MACROSCOPE
An Engineering Approach to Translational Medicine
Physician-scientists may benefit from an approach that emphasizes solving problems over generating hypotheses
Michael Liebman
Tumor Classification and Staging
Tumor classification is critical to the assessment and treatment of
cancer. To optimize this process of classification, the physician
must determine both the present disease state and its potential for
progression. This is a difficult task, and it will become more
difficult as more relations are established between genes,
environment and disease; an ideal representation of cancer would
reflect all of these variables. With this idealized tool, a person's
disease would become a vector in multi-dimensional space, with each
of tens or hundreds of axes representing a clinical or molecular
parameter. Perhaps we will realize this vision.
In the meantime, oncologists use three concrete variables to define
the stage of a tumor—tumor size (T), metastasis (M) and nodal
involvement (N), the finding of cancer in nearby lymph nodes. One
problem with this system is that the mapping of some TMN triples to
fixed stages is ambiguous, perhaps because the terms are imprecise
or insufficient to describe the disease. Another flaw is that these
numbers do not reflect the history of a patient's disease and
treatment. Yet the TMN system could be made into a better assessment
tool simply by setting each variable on its own axis to create a
three-dimensional TMN space. Each person's clinical trajectory can
be viewed as a unique vector in TMN space. This method highlights
the fact that although the stages of tumor progression are linear,
there are different "paths" through the disease; not all
stages may be encountered on each patient's path. Furthermore, as we
see how different vectors turn toward the origin (cancer-free) vs.
the extremity of poor outcome or reoccurrence (10,10,10 in a TMN
space where the axes run from zero to 10), we can identify paths
through TMN space that represent different responses to a given
treatment. The result is better information for clinicians to make
the best decisions for each patient.
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