Prognostic value of heart valve calcifications for cardiovascular events in a lung cancer screening population

To assess the prognostic value of aortic valve and mitral valve/annulus calcifications for cardiovascular events in heavily smoking men without a history of cardiovascular disease. Heavily smoking men without a cardiovascular disease history who underwent non-contrast-enhanced low-radiation-dose chest CT for lung cancer screening were included. Non-imaging predictors (age, smoking status and pack-years) were collected and imaging-predictors (calcium volume of the coronary arteries, aorta, aortic valve and mitral valve/annulus) were obtained. The outcome was the occurrence of cardiovascular events. Multivariable Cox proportional-hazards regression was used to calculate hazard-ratios (HRs) with 95 % confidence interval (CI). Subsequently, concordance-statistics were calculated. In total 3111 individuals were included, of whom 186 (6.0 %) developed a cardiovascular event during a follow-up of 2.9 (Q1-Q3, 2.7-3.3) years. If aortic (n = 657) or mitral (n = 85) annulus/valve calcifications were present, cardiovascular event incidence increased to 9.0 % (n = 59) or 12.9 % (n = 11), respectively. HRs of aortic and mitral valve/annulus calcium volume for cardiovascular events were 1.46 (95 % CI, 1.09-1.84) and 2.74 (95 % CI, 0.92-4.56) per 500 mm(3). The c-statistic of a basic model including age, pack-years, current smoking status, coronary and aorta calcium volume was 0.68 (95 % CI, 0.63-0.72), which did not change after adding heart valve calcium volume. Aortic valve calcifications are predictors of future cardiovascular events. However, there was no added prognostic value beyond age, number of pack-years, current smoking status, coronary and aorta calcium volume for short term cardiovascular events

Automated coronary artery calcification scoring in non-gated chest CT: Agreement and reliability

Objective: To determine the agreement and reliability of fully automated coronary artery calcium (CAC) scoring in a lung cancer screening population. Materials and Methods: 1793 low-dose chest CT scans were analyzed (non-contrast-enhanced, non-gated). To establish the reference standard for CAC, first automated calcium scoring was performed using a preliminary version of a method employing coronary calcium atlas and machine learning approach. Thereafter, each scan was inspected by one of four trained raters. When needed, the raters corrected initially automaticity-identified results. In addition, an independent observer subsequently inspected manually corrected results and discarded scans with gross segmentation errors. Subsequently, fully automatic coronary calcium scoring was performed. Agatston score, CAC volume and number of calcifications were computed. Agreement was determined by calculating proportion of agreement and examining Bland-Altman plots. Reliability was determined by calculating linearly weighted kappa (κ) for Agatston strata and intraclass correlation coefficient (ICC) for continuous values. Results: 44 (2.5%) scans were excluded due to metal artifacts or gross segmentation errors. In the remaining 1749 scans, median Agatston score was 39.6 (P25-P75:0-345.9), median volume score was 60.4 mm3 (P25-P75:0-361.4) and median number of calcifications was 2 (P25-P75:0-4) for the automated scores. The k demonstrated very good reliability (0.85) for Agatston risk categories between the automated and reference scores. The Bland-Altman plots showed underestimation of calcium score values by automated quantification. Median difference was 2.5 (p25-p75:0.0-53.2) for Agatston score, 7.6 (p25-p75:0.0-94.4) for CAC volume and 1 (p25-p75:0-5) for number of calcifications. The ICC was very good for Agatston score (0.90), very good for calcium volume (0.88) and good for number of calcifications (0.64). Discussion: Fully automated coron

The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from ⁸²Rb-PET/CT myocardial perfusion imaging

Background: Assessment of both coronary artery calcium(CAC) scores and myocardial perfusion imaging(MPI) in patients suspected of coronary artery disease(CAD) provides incremental prognostic information. We used an automated method to determine CAC scores on low-dose attenuation correction CT(LDACT) images gathered during MPI in one single assessment. The prognostic value of this automated CAC score is unknown, we therefore investigated the association of this automated CAC scores and major adverse cardiovascular events(MACE) in a large chest-pain cohort. Method: We analyzed 747 symptomatic patients referred for 82RubidiumPET/CT, without a history of coronary revascularization. Ischemia was defined as a summed difference score≥2. We used a validated deep learning(DL) method to determine CAC scores. For survival analysis CAC scores were dichotomized as low(90 days after scanning) or nonfatal myocardial infarction. Cox proportional hazard analysis were performed to identify predictors of MACE. Results: During 4 years follow-up, 115 MACEs were observed. High CAC scores showed higher cumulative event rates, irrespective of ischemia (nonischemic: 25.8% vs 11.9% and ischemic: 57.6% vs 23.4%, P-values <0.001). Multivariable cox regression revealed both high CAC scores (HR 2.19 95%CI 1.43–3.35) and ischemia (HR 2.56 95%CI 1.71–3.35) as independent predictors of MACE. Addition of automated CAC scores showed a net reclassification improvement of 0.13(0.022–0.245). Conclusion: Automatically derived CAC scores determined during a single imaging session are independently associated with MACE. This validated DL method could improve risk stratification and subsequently lead to more personalized treatment in patients suspected of CAD

Automatic coronary artery calcium scoring on radiotherapy planning CT Scans of breast cancer patients: Reproducibility and association with traditional cardiovascular risk factors

