OBJECTIVE: The purpose of this study was to evaluate the performance of an automated computer-assisted detection (CAD) algorithm to detect coronary artery stenosis on coronary CT angiography (CTA). MATERIALS AND METHODS: We investigated 128 consecutive patients (76 men, 52 women; mean [SD] age, 64 ± 11 years) who had acute chest pain and underwent 128-slice dual-source coronary CTA and invasive coronary angiography at an emergency department. All coronary CTA data were analyzed using customized software for the detection of coronary artery stenosis without human interaction. The diagnostic performance of a CAD algorithm for evaluation of stenosis of at least 50% of vessel diameter was compared with that of human interpretation of coronary CTA, with invasive coronary angiography as a reference standard. RESULTS: Of the 128 patients, 25 patients were excluded because of failure of data processing (n = 9) or history of stent insertion or coronary artery bypass graft (n = 16). Invasive coronary angiography revealed significant stenosis in 62% (64/103) of the remaining patients. In detecting significant stenosis, the CAD algorithm yielded 100% sensitivity, 23.1% specificity, 68.1% positive predictive value (PPV), and 100% negative predictive value (NPV) in per-patient analysis. On per-vessel analysis, the CAD algorithm yielded 90.0% sensitivity, 62.4% specificity, 40.1% PPV, and 95.7% NPV. Human interpretation of coronary CTA yielded 98.4% and 96.7% sensitivities, 79.5% and 95.0% specificities, 88.7% and 84.5% PPVs, and 96.9% and 99.0% NPVs for diagnosing significant stenosis on per-patient and per-vessel analyses, respectively. CONCLUSION: The CAD algorithm yields a high NPV in detecting stenosis of at least 50% on coronary CTA. As a second "reader," the CAD algorithm may help to exclude significant coronary stenosis in patients with acute chest pain at an emergency department.
OBJECTIVE: The purpose of this study was to evaluate the performance of an automated computer-assisted detection (CAD) algorithm to detect coronary artery stenosis on coronary CT angiography (CTA). MATERIALS AND METHODS: We investigated 128 consecutive patients (76 men, 52 women; mean [SD] age, 64 ± 11 years) who had acute chest pain and underwent 128-slice dual-source coronary CTA and invasive coronary angiography at an emergency department. All coronary CTA data were analyzed using customized software for the detection of coronary artery stenosis without human interaction. The diagnostic performance of a CAD algorithm for evaluation of stenosis of at least 50% of vessel diameter was compared with that of human interpretation of coronary CTA, with invasive coronary angiography as a reference standard. RESULTS: Of the 128 patients, 25 patients were excluded because of failure of data processing (n = 9) or history of stent insertion or coronary artery bypass graft (n = 16). Invasive coronary angiography revealed significant stenosis in 62% (64/103) of the remaining patients. In detecting significant stenosis, the CAD algorithm yielded 100% sensitivity, 23.1% specificity, 68.1% positive predictive value (PPV), and 100% negative predictive value (NPV) in per-patient analysis. On per-vessel analysis, the CAD algorithm yielded 90.0% sensitivity, 62.4% specificity, 40.1% PPV, and 95.7% NPV. Human interpretation of coronary CTA yielded 98.4% and 96.7% sensitivities, 79.5% and 95.0% specificities, 88.7% and 84.5% PPVs, and 96.9% and 99.0% NPVs for diagnosing significant stenosis on per-patient and per-vessel analyses, respectively. CONCLUSION: The CAD algorithm yields a high NPV in detecting stenosis of at least 50% on coronary CTA. As a second "reader," the CAD algorithm may help to exclude significant coronary stenosis in patients with acute chest pain at an emergency department.
Authors: Christian Thilo; Mulugeta Gebregziabher; Felix G Meinel; Roman Goldenberg; John W Nance; Elisabeth M Arnoldi; Lashonda D Soma; Ullrich Ebersberger; Philip Blanke; Richard L Coursey; Michael A Rosenblum; Peter L Zwerner; U Joseph Schoepf Journal: Eur Radiol Date: 2014-10-15 Impact factor: 5.315