title: 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.
authors.affiliation: Laboratory Medicine, Veterans Affairs and Duke University Medical Centers, Durham, NC 27705, USA.
authors.forename: Robin T
authors.initials: RT
authors.lastname: Vollmer
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis; D001943:Breast Neoplasms / Q000473:pathology*; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology*; D008962:Models, Theoretical; D009362:Neoplasm Metastasis / Q000473:pathology*
chemical_list: D014408:Biomarkers, Tumor