the type of variable. We conducted logistic regression models to estimate ORs and determine the factors associated with recurrence. The recurrence-free period was defined as the time frame between conization and either recurrence of disease or the last date the patient was seen. We used Kaplan-Meier plots to visualize survival curves and log-rank tests to compare the curves. We established a <i>p</i> value &#x3c;0.05 as statistically significant. <b><i>Results:</i></b> After pathologists performed immunohistochemical analysis, they achieved an agreement level of 83.7% for p16 and 60% for Ki-67. We did not find an association between recurrence and either p16 expression (<i>p</i> = 0.69) or the percentage of Ki-67 expression (<i>p</i> = 0.71). The recurrence-free period analysis did not reveal a difference in p16 expression (<i>p</i> = 0.57) nor in the percentage of Ki-67 expression in the 3-tiered scale (<i>p</i> = 0.56). <b><i>Limitations:</i></b> Our main limitation was a reduced sample size. <b><i>Conclusion:</i></b> We found no association between p16 and Ki-67 positivity and the risk of recurrence in previously treated HSIL.

PAI-1 expression and its regulation by promoter 4G/5G polymorphism in clear cell renal cell carcinoma

2011 ◽  
Vol 64 (10) ◽  
pp. 893-897 ◽  
Author(s):  
Jung-Woo Choi ◽  
Ju-Han Lee ◽  
Hong Seok Park ◽  
Young-Sik Kim
Keyword(s):  
Renal Cell Carcinoma ◽  
Distant Metastasis ◽  
Clear Cell ◽  
Disease Free Survival ◽  
Nuclear Grade ◽  
Cell Renal Cell Carcinoma ◽  
Free Survival ◽  
High Nuclear Grade ◽  
Disease Free ◽  
Pai 1

AimsTo characterise patients with high plasminogen activator inhibitor-1 (PAI-1) expression as oral PAI-1 antagonists are currently in preclinical trials, and to determine whether the PAI-1 promoter 4G/5G polymorphism regulates PAI-1 expression in clear cell renal cell carcinoma (CCRCC).MethodsPAI-1 expression was examined by immunohistochemistry in 69 CCRCC specimens. In addition, the promoter 4G/5G polymorphism was investigated by both allele-specific PCR and direct DNA sequencing.ResultsPAI-1 was overexpressed in 25/69 (36.2%) patients with CCRCC. PAI-1 staining was intense in tumour cells with a high Fuhrman nuclear grade and in spindle-shaped tumour cells. PAI‐1 expression was significantly associated with older age at diagnosis (p=0.027), high nuclear grade (p<0.001), advanced clinical stage (p=0.030) and distant metastasis (p=0.009). In survival analyses, PAI-1 expression was correlated with disease-free survival in Kaplan–Meier curves (p=0.015) but was not significant in the Cox hazards model (p=0.527). The frequencies of the promoter polymorphism were 24.6% (17/69) 4G/4G, 43.5% (30/69) 4G/5G and 31.9% (22/69) 5G/5G. The homozygous 4G/4G or 5G/5G group showed a tendency for a high nuclear grade (p=0.05) but the 4G/5G polymorphism was not related to other prognostic parameters. PAI-1 expression was poorly correlated with its promoter 4G/5G polymorphism (Spearman ρ=0.088).ConclusionsCCRCC with high PAI-1 expression is characterised by older age, high nuclear grade, advanced stage, distant metastasis and/or shortened disease-free survival. PAI-1 expression is not affected by the promoter 4G/5G polymorphism.

scholarly journals Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yingjie Xv ◽  
Fajin Lv ◽  
Haoming Guo ◽  
Xiang Zhou ◽  
Hao Tan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Roc Curve ◽  
Renal Cell ◽  
Clear Cell ◽  
Nuclear Grade ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Combined Model ◽  
High Nuclear Grade

Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Immunohistochemical analysis of kallikrein-related peptidases in the normal kidney and renal tumors: potential clinical implications

2010 ◽  
Vol 391 (4) ◽  
Author(s):  
Manal Gabril ◽  
Nicole M. White ◽  
Madeleine Moussa ◽  
Tsz-fung F. Chow ◽  
Shereen M. Metias ◽  
...  
Keyword(s):  
Clear Cell ◽  
Prognostic Significance ◽  
Immunohistochemical Analysis ◽  
Renal Tumors ◽  
Low Grade ◽  
High Grade ◽  
Cytoplasmic Staining ◽  
Chromophobe Rcc ◽  
Clear Cell Rcc ◽  
Granular Cytoplasmic Staining

Abstract Kallikrein-related peptidases (KLKs) have been shown to be differentially expressed in various malignancies and shown to be useful tumor markers. Previous immunohistochemistry (IHC) analysis demonstrated that KLKs 5, 6, 10, and 11 have a potential prognostic significance in renal cell carcinoma (RCC). To further explore the significance of KLKs, we examined KLKs 1, 6, 7, and 15 in different subtypes of renal tumors. KLK1 has stronger expression in high grade compared to low grade clear cell RCC. However, KLK6 and KLK7 show strong expression in low grade in contrast to high grade clear cell RCC. Furthermore, the expression of KLK7 can distinguish between oncocytoma and chromophobe RCC. Oncocytoma showed diffuse, strong granular cytoplasmic staining, but chromophobe RCC showed focal weak homogeneous cytoplasmic stain. The pattern of staining of different KLKs can also be helpful in differentiating some of the subtypes of renal tumors. Our results show the potential ability of KLKs to serve as diagnostic markers and expand previous data about the prognostic significance of KLKs in kidney cancer. In addition, our study is the first to show the ability of KLK staining to distinguish various types of kidney cancers when morphology is similar.

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