OPTIMIZATION OF CANCER DETECTION

DAN H. MOORE II ¹

¹ Biomedical Division, Lawrence Livermore Laboratory, University of California, Livermore, California 94550

A statistical model is developed that describes the population of women who are given a cytologic screening test for cervical cancer. The model is used to determine false positive and false negative rates as a function of (a) the proportion of "positive" cells in women free from cancer and in those with cancer, (b) the number of cells examined and (c) the minimal number of positive cells for a diagnosis of cancer. The model allows estimation of the minimal number of cells that must be examined in order to reduce both the false positive and the false negative rates below some predetermined levels. An expected cost equation is derived which combines the costs of examining each cell with the costs for false positives and false negatives. It is shown how cancer detection can be optimized through the use of this cost equation. The method determines both the maximal permissible cost for examining each cell and the optimal number of cells to examine in order to reduce the over-all expected cost below some predetermined level.

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