Goals

1. Develop quantitative algorithms for measuring subtle regions of order and disorder in tumors. These algorithms will allow pathologists and scientists to find sub-groups of cancers that may benefit from more specific types of therapy, which reduces the need for unnecessary treatment and side effects.

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2. Develop a unifying equation for characterizing tumor histopathology and behavior via image analysis.

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The TSGL seeks to establish the foundational knowledge-based algorithms for measuring order and disorder in tissues and tumors. 

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Mission

Cells within a tumor are arranged in various patterns that seem random, except for large-scale morphological features that pathologists can clearly see and subjectively score. These obvious patterns of cell shape and nuclear shape are the reason why a pathologist can say that this tumor is a "Grade 1," while that tumor is a "Grade 3" and more aggressive. Likewise, these large-scale patterns allow pathologists to categorize one tumor as an "adenoid cystic carcinoma," while a different tumor as "spindle cell carcinoma." Beyond these large-scale morphological features, the human eye is unable to measure subtle regions of order and disorder within a tumor. In biology, this problem is referred to as "heterogeneity": in other words, what we see is too complicated for us to pick out any patterns, so we assume that it's random. The answer to the problem of heterogeneity is to turn features into numbers: quantitation and statistics!

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