Paone G, De Angelis G, Greco S, Fiorucci F, Bisetti A, Ameglio F
Department of Cardiovascular and Respiratory Sciences, La Sapienza University, Rome, Italy.
J Cancer Res Clin Oncol. 1996;122(8):499-503. doi: 10.1007/BF01187163.
The classification of lung cancer into small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) is essential for disease prognosis and treatment. For this purpose, we have tried to optimize the use of three tumour markers determined on pleural effusions, to differentiate SCLC from NSCLC by means of a canonic variable, generated by discriminant analysis, including subjects with histologically proven lung cancer. Discriminant analysis was performed by using carcinoembryonic antigen, neuron-specific enolase and tissue polypeptide antigen pleural levels, determined in 65 consecutive and unselected patients, histologically classified as 49 NSCLC and 16 SCLC. To validate the formula generated, a control group of 37 lung cancer patients (10 SCLC and 27 NSCLC), enrolled subsequently, was employed. Applying the discriminant analysis to SCLC and NSCLC patients a good classification was obtained (92% rate of correct classification). The aforementioned formula, applied to the validation group, showed a 92% rate of correct classification. This method, which is rapid, inexpensive and routinely applicable to malignant pleural effusions, may be reliably used to classify lung cancer patients.
将肺癌分为小细胞肺癌(SCLC)和非小细胞肺癌(NSCLC)对于疾病的预后和治疗至关重要。为此,我们试图优化对胸腔积液中测定的三种肿瘤标志物的使用,通过判别分析生成的一个典型变量来区分SCLC和NSCLC,纳入组织学确诊为肺癌的患者。对65例连续且未经选择的患者进行判别分析,这些患者组织学分类为49例NSCLC和16例SCLC,测定其癌胚抗原、神经元特异性烯醇化酶和组织多肽抗原的胸腔积液水平。为验证生成的公式,随后纳入了37例肺癌患者(10例SCLC和27例NSCLC)作为对照组。将判别分析应用于SCLC和NSCLC患者,获得了良好的分类结果(正确分类率为92%)。将上述公式应用于验证组,正确分类率为92%。这种方法快速、廉价且可常规应用于恶性胸腔积液,可可靠地用于肺癌患者的分类。