Deep learning for automatic calcium scoring in population based cardiovascular screening

Background

High volumes of standardized coronary artery calcium (CAC) scans are generated in screening that need to be scored accurately and efficiently to risk stratify individuals.

Purpose

To evaluate the performance of deep learning based software for automatic coronary calcium scoring in a screening setting.

Methods

Participants from the Robinsca trial that underwent low-dose ECG-triggered cardiac CT for calcium scoring were included. CAC was measured with fully automated deep learning prototype and compared to the original manual assessment of the Robinsca trial. Detection rate, positive Agatston score and risk categorization (0–99, 100–399, ≥400) were compared using McNemar test, ICC, and Cohen's kappa. False negative (FN), false positive (FP) rate and diagnostic accuracy were determined for preventive treatment initiation (cut-off ≥100 AU).

Results

In total, 997 participants were included between December 2015 and June 2016. Median age was 61.0 y (IQR: 11.0) and 54.4% was male. A high agreement for detection was found between deep learning based and manual scoring, κ=0.87 (95% CI 0.85–0.89). Median Agatston score was 58.4 (IQR: 12.3–200.2) and 61.2 (IQR: 13.9–212.9) for deep learning based and manual assessment respectively, ICC was 0.958 (95% CI 0.951–0.964). Reclassification rate was 2.0%, with a very high agreement with κ=0.960 (95% CI: 0.943–0.997), p<0.001. FN rate was 0.7% and FP rate was 0.1% and diagnostic accuracy was 99.2% for initiation of preventive treatment.

Conclusion

Deep learning based software for automatic CAC scoring can be used in a cardiovascular CT screening setting with high accuracy for risk categorization and initiation of preventive treatment.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Robinsca trial was supported by advanced grant of European Research Council

Risk results from screening for a high cardiovascular disease risk by means of traditional risk factor measurement or coronary artery calcium scoring in the ROBINSCA trial

Aims

Screening for a high cardiovascular disease (CVD) risk followed by preventive treatment can potentially reduce coronary heart disease (CHD)-related morbidity and mortality. ROBINSCA (Risk Or Benefit IN Screening for CArdiovascular disease) is a population-based randomized controlled screening trial that investigates the effectiveness of CVD screening in asymptomatic participants using the Systematic COronary Risk Evaluation (SCORE) model or Coronary Artery Calcium (CAC) scoring. This study describes the distributions in risk and treatment in the ROBINSCA trial.

Methods and results

Individuals at expected elevated CVD risk were randomized (1:1:1) into the control arm (n=14,519; usual care); screening arm A (n=14,478; SCORE, 10-year fatal and non-fatal risk); or screening arm B (n=14,450; CAC scoring). Preventive treatment was largely advised according to current Dutch guidelines. Risk and treatment differences between the screening arms were analysed. 12,185 participants (84.2%) in arm A and 12,950 (89.6%) in arm B were screened. 48.7% were women, and median age was 62 (InterQuartile Range 10) years. SCORE screening identified 45.1% at low risk (SCORE<10%), 26.5% at intermediate risk (SCORE 10–20%), and 28.4% at high risk (SCORE≥20%). According to CAC screening, 76.0% were at low risk (Agatston<100), 15.1% at high risk (Agatston 100–399), and 8.9% at very high risk (Agatston≥400). CAC scoring significantly reduced the number of individuals indicated for preventive treatment compared to SCORE (relative reduction women: 37.2%; men: 28.8%).

Conclusion

We showed that compared to risk stratification based on SCORE, CAC scoring classified significantly fewer men and women at increased risk, and less preventive treatment was indicated.

ROBINSCA flowchart

Funding Acknowledgement

Type of funding source: Public grant(s) – EU funding. Main funding source(s): Advanced Research Grant

Relationship between cardiovascular risk factors and coronary calcification in a middle-aged Dutch population: the Imalife study

Background

Systematic COronary Risk Evaluation (SCORE) has been proposed to assess the 10-year risk of fatal cardiovascular diseases, with distinction between low-risk and high-risk countries. Risk modifiers are recommended to further improve risk reclassification, for example the coronary artery calcium (CAC) score. CAC scoring can significantly improve risk prediction for coronary events based on outcome studies. The impact of CAC scoring on risk classification in a middle-aged cohort from a low-risk country in comparison to SCORE is unknown.

