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1.  Automated classification of immunostaining patterns in breast tissue from the human protein atlas 
Journal of Pathology Informatics  2013;4(Suppl):S14.
The Human Protein Atlas (HPA) is an effort to map the location of all human proteins ( It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.
Materials and Methods:
The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.
We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.
Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.
PMCID: PMC3678740  PMID: 23766936
Color deconvolution; dual tree complex wavelets; histology; human protein atlas; support vector machine; textural features; weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) features
2.  Multicellular Tumour Spheroid as a model for evaluation of [18F]FDG as biomarker for breast cancer treatment monitoring 
In order to explore a pre-clinical method to evaluate if [18F]FDG is valid for monitoring early response, we investigated the uptake of FDG in Multicellular tumour spheroids (MTS) without and with treatment with five routinely used chemotherapy agents in breast cancer.
The response to each anticancer treatment was evaluated by measurement of the [18F]FDG uptake and viable volume of the MTSs after 2 and 3 days of treatment.
The effect of Paclitaxel and Docetaxel on [18F]FDG uptake per viable volume was more evident in BT474 (up to 55% decrease) than in MCF-7 (up to 25% decrease).
Doxorubicin reduced the [18F]FDG uptake per viable volume more noticeable in MCF-7 (25%) than in BT474 MTSs.
Tamoxifen reduced the [18F]FDG uptake per viable volume only in MCF-7 at the highest dose of 1 μM.
No effect of Imatinib was observed.
MTS was shown to be appropriate to investigate the potential of FDG-PET for early breast cancer treatment monitoring; the treatment effect can be observed before any tumour size changes occur.
The combination of PET radiotracers and image analysis in MTS provides a good model to evaluate the relationship between tumour volume and the uptake of metabolic tracer before and after chemotherapy. This feature could be used for screening and selecting PET-tracers for early assessment of treatment response.
In addition, this new method gives a possibility to assess quickly, and in vitro, a good preclinical profile of existing and newly developed anti-cancer drugs.
PMCID: PMC1459213  PMID: 16556298

Results 1-2 (2)