TY - JOUR
T1 - Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients
AU - Gernaat, Sofie A. M.
AU - van Velzen, Sanne G. M.
AU - Koh, Vicky
AU - Emaus, Marleen J.
AU - Išgum, Ivana
AU - Lessmann, Nikolas
AU - Moes, Shinta
AU - Jacobson, Anouk
AU - Tan, Poey W.
AU - Grobbee, Diederick E.
AU - van den Bongard, Desiree H. J.
AU - Tang, Johann I.
AU - Verkooijen, Helena M.
N1 - Copyright © 2018 Elsevier B.V. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Purpose: This study automatically quantified calcifications in coronary arteries (CAC) and thoracic aorta (TAC) on breast planning computed tomography (CT) scans and assessed its reproducibility compared to manual scoring. Material and Methods: Dutch (n = 1199) and Singaporean (n = 1090) breast cancer patients with radiotherapy planning CT scan were included. CAC and TAC were automatically scored using deep learning algorithm. CVD risk categories were based on Agatson CAC: 0, 1–10, 11–100, 101–400 and >400. Reliability between automatic and manual scoring was assessed in 120 randomly selected CT scans from each population, with linearly weighted kappa for CAC categories and intraclass correlation coefficient for TAC. Results: Median age was higher in Dutch patients than Singaporean patients: 57 versus 52 years. CAC and TAC increased with age and were more present in Dutch patients than Singaporean patients: 24.2% versus 17.3% and 73.0% versus 62.2%, respectively. Reliability of CAC categories and TAC was excellent in the Netherlands (0.85 (95% confidence interval (CI) = 0.77–0.93) and 0.98 (95% CI = 0.96–0.98) respectively) and Singapore (0.90 (95% CI = 0.84–0.96) and 0.99 (95% CI = 0.98–0.99) respectively). Conclusions: CAC and TAC prevalence was considerable and increased with age. Deep learning software is a reliable method to automatically measure CAC and TAC on radiotherapy breast CT scans.
AB - Purpose: This study automatically quantified calcifications in coronary arteries (CAC) and thoracic aorta (TAC) on breast planning computed tomography (CT) scans and assessed its reproducibility compared to manual scoring. Material and Methods: Dutch (n = 1199) and Singaporean (n = 1090) breast cancer patients with radiotherapy planning CT scan were included. CAC and TAC were automatically scored using deep learning algorithm. CVD risk categories were based on Agatson CAC: 0, 1–10, 11–100, 101–400 and >400. Reliability between automatic and manual scoring was assessed in 120 randomly selected CT scans from each population, with linearly weighted kappa for CAC categories and intraclass correlation coefficient for TAC. Results: Median age was higher in Dutch patients than Singaporean patients: 57 versus 52 years. CAC and TAC increased with age and were more present in Dutch patients than Singaporean patients: 24.2% versus 17.3% and 73.0% versus 62.2%, respectively. Reliability of CAC categories and TAC was excellent in the Netherlands (0.85 (95% confidence interval (CI) = 0.77–0.93) and 0.98 (95% CI = 0.96–0.98) respectively) and Singapore (0.90 (95% CI = 0.84–0.96) and 0.99 (95% CI = 0.98–0.99) respectively). Conclusions: CAC and TAC prevalence was considerable and increased with age. Deep learning software is a reliable method to automatically measure CAC and TAC on radiotherapy breast CT scans.
KW - Aged
KW - Aorta, Thoracic/diagnostic imaging
KW - Aortic Diseases/diagnostic imaging
KW - Breast Neoplasms/diagnostic imaging
KW - Calcinosis/diagnostic imaging
KW - Coronary Artery Disease/diagnostic imaging
KW - Female
KW - Humans
KW - Male
KW - Middle Aged
KW - Radionuclide Imaging
KW - Radiotherapy Planning, Computer-Assisted/methods
KW - Reproducibility of Results
KW - Tomography, X-Ray Computed/methods
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046115965&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/29703498
U2 - https://doi.org/10.1016/j.radonc.2018.04.011
DO - https://doi.org/10.1016/j.radonc.2018.04.011
M3 - Article
C2 - 29703498
SN - 0167-8140
VL - 127
SP - 487
EP - 492
JO - Radiotherapy and oncology
JF - Radiotherapy and oncology
IS - 3
ER -