Objectives Coronary artery calcium (CAC) is a strong and independent predictor of cardiovascular disease (CVD) risk. This study assesses reproducibility of automatic CAC scoring on radiotherapy planning computed tomography (CT) scans of breast cancer patients, and examines its association with traditional cardiovascular risk factors. Methods This study included 561 breast cancer patients undergoing radiotherapy between 2013 and 2015. CAC was automatically scored with an algorithm using supervised pattern recognition, expressed as Agatston scores and categorized into five categories (0, 1-10, 11-100, 101-400, >400). Reproducibility between automatic and manual expert scoring was assessed in 79 patients with automatically determined CAC above zero and 84 randomly selected patients without automatically determined CAC. Interscan reproducibility of automatic scoring was assessed in 294 patients having received two scans (82% on the same day). Association between CAC and CVD risk factors was assessed in 36 patients with CAC scores >100, 72 randomly selected patients with scores 1-100, and 72 randomly selected patients without CAC. Reliability was assessed with linearly weighted kappa and agreement with proportional agreement. Results 134 out of 561 (24%) patients had a CAC score above zero. Reliability of CVD risk categorization between automatic and manual scoring was 0.80 (95% Confidence Interval (CI): 0.74-0.87), and slightly higher for scans with breath-hold. Agreement was 0.79 (95% CI: 0.72-0.85). Interscan reliability was 0.61 (95% CI: 0.50-0.72) with an agreement of 0.84 (95% CI: 0.80-0.89). Ten out of 36 (27.8%) patients with CAC scores above 100 did not have other cardiovascular risk factors. Conclusions Automatic CAC scoring on radiotherapy planning CT scans is a reliable method to assess CVD risk based on Agatston scores. One in four breast cancer patients planned for radiotherapy have elevated CAC score. One in three patients with high CAC scores don't have other CVD risk factors and wouldn't have been identified as high risk

Prognostic value of heart valve calcifications for cardiovascular events in a lung cancer screening population

To assess the prognostic value of aortic valve and mitral valve/annulus calcifications for cardiovascular events in heavily smoking men without a history of cardiovascular disease. Heavily smoking men without a cardiovascular disease history who underwent non-contrast-enhanced low-radiation-dose chest CT for lung cancer screening were included. Non-imaging predictors (age, smoking status and pack-years) were collected and imaging-predictors (calcium volume of the coronary arteries, aorta, aortic valve and mitral valve/annulus) were obtained. The outcome was the occurrence of cardiovascular events. Multivariable Cox proportional-hazards regression was used to calculate hazard-ratios (HRs) with 95 % confidence interval (CI). Subsequently, concordance-statistics were calculated. In total 3111 individuals were included, of whom 186 (6.0 %) developed a cardiovascular event during a follow-up of 2.9 (Q1–Q3, 2.7–3.3) years. If aortic (n = 657) or mitral (n = 85) annulus/valve calcifications were present, cardiovascular event incidence increased to 9.0 % (n = 59) or 12.9 % (n = 11), respectively. HRs of aortic and mitral valve/annulus calcium volume for cardiovascular events were 1.46 (95 % CI, 1.09–1.84) and 2.74 (95 % CI, 0.92–4.56) per 500 mm3. The c-statistic of a basic model including age, pack-years, current smoking status, coronary and aorta calcium volume was 0.68 (95 % CI, 0.63–0.72), which did not change after adding heart valve calcium volume. Aortic valve calcifications are predictors of future cardiovascular events. However, there was no added prognostic value beyond age, number of pack-years, current smoking status, coronary and aorta calcium volume for short term cardiovascular events

Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium

Background Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy. Objectives To lay out the theoretical framework, purpose, and structure of a novel AI consortium. Methods We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach. Results A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application. Conclusion CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.</p

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

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

Introduction Cardiovascular disease (CVD) is an important cause of death in breast cancer survivors. Some breast cancer treatments including anthracyclines, trastuzumab and radiotherapy can increase the risk of CVD, especially for patients with pre-existing CVD risk factors. Early identification of patients at increased CVD risk may allow switching to less cardiotoxic treatments, active surveillance or treatment of CVD risk factors. One of the strongest independent CVD risk factors is the presence and extent of coronary artery calcifications (CAC). In clinical practice, CAC are generally quantified on ECGtriggered cardiac CT scans. Patients with breast cancer treated with radiotherapy routinely undergo radiotherapy planning CT scans of the chest, and those scans could provide the opportunity to routinely assess CAC before a potentially cardiotoxic treatment. The Bragatston study aims to investigate the association between calcifications in the coronary arteries, aorta and heart valves (hereinafter called ‘cardiovascular calcifications’) measured automatically on planning CT scans of patients with breast cancer and CVD risk. Methods and analysis In a first step, we will optimise and validate a deep learning algorithm for automated quantification of cardiovascular calcifications on planning CT scans of patients with breast cancer. Then, in a multicentre cohort study (University Medical Center Utrecht, Utrecht, Erasmus MC Cancer Institute, Rotterdam and Radboudumc, Nijmegen, The Netherlands), the association between cardiovascular calcifications measured on planning CT scans of patients with breast cancer (n≈16 000) and incident (non-)fatal CVD events will be evaluated. To assess the added predictive value of these calcifications over traditional CVD risk factors and treatment characteristics, a case-cohort analysis will be performed among all cohort members diagnosed with a CVD event during follow-up (n≈200) and a random sample of the baseline cohort (n≈600). Ethics and dissemination The Institutional Review Boards of the participating hospitals decided that the Medical R