Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.
Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.
The striatum (neostriatum) is one of the principal constituents of the basal nuclei. It is a complex structure which consists of a dorsal and the ventral components. According to the spine distribution and their density, neurons of the human striatum can be classified into two main types: spiny and aspiny cells. Further classification recognizes two groups of spiny, and three groups of aspiny neurons. The goal of this study was to analyze different morphometric properties of the digital images of the group IV and group V aspiny neurons, from the dorsal striatum of both cerebral hemispheres. In this study, a total of 175 two-dimensional images of aspiny neurons were analyzed. Image reconstruction and measurement was performed with the specialized, public software Image J. Four parameters of standard fractal analysis were quantified from these binary images. In addition, five textural parameters were obtained by analyzing grayscale images of the entire neuron. Results of both analyses show that six of nine parameters differed between the group IV and group V aspiny neurons. Moreover, in both groups of neurons, one parameter of the fractal and three of the texture analyses differed between the putamen and the caudate nucleus neurons. Thus, this study corroborates previous classification of aspiny neurons. Although they belong to the same aspiny group, different type of cells can qualify nerve signals in their own way. Therefore, this study supports the hypothesis that neuronal morphology differences can reflect their functional diversity and their role in communication.
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