Relationship between tumor grade and computed architectural complexity in breast cancer specimens.

Breast cancer is the leading form of cancer diagnosed in women, and the second leading cause of cancer mortality in this group. A commonly accepted grading system for breast cancer that has proven useful for guiding treatment strategy is the modified Bloom-Richardson system. However, this system is subject to interobserver variability, which can affect patient management and outcome. Hence, there is a need for an independent objective and reproducible breast cancer-grading tool to reduce interobserver variability. In this work, we hypothesized that architectural complexity of epithelial structures increases with decreasing differentiation in ductal carcinoma of the breast. To test this hypothesis, we explored the potential of a computer-based approach using fractal image analysis to quantitatively measure the complexity of breast histology specimens and investigate the relationship between increasing fractal dimension and tumor grade. More specifically, we developed an optimal staining and computational technique to compute the fractal dimensions of breast sections of grades 1, 2, and 3 tumors, assigned by a breast cancer pathologist, and compared the mean fractal dimensions between the tumor grades. We found that significant differences (P < .0005) exist between the mean fractal dimensions corresponding to the 3 tumor grades, and that the mean fractal dimension increases with increasing tumor grade. These results indicate that breast tumor differentiation can be characterized by the degree of architectural complexity of epithelial structures. They also indicate that fractal dimension has potential as an objective, reproducible, and automated measure of architectural complexity that may help reduce interobserver variability in grading.