Pathologists examine histology sections to make diagnostic and prognostic assessments regarding cancer based on
deviations in cellular and/or glandular structures. However, these assessments are subjective and exhibit some degree of
observer variability. Recent studies have shown that fractal dimension (a quantitative measure of structural complexity)
has proven useful for characterizing structural deviations and exhibits great potential for automated cancer diagnosis and
prognosis. Computing fractal dimension relies on accurate image segmentation to capture the architectural complexity
of the histology specimen. For this purpose, previous studies have used techniques such as intensity histogram analysis
and edge detection algorithms. However, care must be taken when segmenting pathologically relevant structures since
improper edge detection can result in an inaccurate estimation of fractal dimension. In this study, we established a
reliable method for segmenting edges from grayscale images. We used a Koch snowflake, an object of known fractal
dimension, to investigate the accuracy of various edge detection algorithms and selected the most appropriate algorithm
to extract the outline structures. Next, we created validation objects ranging in fractal dimension from 1.3 to 1.9
imitating the size, structural complexity, and spatial pixel intensity distribution of stained histology section images. We
applied increasing intensity thresholds to the validation objects to extract the outline structures and observe the effects on
the corresponding segmentation and fractal dimension. The intensity threshold yielding the maximum fractal dimension
provided the most accurate fractal dimension and segmentation, indicating that this quantitative method could be used in
an automated classification system for histology specimens.
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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