TY - JOUR
T1 - Radiomics of coronary artery calcium in the framingham heart study
AU - Eslami, Parastou
AU - Foldy, Borek
AU - Scholtz, Jan Erik
AU - Ivanov, Alexander
AU - Zeleznik, Roman
AU - Lu, Michael T.
AU - Ferencik, Maros
AU - Vasan, Ramachandran S.
AU - Baltrusaitis, Kristin
AU - Massaro, Joseph M.
AU - D’agostino, Ralph B.
AU - Mayrhofer, Thomas
AU - O’donnell, Christopher J.
AU - Aerts, Hugo J.W.L.
AU - Hoffmann, Udo
N1 - Publisher Copyright:
© RSNA, 2020.
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
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U2 - 10.1148/ryct.2020190119
DO - 10.1148/ryct.2020190119
M3 - Article
C2 - 32715301
AN - SCOPUS:85098209470
SN - 2638-6135
VL - 2
JO - Radiology: Cardiothoracic Imaging
JF - Radiology: Cardiothoracic Imaging
IS - 1
M1 - e190119
ER -