Primary lung cancer vs metastatic breast cancer: a probabilistic approach.

Abstract

In this study, a mathematical and probabilistic model is used to study the probability that a lung tumor is a primary vs a metastasis from cancer of the breast. The model uses information from immunohistochemical stains for thyroid transcription factor (TTF)-1, mammaglobin, p63, and estrogen receptor and epidemiologic data about primary lung and metastatic breast cancers in women. The results demonstrate that these 4 stains can yield nearly certain diagnoses in approximately 80% of tumors falling into the pool of this differential diagnosis. Nevertheless, uncertainty of diagnosis remains for the 19% of tumors in the pool that are negative for TTF-1, mammaglobin, and p63.