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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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