title: An immunohistochemical analysis-based decision tree model for estimating the risk of lymphatic metastasis in pN0 squamous cell carcinomas of the lung.
abstract: OBJECTIVE
  Lung cancer patients within the pN0 category have a significantly different outcome. The aim of this study was to develop a mathematical model to assist in predicting the prognosis of pN0 lung squamous cell carcinoma (SCC).
  
  
  RESULTS
  Twenty-three proteins were examined by immunohistochemical (IHC) analysis on primary tumour tissues from 319 lung SCC patients. In a training group, using IHC data, a recursive partitioning decision tree (RP-DT) was used to build a model for estimating the risk for lymphatic metastasis. This model was then validated in a test cohort. Of 23 proteins, 8 (matrix metallopeptidase 1, metalloproteinase inhibitor 1, Ras GTPase-activating-like protein IQGAP1, targeting protein for Xklp2, urokinase-type plasminogen activator, cathepsin D, fascin, polymeric immunoglobulin receptor/secretory component) were selected, and generated a tree model in a training group of 255 patients to classify them as at high or low risk of lymphatic invasion, with accuracy of 78.0% (compared to histopathological diagnosis), sensitivity of 83.0% and specificity of 70.3%. When the tree model was applied to the test group, the accuracy, sensitivity and specificity were 76.6%, 76.0% and 76.9%, respectively. The performance of this mathematical model was substantiated further in 34 'problematic' stage I/pN0 patients by survival analysis.
  
  
  CONCLUSIONS
  The RP-DT model, constructed with eight protein markers for estimating lymphatic metastasis risk in pN0 lung SCC, is clinically feasible and practical, using IHC data from the primary tumour.
authors.affiliation: State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Cancer Institute (Hospital), Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Liu
authors.forename: Dongmei
authors.initials: D
authors.lastname: Lin
authors.forename: Ting
authors.initials: T
authors.lastname: Xiao
authors.forename: Ying
authors.initials: Y
authors.lastname: Ma
authors.forename: Zhi
authors.initials: Z
authors.lastname: Hu
authors.forename: Hongwei
authors.initials: H
authors.lastname: Zheng
authors.forename: Shan
authors.initials: S
authors.lastname: Zheng
authors.forename: Yan
authors.initials: Y
authors.lastname: Liu
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Lin
authors.initials: L
authors.lastname: Li
authors.forename: Yan
authors.initials: Y
authors.lastname: Cao
authors.forename: Suping
authors.initials: S
authors.lastname: Guo
authors.forename: Naijun
authors.initials: N
authors.lastname: Han
authors.forename: Xuebing
authors.initials: X
authors.lastname: Di
authors.forename: Kaitai
authors.initials: K
authors.lastname: Zhang
authors.forename: Shujun
authors.initials: S
authors.lastname: Cheng
authors.forename: Yanning
authors.initials: Y
authors.lastname: Gao
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D002294:Carcinoma, Squamous Cell / Q000401:mortality / Q000473:pathology*; D003663:Decision Trees; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology*; D008207:Lymphatic Metastasis; D009367:Neoplasm Staging / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D046888:Tissue Array Analysis
chemical_list: D014408:Biomarkers, Tumor