Bragatston study protocol: a multicentre cohort study on automated quantification of cardiovascular calcifications on radiotherapy planning CT scans for cardiovascular risk prediction in patients with breast cancer

Cancer therapy-related cardiac dysfunction and the role of cardiovascular imaging: systemic review and opinion paper from the Working Group on Cardio-Oncology of the Korean Society of Cardiology

Cardio-oncology is a critical field due to the escalating significance of cardiovascular toxicity as a side effect of anticancer treatments. Cancer therapy-related cardiac dysfunction (CTRCD) is a prevalent condition associated with cardiovascular toxicity, necessitating effective strategies for prediction, monitoring, management, and tracking. This comprehensive review examines the definition and risk stratification of CTRCD, explores monitoring approaches during anticancer therapy, and highlights specific cardiovascular toxicities linked to various cancer treatments. These include anthracyclines, HER2-targeted agents, vascular endothelial growth factor inhibitors, immune checkpoint inhibitors, chimeric antigen receptor T-cell therapies, and tumor-infiltrating lymphocytes therapies. Incorporating the Korean data, this review offers insights into the regional nuances in managing CTRCD. Using systematic follow-up incorporating cardiovascular imaging and biomarkers, a better understanding and management of CTRCD can be achieved, optimizing the cardiovascular health of both cancer patients and survivors.

Competing risk analysis of cardiovascular disease risk in breast cancer patients receiving a radiation boost

BACKGROUND

Thoracic radiotherapy may damage the myocardium and arteries, increasing cardiovascular disease (CVD) risk. Women with a high local breast cancer (BC) recurrence risk may receive an additional radiation boost to the tumor bed.

OBJECTIVE

We aimed to evaluate the CVD risk and specifically ischemic heart disease (IHD) in BC patients treated with a radiation boost, and investigated whether this was modified by age.

METHODS

We identified 5260 BC patients receiving radiotherapy between 2005 and 2016 without a history of CVD. Boost data were derived from hospital records and the national cancer registry. Follow-up data on CVD events were obtained from Statistics Netherlands until December 31, 2018. The relation between CVD and boost was evaluated with competing risk survival analysis.

RESULTS

1917 (36.4%) received a boost. Mean follow-up was 80.3 months (SD37.1) and the mean age 57.8 years (SD10.7). Interaction between boost and age was observed for IHD: a boost was significantly associated with IHD incidence in patients younger than 40 years but not in patients over 40 years. The subdistribution hazard ratio (sHR) was calculated for ages from 25 to 75 years, showing a sHR range from 5.1 (95%CI 1.2-22.6) for 25-year old patients to sHR 0.5 (95%CI 0.2-1.02) for 75-year old patients.

CONCLUSION

In patients younger than 40, a radiation boost is significantly associated with an increased risk of CVD. In absolute terms, the increased risk was low. In older patients, there was no association between boost and CVD risk, which is likely a reflection of appropriate patient selection.

Academic patterns of practice regarding CT simulation scans and radiology review

OBJECTIVE

Currently, there are no consensus guidelines about handling incidental radiological findings on radiotherapy planning CT simulation scans. Retrospective studies analyzing incidental findings on CT simulations show a small, but not insignificant, rate of both oncologic and non-oncologic findings. These findings may have medico-legal, financial, and clinical implications. Given a lack of guidelines, we obtained a formal survey of multiple academic institutions to evaluate how CT simulations are handled in regard to incidental findings.

METHODS

A formal survey was developed consisting of 12 questions related to institutional practices regarding CT simulation scans. From 7/18/21 to 8/27/21 and 5/6/22 to 5/24/22, the survey was administered electronically by REDCap to key personnel at Academic Radiation Oncology Programs identified through the American Society for Radiation Oncology (ASTRO) with inclusion criteria including an active ACGME approved Radiation Oncology residency program.

