Abstract
Lung cancer is the leading cause of cancer deaths. The use of computational methods to quantify changes not perceptible to the human eye is growing in digital pathology imaging, improving detection rates quickly and at low cost. Therefore, the present study aims to use computational complex shape markers as tools for automated analysis of the spatial distribution of cells in microscopic images of squamous cell lung carcinoma (SqCC). Photomicrographs from pathology glass slides in the database LC25000 were used. The fractal dimension and lacunarity of the lung cell nuclei statistically changed in SqCC compared to the control. The multifractal analysis showed a significant difference in Dq, α, and f(α) for all values of q (-10 to + 10), with a greater increase for more positive q values. The number of cells, circularity, area, and perimeter also changed in SqCC images. However, the parameters aspect ratio, roundness, and solidity did not show statistical differences between the SqCC and benign tissue. The complex shape markers with the greatest changes in this study were the f(α) value in multifractality (53%) and lacunarity (41%). In conclusion, the automated quantification of the spatial distribution of cell nuclei can be a fast, low-cost tool for evaluating the microscopic characteristics of SqCC; therefore, the complex shape markers could be useful methods for software and artificial intelligence to detect lung carcinoma.