Journal Article

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

Lee, Kyong Joon, Jung, Cheolkyu, Choi, Byung Se, Kang, Ji Hee, Yoo, Roh-Eul, Kim, Jihang, Kim, Kwang-Gi, Kim, Jae Hyoung, Kim, Young Jae, Lee, Seung Hyun, Bae, Yun Jung, Choi, Seung Hong, Sunwoo, Leonard, Kang, Yeonah, Sohn, Chul-Ho

2017

Overview

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

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Aged / Algorithms / Artificial neural networks / Biology and Life Sciences / Biomedical engineering / Brain / Brain cancer / Brain Neoplasms - diagnosis / Brain Neoplasms - secondary / CAD / Classification / Cluster analysis / Clustering / Computer aided design / Computer aided testing / Computer and Information Sciences / Computers / Diagnostic systems / Female / Figure of merit / Gene expression / Hospitals / Humans / Image retrieval / Imaging, Three-Dimensional - methods / Machine Learning / Magnetic resonance / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Male / Medical imaging / Medicine / Medicine and Health Sciences / Metastases / Metastasis / Middle Aged / Models, Theoretical / Neural networks / Neuroimaging / Nodules / Patients / People and Places / Physical Sciences / Physicians / Radiology / Research and Analysis Methods / Resonance / Retrospective Studies / Review boards / Sensitivity / Sensitivity analysis / Sensitivity and Specificity / Software / Statistical analysis / Studies / Template matching / Test sets / Tomography, X-Ray Computed / Training / Vector quantization