Purpose

To assess presence of coronary calcification and association with cardiovascular risk factors and related SCORE risk in a middle-aged population from a low risk country.

Methods

Coronary calcification and classical cardiovascular risk factors were analyzed in 4,083 Dutch participants aged 45–60 years (57.9% women) without a known history of coronary artery disease in the population-based ImaLife (Imaging in Lifelines) study. Individuals underwent non-contrast cardiac CT using third generation dual-source CT. Coronary artery calcium (CAC) scores were quantified using Agatston's method. Age- and sex- specific distributions of CAC categories (0, 1–99, 100–299, ≥300) and percentiles were assessed. Distribution of CAC categories was compared to SCORE risk categories (<1%, ≥1% to 5%, and ≥5%) for low risk countries. Relationship between risk factors and CAC presence was evaluated by logistic regression models. Population attributable fractions (PAFs) of classical risk factors for CAC presence were estimated to investigate potential prevention strategy.

Results

CAC was present in 54.5% of men and in 26.5% of women. With increasing age, an increasing percentage had a positive CAC score, from 38.1% of men and 15.2% of women at age 45–49 years, to 66.9% of men and 36.6% of women at age 55–60. Mean SCORE was 1.3% (2.0% in men, 0.7% in women). In SCORE risk <1%, 32.7% of men and 17.1% of women had CAC. In men with SCORE risk ≥5% (0.1% of women), 26.9% had no CAC. Overall PAF for presence of CAC of the classical risk factors was 18.5% in men and 31.4% in women. PAF was highest for hypertension (in men 8.0%, 95% CI 4.2–11.8%; in women 13.1%, 95% CI 7.9–18.2%) followed by hypercholesterolemia and obesity.

Conclusion

In this middle-aged Dutch cohort, slightly over half of men and a quarter of women had any CAC. With age there was an increase in CAC presence for both sexes. Only a minor proportion of CAC presence was attributable to classical risk factors. This provides further support that CAC scoring can impact risk classification in a middle-aged population of a low-risk country.

Funding Acknowledgement

Type of funding source: Other. Main funding source(s): The ImaLife study is supported by an institutional research grant from Siemens Healthineers and by the Ministry of Economic Affairs and Climate Policy by means of the PPP Allowance made available by the Top Sector Life Sciences & Health to stimulate public-private partnerships.

Calcium scores distribution across coronary artery by age and sex: the ImaLife study

Background

The distribution of coronary artery calcium (CAC) across the coronary system increases the ability to predict coronary events compared to traditional CAC scoring alone. Reference values for regional distribution of CAC by age and sex are not yet available for a general European population.

Purpose

To investigate the distribution of CAC across the coronary arteries by age and sex in the population-based ImaLife study.

Methods

ImaLife is part of Lifelines, a multi-generational, prospective cohort study with over 167,000 participants from the northern Netherlands. From 2017–2019, 5,531 participants aged 45–84 years underwent non-contrast cardiac CT using third-generation dual-source CT. Total and vessel-specific CAC scores (Agatston's method) were acquired semi-automatically using dedicated software. Participants with a positive CAC score were classified into three groups: total CAC score 1–100, 101–300 and >300. The diffusivity index (equation: 1 – [highest one-vessel CAC/total CAC]) was calculated. The diffusivity index is an expression of the relative distribution of CAC across the coronary arteries. Data were analyzed for the whole population and by sex and age groups. Mann-Whitney U test was used to analyze the diffusity index in men and women. Kruskal-Wallis H tests were performed to test the diffusivity index in different age groups.

