Vollmer Robin T
Laboratory Medicine, Veterans Affairs and Duke University Medical Centers, Durham, NC 27705, USA.
Am J Clin Pathol. 2009 Sep;132(3):391-5. doi: 10.1309/AJCPDIP12IUGVRQR.
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.
在本研究中，使用了一个数学和概率模型来研究肺部肿瘤是原发性还是乳腺癌转移瘤的概率。该模型利用了甲状腺转录因子（TTF）-1、乳腺珠蛋白、p63和雌激素受体的免疫组化染色信息以及关于女性原发性肺癌和转移性乳腺癌的流行病学数据。结果表明，这4种染色在大约80%属于该鉴别诊断范围的肿瘤中可得出几乎确定的诊断。然而，对于该范围内TTF-1、乳腺珠蛋白和p63呈阴性的19%的肿瘤，诊断仍存在不确定性。