RESULTS

In total, 88 academic radiation oncology programs were surveyed with total of 45 responses (51%). 1 out of 45 departments who responded has formal guidelines regarding workup of incidental findings. There is variability about sending CT simulation scans for official radiology review if an incidental finding is identified.

CONCLUSIONS

Based on a measurable rate of incidental findings on radiotherapy planning CT simulations and their possible implications, our survey illustrates a likely need for consensus recommendations for handling such findings to improve patient care and safety.

Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics' Common Data Model

Background

Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure.

Objective

The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health Data Sciences and Informatics’ Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) data and the population-level estimation method.

Methods

Using electronic health record–based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses.

Results

The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02).

Conclusions

Although we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years.

Generative models for reproducible coronary calcium scoring

Purpose: Coronary artery calcium (CAC) score, i.e., the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segmentation of calcifications. This limitation is especially pronounced in non-electrocardiogram-synchronized computed tomography (CT) where lesions are more impacted by cardiac motion and partial volume effects. Therefore, we propose a CAC quantification method that does not require a threshold for segmentation of CAC. Approach: Our method utilizes a generative adversarial network (GAN) where a CT with CAC is decomposed into an image without CAC and an image showing only CAC. The method, using a cycle-consistent GAN, was trained using 626 low-dose chest CTs and 514 radiotherapy treatment planning (RTP) CTs. Interscan reproducibility was compared to clinical calcium scoring in RTP CTs of 1662 patients, each having two scans. Results: A lower relative interscan difference in CAC mass was achieved by the proposed method: 47% compared to 89% manual clinical calcium scoring. The intraclass correlation coefficient of Agatston scores was 0.96 for the proposed method compared to 0.91 for automatic clinical calcium scoring. Conclusions: The increased interscan reproducibility achieved by our method may lead to increased reliability of CHD risk categorization and improved accuracy of CHD event prediction.

Next Generation Risk Markers in Preventive Cardio-oncology

PURPOSE OF REVIEW

Cardiovascular disease (CVD) and cancer are the first and second most common causes of death within the USA. It is well established that a diagnosis of cancer increases risk and predisposes the patient to CVD, and vice versa. Despite these associations, cancer is not yet incorporated into current CVD risk calculators, necessitating additional CV risk markers for improved stratification in this at-risk population. In this review, we consider the utility of breast arterial calcification (BAC), coronary artery calcification (CAC), clonal hematopoiesis of indeterminate potential (CHIP), and cancer and cancer treatment in CVD risk assessment.

RECENT FINDINGS

There is evidence supporting the use of BAC, CAC, CHIP, and cancer and cancer treatment for improved CV risk stratification in patients with cancer and those who are being screened for cancer. BAC has been shown to predict CAC, coronary atherosclerotic plaque on coronary CTA, coronary artery stenosis on coronary angiography, and CVD events and accordingly enhances CVD risk stratification beyond the atherosclerotic CVD (ASCVD) risk pooled cohort equation. Additionally, CAC visualized on CT utilized for lung cancer screening, radiation planning, and cancer staging is predictive of coronary artery disease (CAD). Furthermore, CHIP can also be utilized in risk stratification, as the presence of CHIP carries a 40% increase in CV risk independent of traditional CV risk factors. Finally, cancer and many oncologic therapies confer a lifelong increased risk of CVD. We propose an emerging set of tools to be incorporated into the routine continuum of CVD risk assessment in individuals who have been treated for cancer or who are being screened for cancer development. In this review, we discuss BAC, CAC, CHIP, and cancer and cancer treatment as emerging risk markers in cardiovascular health assessment. Their effectiveness in predicting and influencing the burden of CVD will be discussed, along with suggestions on their incorporation into preventive cardio-oncology practice. Future research will focus on short- and long-term CVD outcomes in these populations.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

PURPOSE

Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs).

METHODS

Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors.

RESULTS

The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively.

CONCLUSION

In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.