Results

In total 2,376 men (mean age 56.4±7.7 years) and 3,155 women (mean age 56.0±7.5 years) were analyzed. In participants with CAC, 1, 2, 3 or 4 vessels were affected in 523 (22.0%), 560 (17.7%), 371 (15.6%) and 257 (8.1%) of men, respectively, and in 385 (16.2%), 175 (5.5%), 185 (7.8%) and 81 (2.6%) of women, respectively (P<0.001). The number of 1, 2, 3 or 4 vessels affected were significantly different by age (p<0.001). In age category 45–49 years, CAC in 1, 2, 3, and 4 vessels was present in 60.1%, 21.6%, 15.5%, and 2.9%, respectively; for age 74+ years, these percentages were 19.3%, 19.3%, 31.1% and 30.3%, respectively. The number of affected vessels were significantly different in different CAC categories (p<0.001), see Figure. More vessels were affected in higher CAC categories. The median diffusivity index was higher in men than in women (0.10 (IQR: 0–0.36) vs 0 (IQR: 0–0.24), p<0.001) and increased by increasing age. For age categories of 45–49, 50–54, 55–59,60–64, 65–69, 70–74, and >74 years, diffusivity indexs were 0 (IQR: 0–0.12), 0 (IQR: 0–0.22), 0.02 (IQR: 0–0.28), 0.10 (IQR: 0–0.35), 0.16 (IQR: 0–0.42), 0.20 (IQR: 0–0.44), and 0.28 (IQR: 0.03–0.45) (p<0.001).

Conclusions

In this Dutch population-based study, male participants had higher prevalence of CAC with higher number of involved vessels, and a higher diffusivity index compared to women. For both sexes, involved vessels and diffusivity index increased with age. The reference values of this regional distribution of CAC in a European population can assist in risk categorization of cardiovascular events.

The CAC distribution in ImaLife

Funding Acknowledgement

Type of funding source: Other. Main funding source(s): Siemens Healthineers

Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review

This review provides an overview of the currently available computed tomography (CT) techniques for myocardial tissue characterization in ischemic heart disease, including CT perfusion and late iodine enhancement. CT myocardial perfusion imaging can be performed with static and dynamic protocols for the detection of ischemia and infarction using either single- or dual-energy CT modes. Late iodine enhancement may be used for the analysis of myocardial infarction. The accuracy of these CT techniques is highly dependent on the imaging protocol, including acquisition timing and contrast administration. Additionally, the options for qualitative and quantitative analysis and the accuracy of each technique are discussed.

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the clinical workflow. This review is a comprehensive overview on the types of tasks and applications in which AI can aid the clinician in cardiac CT, and can be used as a primer for medical researchers starting in the field of AI. The applications of AI algorithms are explained and recent examples in cardiac CT of these algorithms are further elaborated on. The critical factors for implementation in the future are discussed.

P3397Risk Or Benefit IN Screening for CArdiovascular disease (ROBINSCA): results from screening for a high cardiovascular disease risk by using a risk prediction model or coronary artery calcium scoring

Introduction

The ROBINSCA (Risk Or Benefit IN Screening for CArdiovascular disease) trial is a large-scale population-based randomized controlled screening trial with the aim to investigate whether screening for a high risk of cardiovascular disease (CVD) by means of either the Systematic COronary Risk Evaluation (SCORE) model or coronary artery calcium (CAC) scoring followed by preventive treatment is effective in reducing morbidity and mortality from coronary heart disease (CHD). This study shows the results of the CVD risks as assessed by the two screening tools.

Methods

Based on the Dutch population registry, 394,058 men aged 45–74 years and women aged 55–74 years received an information brochure, an invitation to participate in the trial, a baseline questionnaire with waist circumference tape and an informed consent form. Eligible individuals with an expected high CVD risk were randomized (1:1:1) into a control arm (n=14,519), intervention arm A (n=14,478) or intervention arm B (n=14,450). In the control arm, usual care was continued. In intervention arm A, participants were screened for a high risk of CVD using the SCORE model based on traditional risk factors. In intervention arm B, CAC scoring after computed tomography scanning was used for screening. After screening en risk communication, preventive treatment according to the Dutch guidelines is advised for high risk persons.

Results

Screening uptake was 84.2% in intervention arm A and 89.6% in intervention arm B. Of the screened participants, 48.7% was female, median age at screening was 62 (Interquartile Range 10), 35.2% was high educated, 19.6% was baseline smoker and 41.4% had a positive family history of myocardial infarction. The assessed CVD risk status according to SCORE screening was stratified into three risk categories; 45.1% was at low risk (SCORE<10%), 26.5% was at intermediate risk (SCORE 10–20%), and 28.4% was at high risk (SCORE ≥20%). According to CAC screening, 76.0% was at low risk (Agatston <100), 15.1% was at high risk (Agatston 100–399), and 8.9% was at very high risk (Agatston ≥400). Associations between baseline variables and increased CVD risk will be analyzed soon and will be available in summer 2019.

