A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA

Subhi J. Al'Aref, Gurpreet Singh, Jeong W. Choi, Zhuoran Xu, Gabriel Maliakal, Alexander R. van Rosendael, Benjamin C. Lee, Zahra Fatima, Daniele Andreini, Jeroen J. Bax, Filippo Cademartiri, Kavitha Chinnaiyan, Benjamin J.W. Chow, Edoardo Conte, Ricardo C. Cury, Gudruf Feuchtner, Martin Hadamitzky, Yong Jin Kim, Sang Eun Lee, Jonathon A. LeipsicErica Maffei, Hugo Marques, Fabian Plank, Gianluca Pontone, Gilbert L. Raff, Todd C. Villines, Harald G. Weirich, Iksung Cho, Ibrahim Danad, Donghee Han, Ran Heo, Ji Hyun Lee, Asim Rizvi, Wijnand J. Stuijfzand, Heidi Gransar, Yao Lu, Ji Min Sung, Hyung Bok Park, Daniel S. Berman, Matthew J. Budoff, Habib Samady, Peter H. Stone, Renu Virmani, Jagat Narula, Hyuk Jae Chang, Fay Y. Lin, Lohendran Baskaran, Leslee J. Shaw, James K. Min

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Objectives: This study sought to identify culprit lesion (CL) precursors among acute coronary syndrome (ACS) patients based on qualitative and quantitative computed tomography–based plaque characteristics. Background: Coronary computed tomography angiography (CTA) has been validated for patient-level prediction of ACS. However, the applicability of coronary CTA to CL assessment is not known. Methods: Utilizing the ICONIC (Incident COroNary Syndromes Identified by Computed Tomography) study, a nested case-control study of 468 patients with baseline coronary CTA, the study included ACS patients with invasive coronary angiography–adjudicated CLs that could be aligned to CL precursors on baseline coronary CTA. Separate blinded core laboratories adjudicated CLs and performed atherosclerotic plaque evaluation. Thereafter, the study used a boosted ensemble algorithm (XGBoost) to develop a predictive model of CLs. Data were randomly split into a training set (80%) and a test set (20%). The area under the receiver-operating characteristic curve of this model was compared with that of diameter stenosis (model 1), high-risk plaque features (model 2), and lesion-level features of CL precursors from the ICONIC study (model 3). Thereafter, the machine learning (ML) model was applied to 234 non-ACS patients with 864 lesions to determine model performance for CL exclusion. Results: CL precursors were identified by both coronary angiography and baseline coronary CTA in 124 of 234 (53.0%) patients, with a total of 582 lesions (containing 124 CLs) included in the analysis. The ML model demonstrated significantly higher area under the receiver-operating characteristic curve for discriminating CL precursors (0.774; 95% confidence interval [CI]: 0.758 to 0.790) compared with model 1 (0.599; 95% CI: 0.599 to 0.599; p < 0.01), model 2 (0.532; 95% CI: 0.501 to 0.563; p < 0.01), and model 3 (0.672; 95% CI: 0.662 to 0.682; p < 0.01). When applied to the non-ACS cohort, the ML model had a specificity of 89.3% for excluding CLs. Conclusions: In a high-risk cohort, a boosted ensemble algorithm can be used to predict CL from non-CL precursors on coronary CTA.

Original languageEnglish
Pages (from-to)2162-2173
Number of pages12
JournalJACC: Cardiovascular Imaging
Issue number10
StatePublished - Oct 2020


  • acute coronary syndrome
  • coronary computed tomography angiography
  • diameter stenosis
  • machine learning


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