Cardiac Computed Tomography in Cardio-Oncology: JACC: CardioOncology Primer

Cancer patients and survivors have elevated cardiovascular risk when compared with noncancer patients. Cardio-oncology has emerged as a new subspecialty to comanage and address cardiovascular complications in cancer patients such as heart failure, atherosclerotic cardiovascular disease (ASCVD), valvular heart disease, pericardial disease, and arrhythmias. Cardiac computed tomography (CT) can be helpful in identifying both clinical and subclinical ASCVD in cancer patients and survivors. Radiation therapy treatment planning CT scans and cancer staging/re-staging imaging studies can quantify calcium scores which can identify pre-existing subclinical ASCVD. Cardiac CT can be helpful in the evaluation of cardiac tumors and pericardial diseases, especially in patients who cannot tolerate or have a contraindication to cardiac magnetic resonance. In this review, we describe the optimal utilization of cardiac CT in cancer patients, including risk assessment for ASCVD and identification of cancer treatment-related cardiovascular toxicity.

AI-Based Radiation Dose Quantification for Estimation of Heart Disease Risk in Breast Cancer Survivors After Radiation Therapy

PURPOSE

To investigate whether the dose planned for cardiac structures is associated with the risk of heart disease (HD) in patients with breast cancer treated with radiation therapy, and whether this association is modified by the presence of coronary artery calcification (CAC).

METHODS AND MATERIALS

Radiation therapy planning computed tomographic (CT) scans and corresponding dose distribution maps of 5561 patients were collected, 5300 patients remained after the exclusion of ineligible patients and duplicates; 1899 patients received their CT scan before 2011, allowing long follow-up. CAC was detected automatically. Using an artificial intelligence-based method, the cardiac structures (heart, cardiac chambers, large arteries, 3 main coronary arteries) were segmented. The planned radiation dose to each structure separately and to the whole heart were determined. Patients were assigned to a low-, medium-, or high-dose group based on the dose to the respective heart structure. Information on HD hospitalization and mortality was obtained for each patient. The association of planned radiation dose to cardiac structures with risk of HD was investigated in patients with and without CAC using Cox proportional hazard analysis in the long follow-up population. Tests for interaction were performed.

RESULTS

After a median follow-up of 96.0 months (interquartile range, 84.2-110.4 months) in the long follow-up group, 135 patients were hospitalized for HD or died of HD. If the dose to a structure increased 1 Gy, the relative HD risk increased by 3% to 11%. The absolute increase in HD risk was substantially higher in patients with CAC (event-rate_low-dose = 14-15 vs event-rate_high-dose = 15-34 per 1000 person-years) than in patients without CAC (event-rate_low-dose = 6-8 vs event-rate_high-dose = 5-17 per 1000 person-years). No interaction between CAC and radiation dose was found.

CONCLUSIONS

Radiation exposure of cardiac structures is associated with increased risk of HD. Automatic segmentation of cardiac structures enables spatially localized dose estimation, which can aid in the prevention of radiation therapy-induced cardiac damage. This could be especially valuable in patients with breast cancer and CAC.

Identification of Risk of Cardiovascular Disease by Automatic Quantification of Coronary Artery Calcifications on Radiotherapy Planning CT Scans in Patients With Breast Cancer

Importance

Cardiovascular disease (CVD) is common in patients treated for breast cancer, especially in patients treated with systemic treatment and radiotherapy and in those with preexisting CVD risk factors. Coronary artery calcium (CAC), a strong independent CVD risk factor, can be automatically quantified on radiotherapy planning computed tomography (CT) scans and may help identify patients at increased CVD risk.

Objective

To evaluate the association of CAC with CVD and coronary artery disease (CAD) in patients with breast cancer.

Design, Setting, and Participants

In this multicenter cohort study of 15 915 patients with breast cancer receiving radiotherapy between 2005 and 2016 who were followed until December 31, 2018, age, calendar year, and treatment-adjusted Cox proportional hazard models were used to evaluate the association of CAC with CVD and CAD.

