Radiomics of coronary artery calcium in the framingham heart study

  • Parastou Eslami
  • , Borek Foldy
  • , Jan Erik Scholtz
  • , Alexander Ivanov
  • , Roman Zeleznik
  • , Michael T. Lu
  • , Maros Ferencik
  • , Ramachandran S. Vasan
  • , Kristin Baltrusaitis
  • , Joseph M. Massaro
  • , Ralph B. D’agostino
  • , Thomas Mayrhofer
  • , Christopher J. O’donnell
  • , Hugo J.W.L. Aerts
  • , Udo Hoffmann

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Purpose: To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events. Materials and Methods: Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002–2005) and were followed for more than a median of 9.1 years for composite major events. A total of 624 participants with CAC Agatston score (AS) of greater than 0 and good or excellent CT image quality were included for manual CAC segmentation and extraction of a predefined set of radiomic features reflecting intensity, shape, and texture. In a discov-ery cohort (n = 318), machine learning was used to select the 20 most informative and nonredundant CAC radiomic features, classify features predicting events, and define a radiomic-based score (RS). Performance of the RS was tested independently for the prediction of events in a validation cohort (n = 306). Results: The RS had a median value of 0.08 (interquartile range, 0.007–0.71) and a weak and modest correlation with Framingham risk score (FRS) (r = 0.2) and AS (r = 0.39), respectively. The continuous RS unadjusted, adjusted for age and sex, FRS, AS, and FRS plus AS were significantly associated with events (hazard ratio [HR] = 2.2, P <.001; HR = 1.8, P =.002; HR = 2.0, P <.001; HR = 1.7, P =.02; and HR = 1.8, P =.01, respectively). In participants with AS of less than 300, RS association with events remained significant when unadjusted and adjusted for age and sex, FRS, AS, and FRS plus AS (HR = 2.4, 2.8, 2.8, 2.3, and 2.6; P <.001, respec-tively). In the same subgroup of participants, adding the RS to AS resulted in a significant improvement in the discriminatory ability for events as compared with the AS (area under the receiver operating curve: 0.80 vs 0.68, respectively; P =.03). Conclusion: A radiomic-based score, including the complex properties of CAC, may constitute an imaging biomarker to be further de-veloped to identify individuals at risk for major adverse cardiovascular events in a community-based cohort.

Original languageEnglish (US)
Article numbere190119
JournalRadiology: Cardiothoracic Imaging
Volume2
Issue number1
DOIs
StatePublished - Feb 2020
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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