Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

Eur Radiol. 2016 Jul;26(7):2139-47. doi: 10.1007/s00330-015-4030-7. Epub 2015 Oct 6.


Objectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC's four-fold double reading process.

Methods: The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system.

Results: The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study.

Conclusions: On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process.

Key points: • CAD systems should be validated on public, heterogeneous databases. • The LIDC/IDRI database is an excellent database for benchmarking nodule CAD. • CAD can identify the majority of pulmonary nodules at a low false positive rate. • CAD can identify nodules missed by an extensive two-stage annotation process.

Keywords: Computer-assisted diagnosis; Image interpretation, computer-assisted; Lung; Lung cancer; Solitary pulmonary nodule.

Publication types

  • Comparative Study

MeSH terms

  • Databases, Factual*
  • Diagnosis, Computer-Assisted / methods
  • Humans
  • Lung / diagnostic imaging
  • Lung Neoplasms / diagnostic imaging*
  • Multiple Pulmonary Nodules / diagnostic imaging*
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Tomography, X-Ray Computed / methods*