Paone G, De Angelis G, Munno R, Pallotta G, Bigioni D, Saltini C, Bisetti A, Ameglio F
Dept of Cardiovascular and Respiratory Sciences, University La Sapienza, Rome, Italy.
Eur Respir J. 1995 Jul;8(7):1136-40. doi: 10.1183/09031936.95.08071136.
A correct diagnosis of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) is essential both for prognostic and therapeutic reasons. We used discriminant analysis as a method to optimize the discriminant power of serum tumour marker levels for differentiation between SCLC and NSCLC. A panel of serum markers, including neurone specific enolase (NSE), cytokeratin fragment antigen 21.1 (CYFRA-21.1), tissue polypeptide antigen (TPA) and carcinoembryonic antigen (CEA) was obtained in 50 consecutive NSCLC and 17 SCLC. Data were analysed by the BMDP statistical program after logarithmic transformation of marker levels. The variables selected were NSE and CYFRA-21.1. Considered together, they were able to give a 97% rate of correct classification. The formula generated (canonic variable, CV) was validated on a group of seven SCLC and 22 NSCLC patients. Only two errors occurred. We therefore conclude that the canonic variable tested, based on NSE and CYFRA-21.1, provides a good discrimination between the two types of lung cancer. The method is rapid, relatively inexpensive, and based on simple serum tests.
出于预后和治疗的原因，准确诊断小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）至关重要。我们使用判别分析作为一种方法，以优化血清肿瘤标志物水平对SCLC和NSCLC进行鉴别的判别能力。我们收集了50例连续的NSCLC患者和17例SCLC患者的一组血清标志物，包括神经元特异性烯醇化酶（NSE）、细胞角蛋白片段抗原21.1（CYFRA - 21.1）、组织多肽抗原（TPA）和癌胚抗原（CEA）。在对标志物水平进行对数转换后，通过BMDP统计程序对数据进行分析。所选择的变量为NSE和CYFRA - 21.1。综合考虑，它们能够给出97%的正确分类率。所生成的公式（典型变量，CV）在一组7例SCLC患者和22例NSCLC患者中得到验证。仅出现了两例错误。因此，我们得出结论，基于NSE和CYFRA - 21.1所测试的典型变量能够很好地区分这两种类型的肺癌。该方法快速、相对便宜，并且基于简单的血清检测。
