Prognostic value of nuclear area index in combination with the World Health Organization grading system for patients with renal cell carcinoma

Kanamaru, Hiroshi; Akino, Hironobu; Suzuki, Yuji; Noriki, Sakon; Okada, Kenichiro

doi:10.1016/s0090-4295(00)00910-9

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…We therefore hypothesized that the incorporation of an internal standard (normal cell nuclei) could minimize the effect of such tissue processing artifacts, and we proposed a new morphometric parameter, NAI, which is defined as the ratio of MNA for cancer cells to MNA for normal cells. The NAI was shown to be an independent prognostic variable for renal cell carcinoma in a recent study 15 . The present results showing the superiority of NAI over MNA in bladder cancer patients could further support our hypothesis.…”

Section: Discussionsupporting

confidence: 87%

“…Using Photograb‐2500 version 1.1 (Fuji, Tokyo, Japan), the final magnification viewed on a monitor was ×2760. For the measurement of nuclear areas, all the nuclei of cancer cells (mean 230) and normal transitional cells (mean 130) with clear nuclear margins were outlined using a mouse, and the nuclear areas were automatically calculated with a computerized image analysis system (Mac SCOPE v2.53; Mitani Corporation, Fukui, Japan) as described by Kanamaru et al 15 . The MNA was calculated as the mean value of the nuclear areas of either cancer cells or normal transitional cells for each case.…”

Section: Methodsmentioning

confidence: 99%

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Prognostic value of nuclear area index in patients with bladder cancer

et al. 2004

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Background : To assess the prognostic usefulness of the nuclear area index (NAI), a new nuclear morphometric parameter expressed as the mean nuclear area (MNA) ratio of cancer to normal transitional cells in patients with bladder cancer, who have undergone radical cystectomy. Methods : Measurements of the nuclear areas of cancer and normal transitional cells were carried out on the histological slides of 73 patients with bladder cancer. The clinical usefulness of MNA, NAI, grade, and TNM categories for the prediction of the cause-specific survival of the patients was examined.Results : The median values of MNA and NAI in the 73 patients were 39 m m 2 and 1.2, respectively. Cause-specific survival rates of the patients were calculated according to stage (T1-2 vs T3-4), grade (grade 2 vs grade 3), MNA ( < 39 m m 2 vs ≥ 39 m m 2 ) and NAI value ( < 1.2 vs ≥ 1.2). Using univariate analysis, all these parameters were statistically significant prognostic factors. However, by multivariate analysis, NAI was the only independent variable for the survival of the patients ( P < 0.01). Causespecific survival rates of patients with NAI values of less than 1.2 were significantly higher than those with NAI values of 1.2 or more, in both grade 2 and grade 3 tumors. Conclusions : These results suggest that NAI could provide improved prognostic information for patients with bladder cancer.

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Section: Discussionsupporting

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Prognostic value of nuclear area index in patients with bladder cancer

et al. 2004

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“…The patients enrolled were pathologically diagnosed with RCC and signed informed consent forms. According to the World Health Organization grading system ( 28 ), RCC tissues were divided into the following three groups in the present study: i) High-grade, ii) middle-grade; and iii) low-grade.…”

Section: Methodsmentioning

confidence: 99%

Suppressive effects of RASAL2 on renal cell carcinoma via SOX2/ERK/p38 MAPK pathway

Wang¹,

Xia²,

He³

et al. 2020

Exp Ther Med

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Metastatic renal cell carcinoma (RCC) is associated with poor prognosis. Ras protein activator like 2 (RASAL2) protein has been previously demonstrated to serves as a tumor suppressor in a variety of malignancies. Therefore, the aim of the present study was to investigate the role of RASAL2 in RCC. Reverse transcription-quantitative PCR, western blot analysis and immunohistochemistry were performed to measure mRNA and protein expression in RCC tissues, whilst immunofluorescence and western blotting were performed to evaluate protein expression in RCC cells. A Cell Counting Kit-8 and 5-bromo-2'-deoxyuridine staining were applied to determine cell viability, and Transwell assays were conducted to measure RCC cell invasion and migration. RASAL2 expression was identified to be downregulated in RCC tissues, which associate negatively with RCC pathological grade. Sox2 expression, in addition to ERK1/2 and p38 MAPK phosphorylation, were demonstrated to be increased in RCC tissues. In RCC cells, RASAL2 overexpression decreased the expression of Sox2 and the activation of ERK1/2 and p38 MAPK. Physiologically, RASAL2 overexpression decreased RCC cell viability, invasion and migration. The expression of metalloproteinase-2/9 and tissue inhibitor of metalloproteinase 1 were also identified to be decreased and increased by RASAL2 overexpression, respectively. By contrast, RASAL2 knockdown exerted opposite effects on RCC cells compared with those observed following RASAL2 overexpression. RASAL2 expression decreased RCC cell viability, migration and invasion, which was demonstrated to be associated with the inactivation of SOX2/ERK1/2/p38 MAPK signaling. These results suggest that RASAL2 may potentially serve as a potential target for the development of novel therapeutic intervention strategies against RCC.

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“…We find that our method can identify new types of image features (i.e., area bin10) that are related to large nuclei. It has been demonstrated that the ccRCC patients with large values of nuclei size have worse prognosis [7] than other patients. As to genomic features, two novel eigengenes (i.e., eigengene 9, eigengene 14) are identified.…”

Section: Optimizationmentioning

confidence: 99%

Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer

Shao

Cheng

Sun

et al. 2018

Lecture Notes in Computer Science

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Existing studies have demonstrated that combining genomic data and histopathological images can better stratify cancer patients with distinct prognosis than using single biomarker, for di erent biomarkers may provide ff complementary information. However, these multi-modal data, most highdimensional, may contain redundant features that will deteriorate the performance of the prognosis model, and therefore it has become a challenging problem to select the informative features for survival analysis from the redundant and heterogeneous feature groups. Existing feature selection methods assume that the survival information of one patient is independent to another, and thus miss the ordinal relationship among the survival time of di erent patients. To solve this issue, we make use of the important ordinal ff survival information among di erent patients and propose an ordinal sparse ff canonical correlation analysis (i.e., OSCCA) framework to simultaneously identify important image features and eigengenes for survival analysis. Specifically, we formulate our framework basing on sparse canonical correlation analysis model, which aims at finding the best linear projections so that the highest correlation between the selected image features and eigen-genes can be achieved. In addition, we also add constrains to ensure that the ordinal survival information of di erent patients is preserved after projection. We evaluate the ff e ectiveness of our method on an early-stage renal cell carcinoma dataset. ff Experimental results demonstrate that the selected features correlated strongly with survival, by which we can achieve better patient stratification than the comparing methods.

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Prognostic value of nuclear area index in combination with the World Health Organization grading system for patients with renal cell carcinoma

Cited by 8 publications

References 9 publications

Prognostic value of nuclear area index in patients with bladder cancer

Prognostic value of nuclear area index in patients with bladder cancer

Suppressive effects of RASAL2 on renal cell carcinoma via SOX2/ERK/p38 MAPK pathway

Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer

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