Conclusions

Using different screening tools resulted in reclassification of the CVD risk. CAC screening caused a substantial shift to more low risk individuals. This might, when screening is found to be effective, lead to less overtreatment in prevention of CVD events. Future 5-year follow-up data should provide evidence about whether population-based screening with subsequent preventive treatment is (cost-)effective in reducing CHD-related morbidity and mortality.

P5309Age and gender distributions of coronary artery calcium in the Dutch adult population: preliminary results of the ImaLife study

Background

Coronary artery calcium (CAC) scoring is a promising tool for cardiovascular risk classification. Population-based reference values are important for the clinical interpretation of CAC scores.

Purpose

To establish standards of CAC distributions by age and gender in an unselected Dutch population, which can be used to determine reference values.

Methods

ImaLife (Imaging in Lifelines) is a computed tomography (CT) based substudy of the Lifelines cohort, with a primary aim to establish reference values of imaging biomarkers for early stages of coronary artery disease in adults (above 45 years old). In total, 12,000 participants will be enrolled from an unselected adult population in the northern Netherlands for CAC scoring with third generation dual-source CT. CAC is quantified with dedicated commercial software using the Agatston method.

Results

Included so far were 3,702 participants (57.5% females, mean age 54 years, range 45–82 years). CAC was present in 39.2% of participants, with a higher prevalence of CAC in men (55.3%) than in women (27.3%). CAC scores increased with increasing age in both genders. The percentiles of CAC scores by age and gender groups are summarized in the table.

Agatston CAC score percentiles by age and gender Percentiles Women – Age, years Men – Age, years 45–49 50–54 55–59 60–64 65∼ 45–49 50–54 55–59 60–64 65∼ N 505 634 719 260 10 355 473 543 185 18 25th 0 0 0 0 0 0 0 0 1 75 50th 0 0 0 0 4 0 1 6 22 556 75th 0 0 6 33 386 6 21 72 129 751 90th 4 26 77 120 1037 49 154 242 500 1803

Conclusion

This preliminary result presents CAC distribution by age and gender in a middle-aged unselected Dutch population. Compared with the Heinz Nixdorf Recall study, CAC scores in our cohort for both genders were lower in the 5-year age groups between 45 and 64 years. Based on the overall data, expected within 2 years, reference values of CAC for the Dutch population can be established.

P1555Agreement of coronary calcium scoring on chest CT and ECG triggered cardiac CT: a population-based study

Background

Low-dose chest computed tomography (CT) is increasingly used in lung cancer screening. The heart is inherently visualized on chest CT. Coronary artery calcium (CAC) identified on chest scans has predictive value for risk of cardiovascular disease. There is discussion whether non-ECG-triggered chest CT is reliable for CAC scoring.

Purpose

To investigate the agreement between chest CT and ECG-triggered cardiac CT in CAC identification and risk classification.

Methods

We included 1000 ImaLife participants who underwent a cardiac scan immediately followed by a non-ECG triggered chest scan. Third-generation dual-source CT and dedicated software were used for scan acquisition and CAC measurement. Chest scans were analyzed after cardiac scans with an interval of at least a month and in a different order. To ensure a comparable prevalence of CAC with previous studies and adequate samples in CAC strata, after the inclusion of the 500th consecutive participants with zero CAC, only participants with >0 CAC based on dedicated cardiac CT were included. CAC scores were divided into four risk strata: 0, 1–99, 100–399, 400. Kappa was used to assess agreement in CAC identification (0 versus >0) and risk classification.

Results

The mean age was 54 years (range 45–77), 42.5% were women, average body mass index (BMI) was 26.1kg/m2. Compared with dedicated cardiac CT, non-ECG triggered chest CT had an accuracy of 0.97, sensitivity of 0.96 and specificity of 0.99 for identifying CAC, and agreement between scans was very high (kappa 0.95) for CAC presence. In terms of CAC risk strata, chest CT had a very high agreement with cardiac CT (kappa 0.95). Total misclassification rate of CAC strata was 6.5%, with most misclassified cases shifting one risk category downward (55/65, 85%). BMI of discordant pairs was significantly higher than concordant pairs, while no difference in heart rate was found.

Conclusion

Non-ECG triggered chest CT may be reliably used for CAC identification and risk classification since chest CT has very high agreement with dedicated cardiac CT results.