Prediction of the development of new coronary atherosclerotic plaques with radiomics

Sang Eun Lee, Youngtaek Hong, Jongsoo Hong, Juyeong Jung, Ji Min Sung, Daniele Andreini, Mouaz H. Al-Mallah, Matthew J. Budoff, Filippo Cademartiri, Kavitha Chinnaiyan, Jung Hyun Choi, Eun Ju Chun, Edoardo Conte, Ilan Gottlieb, Martin Hadamitzky, Yong Jin Kim, Byoung Kwon Lee, Jonathon A. Leipsic, Erica Maffei, Hugo MarquesPedro de Araújo Gonçalves, Gianluca Pontone, Sanghoon Shin, Peter H. Stone, Habib Samady, Renu Virmani, Jagat Narula, Leslee J. Shaw, Jeroen J. Bax, Fay Y. Lin, James K. Min, Hyuk Jae Chang

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). Methods: From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV ​≥ ​1 ​mm3, at follow-up CCTA in each segment. Results: In total, 9583 normal coronary segments were identified from 1162 patients (60.3 ​± ​9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690–0.712] vs. 0.699 [0.0.688–0.710] and 0.696 [0.671–0.725] vs. 0.0.691 [0.667–0.715], respectively, all p ​> ​0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762–0.781] and 0.767 [0.751–0.787], respectively, all p ​< ​00.0001 compared to Models 1 and 2). Conclusion: Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. Clinical Trial Registration: ClinicalTrials.gov NCT0280341.

Original languageEnglish
Pages (from-to)274-280
Number of pages7
JournalJournal of Cardiovascular Computed Tomography
Volume18
Issue number3
DOIs
StatePublished - 1 May 2024

Keywords

  • Coronary artery atherosclerosis
  • Coronary artery disease
  • Coronary computed tomography angiography
  • Radiomics

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