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

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

PLOS ONE, 2017, vol. 12, issue 6, 1-18

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

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0178265

DOI: 10.1371/journal.pone.0178265

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