title: Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data.
abstract: BACKGROUND
  Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. Typically, the problem can be approached by developing more discriminative diagnosis methods. In this paper, computer-aided diagnosis was used to facilitate the prediction of characteristics of solitary pulmonary nodules in CT of lungs to diagnose early-stage lung cancer.
  
  
  METHODS
  The synthetic minority over-sampling technique (SMOTE) was used to account for raw data in order to balance the original training data set. Curvelet-transformation textural features, together with 3 patient demographic characteristics, and 9 morphological features were used to establish a support vector machine (SVM) prediction model. Longitudinal data as the test data set was used to evaluate the classification performance of predicting early-stage lung cancer.
  
  
  RESULTS
  Using the SMOTE as a pre-processing procedure, the original training data was balanced with a ratio of malignant to benign cases of 1∶1. Accuracy based on cross-evaluation for the original unbalanced data and balanced data was 80% and 97%, respectively. Based on Curvelet-transformation textural features and other features, the SVM prediction model had good classification performance for early-stage lung cancer, with an area under the curve of the SVMs of 0.949 (P<0.001). Textural feature (standard deviation) showed benign cases had a higher change in the follow-up period than malignant cases.
  
  
  CONCLUSIONS
  With textural features extracted from a Curvelet transformation and other parameters, a sensitive support vector machine prediction model can increase the rate of diagnosis for early-stage lung cancer. This scheme can be used as an auxiliary tool to differentiate between benign and malignant early-stage lung cancers in CT images.
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Sun
authors.forename: Regina
authors.initials: R
authors.lastname: Zhang
authors.forename: Jingjing
authors.initials: J
authors.lastname: Wang
authors.forename: Xia
authors.initials: X
authors.lastname: Li
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Guo
mesh_terms: D003936:Diagnosis, Computer-Assisted; D055088:Early Detection of Cancer; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000175:diagnosis*; D008962:Models, Theoretical