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The Usefulness of Computer-Aided Detection of Brain Metastases on Contrast-Enhanced Computed Tomography Using Single-Shot Multibox Detector: Observer Performance Study

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Objective This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography. Methods The test data set included whole-brain… Click to show full abstract

Objective This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography. Methods The test data set included whole-brain axial contrast-enhanced computed tomography images of 25 cases with 62 BMs and 5 cases without BM. Six radiologists from 3 institutions with 2 to 4 years of experience independently reviewed the cases, both in conditions with and without CAD assistance. Sensitivity, positive predictive value, number of false positives, and reading time were compared between the conditions using paired t tests. Subanalysis was also performed for groups of lesions divided according to size. A P value <0.05 was considered statistically significant. Results With CAD, sensitivity significantly increased from 80.4% to 83.9% (P = 0.04), whereas positive predictive value significantly decreased from 88.7% to 84.8% (P = 0.03). Reading time with and without CAD was 112 and 107 seconds, respectively (P = 0.38), and the number of false positives was 10.5 with CAD and 7.0 without CAD (P = 0.053). Sensitivity significantly improved for 6- to 12-mm lesions, from 71.2% without CAD to 80.3% with CAD (P = 0.02). The sensitivity of the CAD (95.2%) was significantly higher than that of any reader (with CAD: P = 0.01; without CAD: P = 0.005). Conclusions Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives.

Keywords: cad; aided detection; tomography; detection; computer aided

Journal Title: Journal of Computer Assisted Tomography
Year Published: 2022

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