Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study

被引:0
|
作者
Narula, Jagat [1 ]
Stuckey, Thomas D. [2 ]
Nakazawa, Gaku [3 ]
Ahmadi, Amir [4 ]
Matsumura, Mitsuaki [5 ]
Petersen, Kersten [6 ]
Mirza, Saba [6 ]
Ng, Nicholas [6 ]
Mullen, Sarah [6 ]
Schaap, Michiel [6 ]
Leipsic, Jonathan [7 ]
Rogers, Campbell [6 ]
Taylor, Charles A. [6 ]
Yacoub, Harout [8 ]
Gupta, Himanshu [9 ]
Matsuo, Hitoshi [10 ]
Rinehart, Sarah [11 ]
Maehara, Akiko [12 ]
机构
[1] McGovern Med Sch, Heart & Vasc Inst, 1825 Pressler St,SRB 205A, Houston, TX 77030 USA
[2] LeBauer Brodie Ctr, Heart & Vasc, Cone Hlth Heart & Vasc, Greensboro, NC USA
[3] Kindai Univ, Dept Med, Osaka, Japan
[4] Icahn Sch Med Mt Sinai, Cardiol, New York, NY USA
[5] Cardiovasc Res Fdn, Cardiol, New York, NY USA
[6] HeartFlow Inc, Mountain View, CA USA
[7] Univ British Columbia, Radiol, Vancouver, BC, Canada
[8] Staten Isl Univ Hosp, Northwell Hlth, Cardiol, New York, NY USA
[9] Valley Hlth Syst, Radiology, Ridgewood, NJ USA
[10] Gifu Heart Ctr, Med, Gifu, Japan
[11] Charleston Area Med Ctr Mem Hosp, Cardiol, Charleston, WV USA
[12] Columbia Univ, Cardiovasc Res Fdn, New York, NY USA
关键词
coronary artery disease; coronary luminal stenosis; vulnerable plaque; acute coronary syndrome; artificial intelligence; machine learning;
D O I
10.1093/ehjci/jeae115
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aims Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS).Methods and results The study included clinically stable patients with known coronary artery disease from 15 centres in the USA and Japan. An AI-enabled plaque analysis was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low-attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm, and mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements.Conclusion AI-enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment. Graphical Abstract Matching cross-sectional, longitudinal, and 3D views of IVUS and CCTA show correlating presence of calcified and non-calcified plaque between the two imaging modalities. The table highlights plaque volume correlations for total plaque (TPV), calcified plaque (CP), and non-calcified plaque (NCP) on a per-lesion basis (n = 432). In the cross-sectional and longitudinal view, blue-coloured areas are calcified plaque and yellow-coloured areas are non-calcified plaque. In the 3D view, blue-coloured area is calcified plaque and yellow translucent-coloured area is non-calcified plaque.
引用
收藏
页码:1287 / 1295
页数:9
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