The method could easily be integrated into lung cancer screening, Dr. de Vos said. It does not require any special equipment and would not add time to the exam.
"The method uses only image information, it is fully automatic, and it is fast," Dr. de Vos said. "The method obtains calcium scores in a complete chest CT in less than half a second. This means that the method should be easy to implement in routine patient work ups and screening."
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Most importantly, the method could help identify people in a population of heavy smokers who might be at increased risk of death from cardiovascular disease-related causes.
"Lung screening studies show that heavy smokers die from cardiovascular disease as much as from lung cancer," Dr. de Vos said. "But we also see that some people with very high calcium scores survive, while others with low scores do suffer from major cardiac events. The work offers a direction for future research to precisely pinpoint which calcifications are dangerous."
The researchers have developed a number of methods for automatic calcium scoring that can be applied to a wide variety of data. They are now working toward a calcium scoring method that accurately detects arterial calcification in low-quality data, like data affected by cardiac motion, low image resolution or high noise levels.
"We developed a method, for example, that can detect coronary calcifications even when the lesions are below the clinically used threshold," Dr. de Vos said. "This way, we hope to increase the reproducibility of calcium scoring and enable more accurate prediction."
The United States Preventive Services Task Force (USPSTF) recently expanded its recommendation for low-dose CT lung cancer screening to include high-risk individuals, 50 to 80 years of age, who have a 20-pack-year or more history of smoking and are either current smokers or former smokers who have quit within the last 15 years, facilitating screening access for a larger and more diverse population. Read more in RSNA News.
"Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT." Collaborating with Dr. de Vos were Nikolas Lessmann, Ph.D., Pim A. de Jong, M.D., Ph.D., and Ivana Išgum, Ph.D.
Radiology: Cardiothoracic Imaging is edited by Suhny Abbara, M.D., University of Texas Southwestern Medical Center, Dallas, and owned and published by the Radiological Society of North America, Inc. (https://pubs.rsna.org/cardiothoracic-imaging)
