title: A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma.
abstract: OBJECTIVE
  Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.
  
  
  RESULTS
  The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.
  
  
  CONCLUSIONS
  The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
authors.affiliation: Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. xklinqiang@hotmail.com
authors.forename: Qiang
authors.initials: Q
authors.lastname: Lin
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Peng
authors.forename: Feng
authors.initials: F
authors.lastname: Yao
authors.forename: Xu-Feng
authors.initials: XF
authors.lastname: Pan
authors.forename: Li-Wen
authors.initials: LW
authors.lastname: Xiong
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Geng
authors.forename: Jiu-Xian
authors.initials: JX
authors.lastname: Feng
authors.forename: Bao-Hui
authors.initials: BH
authors.lastname: Han
authors.forename: Guo-Liang
authors.initials: GL
authors.lastname: Bao
authors.forename: Yu
authors.initials: Y
authors.lastname: Yang
authors.forename: Xiaotian
authors.initials: X
authors.lastname: Wang
authors.forename: Li
authors.initials: L
authors.lastname: Jin
authors.forename: Wensheng
authors.initials: W
authors.lastname: Guo
authors.forename: Jiu-Cun
authors.initials: JC
authors.lastname: Wang
mesh_terms: D000230:Adenocarcinoma / Q000145:classification / Q000175:diagnosis / Q000378:metabolism*; D000328:Adult; D000368:Aged; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000145:classification / Q000175:diagnosis / Q000378:metabolism*; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*