Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI

Kevin Li-Chun Hsieh, Ruei-Je Tsai, Yu-Chuan Teng and Chung-Ming Lo

PLOS ONE, 2017, vol. 12, issue 2, 1-14

Abstract: The effects of a computer-aided diagnosis (CAD) system based on quantitative intensity features with magnetic resonance (MR) imaging (MRI) were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105), a sensitivity of 79% (27/34), a specificity of 90% (64/71), and an area under the receiver operating characteristic curve (Az) of 0.89. In the evaluation, the radiologists’ Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists’ confidence rating.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0171342

DOI: 10.1371/journal.pone.0171342

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