Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study.

Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE

Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.

Adipose Tissue*/diagnostic imaging ; Computed Tomography Angiography* ; Nomograms*; Humans ; Female ; Male ; Middle Aged ; Retrospective Studies ; Coronary Artery Disease/diagnostic imaging ; Aged ; Image Processing, Computer-Assisted/methods ; Epicardial Adipose Tissue ; Radiomics

Background: The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE).
Purpose: To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE.
Methods: We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble.
Results: A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility.
Conclusions: The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.
(© 2024 American Association of Physicists in Medicine.)

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SYH-3201150-0010 2021005 Jiangsu Provincial Medical Association Roentgen Imaging Research; HS2019002 Nantong Clinical Medi-cine Center; JC2021195 Nantong Basic Science Research

Keywords: computed tomography angiography; major adverse cardiac events; nomogram; pericoronary adipose tissue; radiomics

Date Created: 20240723 Date Completed: 20241101 Latest Revision: 20241101

20241102

10.1002/mp.17324

39042398