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In 2006, breast cancer is expected to continue as the leading form of cancer diagnosed in women, and the second leading
cause of cancer mortality in this group. A method that has proven useful for guiding the choice of treatment strategy is
the assessment of histological tumor grade. The grading is based upon the mitosis count, nuclear pleomorphism, and
tubular formation, and is known to be subject to inter-observer variability. Since cancer grade is one of the most
significant predictors of prognosis, errors in grading can affect patient management and outcome. Hence, there is a need
to develop a breast cancer-grading tool that is minimally operator dependent to reduce variability associated with the
current grading system, and thereby reduce uncertainty that may impact patient outcome. In this work, we explored the
potential of a computer-based approach using fractal analysis as a quantitative measure of cancer grade for breast
specimens. More specifically, we developed and optimized computational tools to compute the fractal dimension of
low- versus high-grade breast sections and found them to be significantly different, 1.3±0.10 versus 1.49±0.10,
respectively (Kolmogorov-Smirnov test, p<0.001). These results indicate that fractal dimension (a measure of
morphologic complexity) may be a useful tool for demarcating low- versus high-grade cancer specimens, and has
potential as an objective measure of breast cancer grade. Such prognostic value could provide more sensitive and
specific information that would reduce inter-observer variability by aiding the pathologist in grading cancers.
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