European Radiology

, Volume 24, Issue 11, pp 2719–2728

Toward clinically usable CAD for lung cancer screening with computed tomography

  • Matthew S. Brown
  • Pechin Lo
  • Jonathan G. Goldin
  • Eran Barnoy
  • Grace Hyun J. Kim
  • Michael F. McNitt-Gray
  • Denise R. Aberle
Oncology

DOI: 10.1007/s00330-014-3329-0

Cite this article as:
Brown, M.S., Lo, P., Goldin, J.G. et al. Eur Radiol (2014) 24: 2719. doi:10.1007/s00330-014-3329-0

Abstract

Objectives

The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice.

Methods

A new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set.

Results

The test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90.

Conclusions

The new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality.

Key Points

CAD requirements can be based on lung cancer screening trial results.

CAD systems can be evaluated using publically available annotated CT image databases.

A new CAD system was developed with a low false positive rate.

The CAD system has reliable measurement tools needed for clinical use.

Keywords

Lung cancer Multiple pulmonary nodules Computer-assisted diagnosis Early detection of cancer X-ray computerized axial tomography 

Copyright information

© European Society of Radiology 2014

Authors and Affiliations

  • Matthew S. Brown
    • 1
  • Pechin Lo
    • 1
  • Jonathan G. Goldin
    • 1
  • Eran Barnoy
    • 1
  • Grace Hyun J. Kim
    • 1
  • Michael F. McNitt-Gray
    • 1
  • Denise R. Aberle
    • 1
  1. 1.Center for Computer Vision and Imaging Biomarkers, Department of Radiological SciencesDavid Geffen School of Medicine at UCLALos AngelesUSA

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