Browsing by Author "Rajkovic, Nemanja (55844172600)"

To reveal the best choice of algorithm for parvalbumin-immunostained images of the hippocampal gyrus dentatus in two distinct rat models of Parkinson's disease (PD), particularly in terms of extracting the crucial information from the image, we tested whether the impact of experimentally induced dopaminergic (hemiparkinsonism) vs. cholinergic (PD cholinopathy) innervation impairment on the parvalbumin stained GABA interneurons could be detected using two separate algorithms, the fractal box-count and the gray-level co-occurrence matrix analysis (GLCM) algorithms. For the texture and fractal analysis of the hippocampal gyrus dentatus images, we used.tif images from three experimental groups of adult male Wistar rats: control rats, rats with Parkinson disease (PD) cholinergic neuropathology (with a PPT lesion), and hemiparkinsonian rats (with a SNpc lesion). For the suprapyramidal layer of the gyrus dentatus ASM and Entropy differentiated the images of the SNpc lesion versus the images of the control and the PPT lesion subjects, with significantly higher ASM and lower Entropy, indicating the homogenization of the images and their lower gray-level complexity. The infrapyramidal images of the SNpc group were differentiated versus the images from the control and PPT groups in terms of all the GLCM parameters: they showed lower mean Entropy and Contrast and higher ASM, Correlation and IDM. These results strongly suggest a rise in the uniformity, homogeneity and orderliness in the gray-levels of images from the SNpc group. Our results indicate that GLCM analysis is a more sensitive tool than fractal analysis for the detection of increased dendritic arborization in histological images. © 2019 Elsevier Ltd

To reveal the best choice of algorithm for parvalbumin-immunostained images of the hippocampal gyrus dentatus in two distinct rat models of Parkinson's disease (PD), particularly in terms of extracting the crucial information from the image, we tested whether the impact of experimentally induced dopaminergic (hemiparkinsonism) vs. cholinergic (PD cholinopathy) innervation impairment on the parvalbumin stained GABA interneurons could be detected using two separate algorithms, the fractal box-count and the gray-level co-occurrence matrix analysis (GLCM) algorithms. For the texture and fractal analysis of the hippocampal gyrus dentatus images, we used.tif images from three experimental groups of adult male Wistar rats: control rats, rats with Parkinson disease (PD) cholinergic neuropathology (with a PPT lesion), and hemiparkinsonian rats (with a SNpc lesion). For the suprapyramidal layer of the gyrus dentatus ASM and Entropy differentiated the images of the SNpc lesion versus the images of the control and the PPT lesion subjects, with significantly higher ASM and lower Entropy, indicating the homogenization of the images and their lower gray-level complexity. The infrapyramidal images of the SNpc group were differentiated versus the images from the control and PPT groups in terms of all the GLCM parameters: they showed lower mean Entropy and Contrast and higher ASM, Correlation and IDM. These results strongly suggest a rise in the uniformity, homogeneity and orderliness in the gray-levels of images from the SNpc group. Our results indicate that GLCM analysis is a more sensitive tool than fractal analysis for the detection of increased dendritic arborization in histological images. © 2019 Elsevier Ltd

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors. © 2018 Rajkovic, Li, Plataniotis, Kanjer, Radulovic and Miloševic.

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors. © 2018 Rajkovic, Li, Plataniotis, Kanjer, Radulovic and Miloševic.