Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients

Gernaat, Sofie A M, van Velzen, Sanne G M, Koh, Vicky, Emaus, Marleen J, Išgum, Ivana, Lessmann, Nikolas, Moes, Shinta, Jacobson, Anouk, Tan, Poey W, Grobbee, Diederick E, van den Bongard, Desiree H J, Tang, Johann I, Verkooijen, Helena M


Radiotherapy & Oncology 127 (3), p. 487-492


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.