James Selib Michaelson, Ph.D |
Cancer Health Analysis Group |
Cancer Survival ResearchA major goal of cancer medicine is to be able to predict disease outcome from information on tumor and patient characteristics available at the time of detection. We have found that for both breast cancer and melanoma, the relationship between tumor size and the chance of cancer death is well fit to a simple equation (L=1-e^-QD^Z where L is the fraction of patients dying, e is the exponential constant, D is tumor diameter or thickness, and Q and Z are parameters, whose values are specific to the cancer in question). We have also found that for patients with tumors of equivalent size, lethality increased with the number of positive nodes, such that for breast cancer there is approximately an extra ~ 6% chance of death associated with each positive lymph node, while for melanoma there is approximately an extra ~ 23% chance of death associated with each positive lymph node. The lethal contributions ascribable to tumor size and nodal status have proved to be roughly additive, and this has led to a new technique, the Size+Nodes method, for predicting cancer outcome. Ongoing research concerns the refinement of methods for predicting cancer survival, including the development of more powerful methods for measuring and incorporating the lethal impact of prognostic factors such as those detected with gene expression arrays. This has also resulted in the development of a set of linked equations, the SNP [Size+Nodes+PrognosticFactors] method, which can incorporate a variety of data on primary tumor size, nodal status, and other prognostic factors into an overall estimate of the risk of death for each patient. This general mathematical approach has also lead to equations that can predict the risk local and regional recurrence. |
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Copyright © 2007 James Michaelson, PhD
Center for Quantitative Medicine Massachusetts General Hospital Harvard Medical School Boston MA USA |