Prediction of microscopic spread of tumor cells is becoming critically important in the decision making process in planning radiation therapy for cancer. Until recently, radiation treatment of head and neck cancer has been conservative, treating large regions to insure eradication of disease. However, if it is known that regional spread is confined, a more focused treatment can be considered, with the payoff of reducing or eliminating morbidity due to irradiating healthy tissue in the vicinity of node groups. Knowledge about the occurrence of micrometastases comes mainly from pathology reports in connection with surgery. As the data accrue, it will be possible and necessary to represent this knowledge in a symbolic computational model. Our work reports on the feasibility of modeling this knowledge using published data.
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