肺癌患者淋巴结转移与免疫组化及其病理学改变的相关性分析

Correlation between Immunohistochemistry and Pathology for Lung Cancer Lymphatic Metastasis

目的分析和筛选肺癌淋巴结转移患者的免疫组化和病理学相关风险因素,建立相关数学模型并进行初步评价。方法回顾性收集解放军空军总医院2009～2011年度肺癌患者的相关信息,采用单因素和多因素的非条件logistic回归方法对27个淋巴结转移相关风险因素进行分析,筛选出具有统计学意义的影响因素,并建立相应的数学模型。采用ROC分析方法对所建立的模型进行评价。结果共纳入216例患者。对27个相关因素的单因素分析结果显示,病理学分级(P=0.00)、年龄(P=0.00)、肿瘤类型(P=0.01)、nm23(P=0.00)、GSTII(P=0.01)、TTF1(P=0.01)、MRP(P=0.01)、CK14(P=0.02)、CD56(P=0.02)和EGFR(P=0.03)共10个因素具有统计学意义。将该10个因素引入多因素非条件logistic回归分析,筛选出病理学分级(OR=2.34)、年龄(OR=1.02)、nm23(OR=1.66)和EGFR(OR=1.47)4个相关因素。同时根据所筛选出的因素建立数学诊断模型,采用ROC方法进行评价,结果显示所建立的模型能提高诊断的敏感性和特异性。结论病理学分级、年龄、nm23和EGFR 4个因素为肺癌淋巴结转移相关因素,且均为高风险因素。其对辅助诊断肺癌淋巴结转移具有较高的价值。

Objective To analyze and screen the risk factors of both immunohistochemistry and pathology for lung cancer lymphatic metastasis, and to build a mathematical model for preliminary evaluation. Methods By conducting retrospective studies, the information of lung cancer patients in the General Hospital of Air Force from 2009 to 2011 were collected. Both single and multiple unconditional logistic regression analyses were applied to screen total 27 possible fac- tors for lymphatic metastasis. After the factors with statistical significance were selected, the relevant mathematical model was built and then evaluated by means of receiver operating characteristic （ROC） analysis. Results A total of 216 pa- tients were included. The single analyses on 27 possible factors showed significant differences in the following 10 factors： pathological grade （P=0.00）, age （P=0.00）, tumor types （P=0.01）, nm23 （P=0.00）, GSTII （P=0.01）, TTF1 （P=0.01）, MRP （P=0.01）, CK14 （P=0.02）, CD56 （P--0.02）, and EGFR （P=0.03）. The multiple factors unconditional logistic regression anal- yses on those 10 risk factors screened 4 relevant factors as follows： pathological grade （OR=2.34）, age （OR=1.02）, nm23 （OR= 1.66）, and EGFR （OR= 1.47）. Then a mathematical diagnostic model was established based on those 4 identified risk factors, and the result of ROC analysis showed it could improve the diagnostic sensitivity and specificity compared with the single factor mathematical diagnostic model. Conclusion Pathological grade, age, nm23, and EGFR are related with lung cancer lymphatic metastasis, and all of them are the risk factors which have higher adjuvant diagnostic value for lung cancer lymphatic metastasis.