| Περίληψη: | Breast cancer is among the well-researched type compared to other common types of cancer. However, there still remain important open issues for investigation. One of these issues is the clarification of the importance of certain biological factors, such as histological tumour grade and estrogens reception (ER) status, to clinical management of the disease. Until now, histological grading and ER status assessment is based on the visual evaluation of breast tissue specimens under the microscope. More specifically, grading is determined on the visual estimation of certain histological features, on H&E (Hematoxylin & Eosin) stained specimens according to the World Health Organization (WHO) guidelines, whereas ER-status is assessed as the percentage of expressed nuclei on immunohistochemically stained (IHC) specimens as suggested by the American Society of Clinical Oncology (ASCO) protocol.
Recent studies have attempted to examine whether histological tumour grade relates to ER status. Such a relation seems to be of importance in the various treatment strategies followed in breast tumours. However, the quantification of ER status presents certain weaknesses: a) there is a lack of consensus among experts regarding the protocol to be followed for calculating the ER status; b) an exact estimate of the ER status is difficult to be obtained, since the latter would require manual counting of positively expressed nuclei. In clinical practice often a gross estimate is obtained by the histopathologists through visual inspection on representative specimen areas. Consequently, the evaluation of ER status, which has been considered by previous studies as the key measure for assessing the correlation between ERs and tumour grade, is prone to the physician’s subjective estimation. Therefore, more reliable methods are needed. This thesis has been carried out in the search of such alternative, more reliable, methods.
Accordingly, the aims of the present thesis are: (i) to develop a reliable segmentation methodology for detection of ER-expressed nuclei in breast cancer tissue images stained with IHC, (ii) to objectively quantify ER status in breast cancer tissue images stained with IHC, (iii) to investigate potential correlation between ER status and histological grade by combining information from IHC and H&E stained breast cancer tissue images obtained from the same patient, (iv) to establish evidence for linking chromatin texture variations with textural variations on ER-expressed nuclei, (v) to investigate the potential of the proposed hybrid supervised pattern recognition strategies to other challenging fields of medical image processing and analysis.
To address the above issues and in search of reliable methods for quantitatively assessing ER status and its correlation with histological grade based, a novel hybrid (unsupervised-supervised) pattern recognition methodology has been designed, developed and implemented for the analysis of breast cancer tissue images.
Moreover, it will be shown that proper modification of the proposed methodology may result to generalize pixel classification approach suitable for processing and analysis of medical images other than microscopic such as Computed Tomography Angiography images.
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