Exposures

Overall CAC scores were automatically extracted from planning CT scans using a deep learning algorithm. Patients were classified into Agatston risk categories (0, 1-10, 11-100, 101-399, >400 units).

Main Outcomes and Measures

Occurrence of fatal and nonfatal CVD and CAD were obtained from national registries.

Results

Of the 15 915 participants included in this study, the mean (SD) age at CT scan was 59.0 (11.2; range, 22-95) years, and 15 879 (99.8%) were women. Seventy percent (n = 11 179) had no CAC. Coronary artery calcium scores of 1 to 10, 11 to 100, 101 to 400, and greater than 400 were present in 10.0% (n = 1584), 11.5% (n = 1825), 5.2% (n = 830), and 3.1% (n = 497) respectively. After a median follow-up of 51.2 months, CVD risks increased from 5.2% in patients with no CAC to 28.2% in patients with CAC scores higher than 400. After adjustment, CVD risk increased with higher CAC score (hazard ratio [HR]CAC = 1-10 = 1.1; 95% CI, 0.9-1.4; HRCAC = 11-100 = 1.8; 95% CI, 1.5-2.1; HRCAC = 101-400 = 2.1; 95% CI, 1.7-2.6; and HRCAC>400 = 3.4; 95% CI, 2.8-4.2). Coronary artery calcium was particularly strongly associated with CAD (HRCAC>400 = 7.8; 95% CI, 5.5-11.2). The association between CAC and CVD was strongest in patients treated with anthracyclines (HRCAC>400 = 5.8; 95% CI, 3.0-11.4) and patients who received a radiation boost (HRCAC>400 = 6.1; 95% CI, 3.8-9.7).

Conclusions and Relevance

This cohort study found that coronary artery calcium on breast cancer radiotherapy planning CT scan results was associated with CVD, especially CAD. Automated CAC scoring on radiotherapy planning CT scans may be used as a fast and low-cost tool to identify patients with breast cancer at increased risk of CVD, allowing implementing CVD risk-mitigating strategies with the aim to reduce the risk of CVD burden after breast cancer.

Trial Registration

ClinicalTrials.gov Identifier: NCT03206333.

Incidental Coronary Artery Calcium on Breast Radiation Therapy Planning Scans Identifies Patients for Cardiac Preventive Therapy

PURPOSE/OBJECTIVE(S)

The presence of coronary artery calcium (CAC>0) is associated with increased cardiac-related mortality and is a common indication to initiate statin therapy to prevent future long-term cardiac-related adverse events. CAC is also well visualized on noncontrast chest computed tomography simulation (CT sim) scans used for breast radiation planning. We hypothesize that by screening for incidental CAC on CT sims, radiation oncologists could help identify patients who may benefit from additional preventive medical interventions with their primary care physician or cardiologist.

METHODS

A retrospective analysis of 126 consecutive patients with breast cancer treated with external beam radiation therapy at a single institution was performed. Noncontrast CT sim scans were reviewed for the presence of CAC and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) was calculated to identify patients who may benefit from initiating statin therapy. Patients with CAC>0 and/or ASCVD risk >20% were identified as those who may benefit from statin therapy.

RESULTS

Out of 72 patients with CAC>0, only 12(16%) had reported pre-existing coronary artery disease and 32(44%) were not already on recommended statin therapy. CAC>0 visualized on CT sim was able to identify 29 additional patients who would benefit from statin beyond what the ASCVD risk calculator could identify.

CONCLUSION

Observation of incidental CAC on breast radiation-planning CT scans identified patients who could benefit from cardiac-related preventive strategies. By increasing attention, awareness, and reporting of incidental CAC visible on CT sims, radiation oncologists may fulfill a unique role to bridge a potential gap in cardiovascular preventive medicine.

Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.