Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Issue 4 (April 2022)

• Record Type: Journal Article
• Authors: Lin, Andrew; Manral, Nipun; McElhinney, Priscilla; Killekar, Aditya; Matsumoto, Hidenari; Kwiecinski, Jacek; Pieszko, Konrad; Razipour, Aryabod; Grodecki, Kajetan; Park, Caroline; Otaki, Yuka; Doris, Mhairi; Kwan, Alan C; Han, Donghee; Kuronuma, Keiichiro; Flores Tomasino, Guadalupe; Tzolos, Evangelos; Shanbhag, Aakash; Goeller, Markus; Marwan, Mohamed; Gransar, Heidi; Tamarappoo, Balaji K; Cadet, Sebastien; Achenbach, Stephan; Nicholls, Stephen J; Wong, Dennis T; Berman, Daniel S; Dweck, Marc; Newby, David E; Williams, Michelle C; Slomka, Piotr J; Dey, Damini;
• Is Part Of: Lancet. Volume 4:Issue 4(2022)
• Page Start: e256
• Page End: e265
• Publication Date: 2022-04
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(22)00022-X
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: Background: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. Methods: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. Findings: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm 3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). Interpretation: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction. Funding: National Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.