Predicting Recurrence Using the Clinical Factors of Patients with Non-small Cell Lung Cancer After Curative Resection.
10.3346/jkms.2009.24.5.824
- Author:
Hyun Joo LEE
1
;
Jisuk JO
;
Dae Soon SON
;
Jinseon LEE
;
Yong Soo CHOI
;
Kwhanmien KIM
;
Young Mog SHIM
;
Jhingook KIM
Author Information
1. Department of Thoracic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
- Publication Type:Original Article ; Randomized Controlled Trial
- Keywords:
Carcinoma, Non-Small-Cell Lung;
Prognosis;
Recurrence
- MeSH:
Adenocarcinoma/mortality/pathology/surgery;
Adolescent;
Adult;
Aged;
Aged, 80 and over;
Carcinoma, Non-Small-Cell Lung/mortality/pathology/*surgery;
Carcinoma, Squamous Cell/mortality/pathology/surgery;
Disease-Free Survival;
Female;
Humans;
Lung Neoplasms/mortality/pathology/*surgery;
Male;
Middle Aged;
Neoplasm Recurrence, Local/*diagnosis;
Neoplasm Staging;
Predictive Value of Tests;
Prognosis;
Regression Analysis;
Risk Factors;
Survival Rate
- From:Journal of Korean Medical Science
2009;24(5):824-830
- CountryRepublic of Korea
- Language:English
-
Abstract:
We present a recurrence prediction model using multiple clinical parameters in patients surgically treated for non-small cell lung cancer. Among 1,578 lung cancer patients who underwent complete resection, we compared the early-recurrence group with the 3-yr non-recurrence group for evaluating those factors that influence early recurrence within one year after surgery. Adenocarcinoma and squamous cell carcinoma were analyzed independently. We used multiple logistic regression analysis to identify the independent clinical predictors of recurrence and Cox's proportional hazard regression method to develop a clinical prediction model. We randomly divided our patients into the training and test subsets. The pathologic stages, tumor cell type, differentiation of tumor, neoadjuvant therapy and age were significant factors on the multivariable analysis. We constructed the model for the training set with adenocarcinoma (n=236) and squamous cell carcinoma (n=305), and we applied it to the test set with adenocarcinoma (n=110) and squamous cell carcinoma (n=154). It was predictive for the in adenocarcinoma (P<0.001) and the squamous cell carcinoma (P=0.037), respectively. Our results showed that our recurrence prediction model based on the clinical parameters could significantly predict the individual patients who were at high risk or low risk for recurrence.
