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Tissues are fractal due to its self-similar structure, and the fractaldimension change with the abnormalities such as in disease like cancer. Theoptical imaging of thin slices of tissue using transmission microscopy canproduce an intensity distribution pattern proportional to its refractive indexwhich represents the mass density distribution pattern of the tissues. Thefractal dimension of tissue is calculated by analyzing this mass densitydistribution and is known to increase with the progression of carcinogenesis.This paper explores the viability of using this quantitative approach offractal dimension analysis to create a standardized accurate cancer diagnosistest and staging which reduces the issues plaguing current testing methods. Acommonality for most of the deadliest cancers is their lethality due to thedifficulty in properly diagnosing them in the early stages or accurate latestaging. This difficulty can arise from the physical location of the organcausing them to be hard to access and the lack of noticeable symptoms until thelate stages when there is almost no hope of treatment. To study some of thesedeadly cancers, commercially available paraffin embedded tissue microarray(TMA) samples containing multiple cores of different cases and stages ofPancreatic, Breast, Colon, and Prostate cancer are analyzed. The fractaldimension of different TMA samples is able to correctly differentiate betweenthe different stages of each cancer, raising the possibility of a standardizedsystem being created to increase diagnosis accuracy in the future.

Detection of cancer stages through fractal dimension analysis of tissue microarrays (TMA) via optical transmission microscopy