Nuclear image analysis of immunohistochemically stained cells in breast carcinomas

Haroske, G.; Dimmer, V.; Friedrich, Katrin; Meyer, W.; Thieme, Berit; Theissig, F; Kunze, K.D.

doi:10.1007/bf01457662

Cited by 12 publications

(16 citation statements)

References 20 publications

(13 reference statements)

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“…We, and many others, have shown that image analysis of nuclear features (size, shape, chromatin texture, and ploidy) can be prognostic for survival of breast cancer patients (Chapman et al, 2007). There have been previous reports of an association between p53 immunohistochemistry and nuclear features consistent with our observations, although a relationship with patient survival was not determined (Haroske et al, 1996; Friedrich et al, 1997). In this study we have shown that the protein based method and the DNA based method identify similar groups of patients with good and poor survival.…”

Section: Discussionsupporting

confidence: 88%

Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry

Axelrod¹,

Shah²,

Yang³

et al. 2012

CCO

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p53 protein detected immunohistochemically has not been accepted as a biomarker for breast cancer patients because of disparate reports of the relationship between the amount of p53 protein detected and patient survival. The purpose of this study was to determine experimental conditions and methods of data analysis for which p53 stain intensity could be prognostic for survival of young breast cancer patients. A tissue microarray of specimens from 93 patients was stained with anti-p53 antibody, and stain intensity measured with a computer-aided image analysis system. A cut-point at one standard deviation below the mean of the distribution of p53 stain intensity separated patients into two groups with significantly different survival. These results were confirmed by Quantitative Nuclear Grade determined by DNA-specific Feulgen staining. P53 provided information beyond ER and PR status. Therefore, under the conditions reported here, p53 protein can be an effective prognostic factor for young breast cancer patients.

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Section: Discussionsupporting

confidence: 88%

Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry

Axelrod¹,

Shah²,

Yang³

et al. 2012

CCO

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“…Oestrogen receptor-positive and -negative tumours could be distinguished on the basis of different nuclear features, such as area, DNA content, and certain texture features [16]. Other image cytometry studies showed that several features of nuclear contour and chromatin structure were signi®cantly different between breast tumour cells positive or negative for p53 or for progesterone receptor [17,18]. These ®ndings are interesting, because they show the ability of image cytometry to detect nuclear morphological changes which are related to the biology of the cell.…”

Section: Introductionmentioning

confidence: 99%

Nuclear cytometric changes in breast carcinogenesis

et al. 2001

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Breast cancer is thought to originate through progressively aberrant precursor lesions, paralleled by increasing morphological changes. The aim of this study was to quantify nuclear features by image cytometry in invasive breast cancer and its early (hyperplasia) and late (ductal carcinoma in situ) precursor lesions, in order to objectively describe nuclear changes in the spectrum of proliferative intraductal and invasive breast lesions. Image cytometry was performed on tissue sections of 20 samples of normal breast tissue, 71 of usual ductal hyperplasia (UDH), nine of atypical ductal hyperplasia (ADH), and 11 of well-differentiated and 13 of poorly differentiated ductal carcinoma in situ (DCIS) lesions. The invasive breast carcinomas consisted of 19 well-differentiated and 24 poorly differentiated lesions. Through the spectrum from normal breast tissue to invasive carcinoma, progressive changes in many nuclear features were measured. Significant differences were found between nuclei of florid ductal hyperplasia compared with mild and moderate ductal hyperplastic lesions, suggesting that florid ductal hyperplasia may be a more advanced lesion than assumed and may contain cancer precursor cells. No differences were found between ADH and well-differentiated DCIS, suggesting that these lesions are closely related. Feature values of well-differentiated DCIS were comparable to values found in well-differentiated invasive carcinoma and the same applied to poorly differentiated DCIS and invasive lesions. These results support the hypothesis that breast cancer develops through different routes of progression, one leading to well-differentiated invasive cancer through well-differentiated DCIS, and one leading to poorly differentiated invasive cancer through poorly differentiated DCIS. In conclusion, image cytometry reveals progressive changes in nuclear morphological and subvisual chromatin distribution features in the spectrum from intraductal proliferations to invasive breast cancer. This provides evidence for a progression from usual to atypical ductal hyperplasia and then to invasive cancer, through different routes for well-differentiated and poorly differentiated lesions.

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“…Local gray level differences and cell density combining with other morphological parameters are possible to follow cell culture development under various experimental conditions [29]. Hitherto, the relationship between malignancy-associated morphological features in single tumour cells and the expression of markers indicating functional properties of these cells remained widely unknown [65]. …”

Section: Tasks and Problemsmentioning

confidence: 99%

Recent Advances in Morphological Cell Image Analysis

Chen

Zhao

et al. 2012

Computational and Mathematical Methods in Medicine

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This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

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Nuclear image analysis of immunohistochemically stained cells in breast carcinomas

Cited by 12 publications

References 20 publications

Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry

Prognosis for Survival of Young Women with Breast Cancer by Quantitative p53 Immunohistochemistry

Nuclear cytometric changes in breast carcinogenesis

Recent Advances in Morphological Cell Image Analysis

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