da Silva Lucas Glaucio, da Silva Monteiro Waleska Rayanne Sizinia, de Aguiar Moreira Tiago Medeiros, Rabelo Maria Aparecida Esteves, de Assis Emílio Augusto Campos Pereira, de Souza Gustavo Torres
Faculty of Medical and Health Sciences of Juiz de Fora, Alameda Salvaterra, Juiz de Fora, Minas Gerais, 200 - 36033-003, Brazil.
Department of Biology - Genetics - Federal University of Juiz de Fora, Rua José Lourenço Kelmer, s/n, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
Appl Microsc. 2021 Apr 30;51(1):6. doi: 10.1186/s42649-021-00055-w.
Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promissing statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.
组织病理学是用于大多数恶性肿瘤（包括乳腺癌）的成熟标准诊断方法。然而，尽管有培训和标准化，但它仍被认为依赖操作人员，错误仍然是一个问题。分形维数分析是一种计算图像处理技术，可用于评估模式的复杂程度。我们的目的是提供一种强大且易于实现的方法，将计算机辅助技术引入组织病理学实验室。使用了来自两个数据库的切片：A）乳腺癌组织病理学数据库；B）乳腺癌组织学大挑战数据库。A组包含来自24例良性病变患者的2480张图像，以及来自58例乳腺癌患者的5429张图像。B组每种类型包含100张图像：正常组织、良性病变、原位癌和浸润性癌。所有图像均在ImageJ计算环境中使用FracLac算法进行分析，以得出盒计数分形维数（Db）结果。A组40倍放大的图像在统计学上有差异（p = 0.0003），而400倍放大的图像在均值上没有差异。在B组中，比较正常和/或良性图像与原位和/或浸润性癌时，平均Db值呈现出有前景的统计学差异（所有p < 0.0001）。有趣的是，比较正常组织与良性病变时没有差异。这些数据与之前分形分析能够区分恶性肿瘤的工作相佐证。计算机辅助诊断算法可能会受益于使用Db数据；特定的Db临界值在诊断乳腺癌时可能产生约99%的特异性。此外，由于它能够评估组织复杂性，该工具可用于了解癌症组织学改变的进展。
