Three-dimensional texture feature analysis of pulmonary nodules in CT images: Lung cancer predictive models based on support vector machine classifier Academic Article uri icon

abstract

  • To extract texture features of pulmonary nodules from three-dimensional views and to assess if predictive models of lung CT images from a three-dimensional texture feature could improve assessments conducted by radiologists. Clinical and CT imaging data for three dimensions (axial, coronal, and sagittal) in pulmonary nodules in 285 patients were collected from multiple centers and the Cancer Imaging Archive after ethics committee approval. Three-dimensional texture feature values (contourlets), and clinical and computed tomography (CT) imaging data were built into support vector machine (SVM) models to predict lung cancer, using four evaluation methods (disjunctive, conjunctive, voting, and synthetic); sensitivity, specificity, the Youden index, discriminant power (DP), and F value were calculated to assess model effectiveness. Additionally, diagnostic accuracy (three-dimensional model, axial model, and radiologist assessment) was assessed using the area under the curves for receiver operating characteristic (ROC) curves. Cross-sectional data from 285 patients (median age, 62 [range, 45-83] years; 115 males [40.4%]) were evaluated. Integrating three-dimensional assessments, the voting method had relatively high effectiveness based on both sensitivity (0.98) and specificity (0.79), which could improve radiologist diagnosis (maximum sensitivity, 0.75; maximum specificity, 0.51) for 23% and 28% respectively. Furthermore, the three-dimensional texture feature model of the voting method has the best diagnosis of precision rate (95.4%). Of all three-dimensional texture feature methods, the result of the voting method was the best, maintaining both high sensitivity and specificity scores. Additionally, the three-dimensional texture feature models were superior to two-dimensional models and radiologist-based assessments.

authors

  • Gao, Ni
  • Tian, Sijia
  • Li, Xia
  • Huang, Jian
  • Wang, Jingjing
  • Chen, Sipeng
  • Ma, Yuan
  • Liu, Xiangtong
  • Guo, Xiuhua

publication date

  • 2019