Department of Histology and Embryology, Bioscience Center, Federal University of Pernambuco, Av. Prof. Moraes Rego, S/N, Cidade Universitária, Recife, Pernambuco, 760-420, Brazil.
Department of Veterinary Medicine, Federal Rural University of Pernambuco, Recife, Pernambuco, Brazil.
Histochem Cell Biol. 2024 Nov 6;163(1):1. doi: 10.1007/s00418-024-02336-3.
Lung cancer is the leading cause of cancer-related death. The use of computational methods to quantify changes that are not perceptible to the human eye is increasing in digital pathology imaging and has quickly improved detection rates at a low cost. Therefore, the present study aims to use complex computational shape markers as tools for automated analysis of the spatial distribution of cells in microscopy images of squamous cell lung carcinoma (SqCC). Photomicrographs from pathology glass slides in the LC25000 dataset were used in this study. Compared with those of the control, the fractal dimension (28%) and lacunarity (41%) of the cell nuclei changed in SqCC. The multifractal analysis revealed a significant difference in parameters Dq, α, and f(α) for all values of q (-10 to + 10), with a greater increase for more positive q values. The values at q + 10 increased by 34% for Dq, 36% for α, and 53% for f(α) in the SqCC images. The circularity, area, and perimeter also changed in the SqCC images. However, the parameters of aspect ratio, roundness, and solidity did not significantly differ between SqCC and benign tissue. The complex shape markers with the greatest changes in this study were the f(α) values for multifractality (53%) and lacunarity (41%). In conclusion, automated quantification of the spatial distribution of cell nuclei can be a fast, low-cost tool for evaluating the microscopic characteristics of SqCC; therefore, complex shape markers could be useful tools for software and artificial intelligence to detect lung carcinoma.
肺癌是癌症相关死亡的主要原因。在数字病理学成像中，使用计算方法来量化人眼无法察觉的变化的应用越来越多，并且已经以低成本快速提高了检测率。因此，本研究旨在使用复杂的计算形状标记作为工具，用于自动分析鳞状细胞肺癌（SqCC）显微镜图像中细胞的空间分布。本研究使用了 LC25000 数据集的病理载玻片的显微照片。与对照组相比，SqCC 中细胞核的分形维数（28%）和空隙度（41%）发生了变化。多重分形分析显示，所有 q 值（-10 到+10）的参数 Dq、α 和 f(α) 存在显著差异，正值 q 值的差异更大。在 SqCC 图像中，Dq 值增加了 34%，α 值增加了 36%，f(α) 值增加了 53%。圆形度、面积和周长也在 SqCC 图像中发生了变化。然而，SqCC 和良性组织之间的长宽比、圆度和密实度参数没有显著差异。在这项研究中变化最大的复杂形状标记是多重分形的 f(α)值（53%）和空隙度（41%）。总之，细胞核空间分布的自动定量可以成为评估 SqCC 微观特征的快速、低成本工具；因此，复杂形状标记可能是软件和人工智能检测肺癌的有用工具。
