DUCTAL CARCINOMA IN-SITU OF THE BREAST - HISTOLOGIC CATEGORIZATION AND ITS RELATIONSHIP TO PLOIDY AND IMMUNOHISTOCHEMICAL EXPRESSION OF HORMONE RECEPTORS, P53, AND C-ERBB-2 PROTEIN

Background. Ductal carcinoma in situ (DCIS) of the breast has been diagnosed increasingly since the advent of mammography. However, the natural history of these lesions remains uncertain. Ductal carcinoma in situ of the breast does not represent a single entity but a heterogeneous group with histologic and clinical differences. The histologic subtype of DCIS seems to have an influence on its biologic behavior, but there are few studies correlating subtype with biologic markers.Methods. The authors studied a consecutive series of 40 cases of DCIS and after its histologic categorization verified its relationship with ploidy using image analysis and analyzing estrogen receptor (ER), progesterone receptor (PR), p53 and c-erbB-2 expression using immunohistochemistry.Results. The three groups proposed according to the grade of malignancy were correlated significantly with some of the additional parameters studied, including aneuploidy and c-erB-2 expression. Aneuploidy was detected in 77.5% of cases of DCIS mainly in high and intermediate grade subtypes (100% and 80% vs. 35.7% in low grade) whereas immunoreactivity for c-erbB-2 was detected in 45% of cases of DCIS mainly in the high grade group. Expression of ER and PR were observed frequently in this study (63.9% and 65.7% respectively), but without correlation with the histologic subtype of DCIS, although we found a somewhat significant association between high grade DCIS and lack of ER. p53 protein expression was detected in 36.8% of these cases, but no relationship between this expression and histologic subtype or grading of DCIS was found.Conclusions. These results provide further evidence for the morphologic and biologic heterogeneity of DCIS. Besides histologic classification and nuclear grading, some biologic markers such as aneuploidy and c-erbB-2 expression constitute additional criteria of high grade of malignancy.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.