Computer-aided Detection of Brain Metastasis on 3D MR Imaging: Observer Performance Study

PLoS One. 2017 Jun 8;12(6):e0178265. doi: 10.1371/journal.pone.0178265. eCollection 2017.

Abstract

Purpose: To assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists' diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.

Materials and methods: The institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy.

Results: The sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time).

Conclusion: CAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

MeSH terms

  • Aged
  • Algorithms
  • Brain Neoplasms / diagnosis*
  • Brain Neoplasms / secondary*
  • Female
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Machine Learning
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Models, Theoretical
  • Retrospective Studies
  • Sensitivity and Specificity
  • Software
  • Tomography, X-Ray Computed

Grant support

This study was supported by a grant from the SNUH Research Fund (No. 0320140350 (2014-1078); http://www.snuh.org), and a grant from the National Research Foundation of Korea (NRF-2015R1C1A1A02037475; http://nrf.re.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.