Development and cross-validation of prognostic models to assess the treatment effect of cisplatin/pemetrexed chemotherapy in lung adenocarcinoma patients

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• 作者：Wenjun Mou (1) (2)
  Zhaoqi Liu (3)
  Yuan Luo (1)
  Meng Zou (3)
  Chao Ren (4)
  Chunyan Zhang (1) (2)
  Xinyu Wen (1)
  Yong Wang (3)
  Yaping Tian (1)
  
• 关键词：Prognostic models ; Five ; fold cross ; validation ; The stepwise forward selection ; Lung adenocarcinoma ; Chemotherapy
• 刊名：Medical Oncology
• 出版年：2014
• 出版时间：September 2014
• 年：2014
• 卷：31
• 期：9
• 全文大小：918 KB
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• 作者单位：Wenjun Mou (1) (2)
  Zhaoqi Liu (3)
  Yuan Luo (1)
  Meng Zou (3)
  Chao Ren (4)
  Chunyan Zhang (1) (2)
  Xinyu Wen (1)
  Yong Wang (3)
  Yaping Tian (1)
  
  1. Department of Clinical Biochemistry, Chinese PLA General Hospital, Beijing, 100853, China
  2. School of Medicine, Nankai University, Tianjin, 300071, China
  3. National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China
  4. Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
  
• ISSN：1559-131X

文摘

Better understanding of the treatment effect of cisplatin/pemetrexed chemotherapy on lung adenocarcinoma patients is needed to facilitate chemotherapy planning and patient care. In this retrospective study, we will develop prognostic models by the cross-validation method using clinical and serum factors to predict outcomes of cisplatin/pemetrexed chemotherapy in lung adenocarcinoma patients. Lung adenocarcinoma patients admitted between 2008 and 2013 were enrolled. 29 serum parameters of laboratory tests and 14 clinical factors were analyzed to develop the prognostic models. First, the stepwise selection and five-fold cross-validation were performed to identify candidate prognostic factors. Then a classification of all patients based on the number of metastatic sites resulted in four distinct subsets. In each subset, a prognostic model was fitted with the most accurate prognostic factors from the candidate prognostic factors. Categorical survival prediction was estimated using a log-rank test and visualized with Kaplan–Meier method. 227 lung adenocarcinoma patients were enrolled. Twenty candidate prognostic factors evaluated using the five-fold cross-validation method were total protein, total bilirubin, direct bilirubin, creatine kinase, age, smoking index, neuron-specific enolase, bone metastasis, total triglyceride, albumin, gender, uric acid, CYFRA21-1, lymph node metastasis, liver metastasis, lactate dehydrogenase, CA153, peritoneal metastasis, CA125, and CA199. From these 20 candidate prognostic factors, the multivariate Cox proportional hazard model with the highest prognostic accuracy in each subset was identified by the stepwise forward selection method, which generated significant prognostic stratifications in Kaplan–Meier survival analyses (all log-rank p?