title: Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.
abstract: OBJECTIVE
  To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer.
  
  
  METHODS
  A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.
  
  
  RESULTS
  Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.
  
  
  CONCLUSIONS
  Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Sun
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Ni
authors.initials: N
authors.lastname: Gao
authors.affiliation: School of Medical Sciences, Edith Cowan University, Perth, Australia; School of Exercise and Health Sciences, Edith Cowan University, Perth, Australia.
authors.forename: Desmond Dev
authors.initials: DD
authors.lastname: Menon
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Yanxia
authors.initials: Y
authors.lastname: Luo
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Gao
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Department of Epidemiology & Public Health, University College Cork, Cork, Ireland.
authors.forename: Xia
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China; School of Medical Sciences, Edith Cowan University, Perth, Australia.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Huiping
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
authors.forename: Pingxin
authors.initials: P
authors.lastname: Lv
authors.affiliation: Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Liang
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Lixin
authors.initials: L
authors.lastname: Tao
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Xiangtong
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Guo
mesh_terms: D005260:Female; D006801:Humans; D008297:Male; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*