Deep Learning-Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99mTc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

BACKGROUND: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. OBJECTIVES: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. METHODS: The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. RESULTS: In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. CONCLUSIONS: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Health care utilization and the associated costs attributable to cardiovascular disease in Ireland: a cross-sectional study.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality and disability globally. We examined healthcare service utilization and costs attributable to CVD in Ireland in the period before the introduction of a major healthcare reform in 2016. METHODS: Secondary analysis of data from 8 113 participants of the first wave of The Irish Longitudinal Study on Ageing. CVD was defined as having a self-reported doctor's diagnosis of myocardial infarction, angina, heart failure, stroke, atrial fibrillation or transient ischaemic attack. Participants self-reported the utilization of healthcare services in the year preceding the interview. Negative binomial regression with average marginal effects (AME) was used to estimate the incremental number of general practitioner (GP) and outpatient department (OPD) visits, accident and emergency department attendances and hospitalisations in population with CVD relative to population without CVD. We calculated the corresponding costs at individual and population levels, by gender and age groups. RESULTS: The prevalence of CVD was 18.2% (95% CI: 17.3, 19.0) Participants with CVD reported higher utilization of all healthcare services. In adjusted models, having CVD was associated with incremental 1.19 (95% CI: 0.99, 1.39) GP and 0.79 (95% CI: 0.65, 0.93) OPD visits. There were twice as many incremental hospitalisations in males with CVD compared to females with CVD (AME (95% CI): 0.20 (0.16, 0.23) vs 0.10 (0.07, 0.14)). The incremental cost of healthcare service use in population with CVD was an estimated €352.2 million (95% CI: €272.8, €431.7), 93% of which was due to use of secondary care services. CONCLUSION: We identified substantially increased use of healthcare services attributable to CVD in Ireland. Continued efforts aimed at CVD primary prevention and management are required.

How and why do financial incentives contribute to helping people stop smoking? A realist review.

BACKGROUND: Tobacco smoking remains a key cause of preventable illness and death globally. In response, many countries provide extensive services to help people to stop smoking by offering a variety of effective behavioural and pharmacological therapies. However, many people who wish to stop smoking do not have access to or use stop smoking supports, and new modes of support, including the use of financial incentives, are needed to address this issue. A realist review of published international literature was undertaken to understand how, why, for whom, and in which circumstances financial incentives contribute to success in stopping smoking for general population groups and among pregnant women. METHODS: Systematic searches were undertaken from inception to February 2022 of five academic databases: MEDLINE (ovid), Embase.com, CIHAHL, Scopus and PsycINFO. Study selection was inclusive of all study designs. Twenty-two studies were included. Using Pawson and Tilley's iterative realist review approach, data collected were screened, selected, coded, analysed, and synthesised into a set of explanatory theoretical findings. RESULTS: Data were synthesised into six Context-Mechanism-Outcome Configurations and one overarching programme theory after iterative rounds of analysis, team discussion, and expert panel feedback. Our programme theory shows that financial incentives are particularly useful to help people stop smoking if they have a financial need, are pregnant or recently post-partum, have a high threshold for behaviour change, and/or respond well to external rewards. The incentives work through a number of mechanisms including the role their direct monetary value can play in a person's life and through a process of reinforcement where they can help build confidence and self-esteem. CONCLUSION: This is the first realist review to synthesise how, why, and for whom financial incentives work among those attempting to stop smoking, adding to the existing evidence demonstrating their efficacy. The findings will support the implementation of current knowledge into effective programmes which can enhance the impact of stop smoking care. PROSPERO REGISTRATION NUMBER: CRD42022298941.

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

PURPOSE: The myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables. METHODS: Consecutive patients undergoing regadenoson rest-stress [82Rb]Cl PET-MPI were included. A newly developed 3D motion correction algorithm quantified myocardial creep, the maximum motion at stress during the first pass (60 s), in each direction. All-cause mortality (ACM) served as the primary endpoint. RESULTS: A total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5). CONCLUSIONS: Downward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Specialist cancer hospital-based smoking cessation service provision in Ireland.

BACKGROUND: While much progress has been made in reducing tobacco use in many countries, both active and passive smoking remain challenges. The benefits of smoking cessation are universally recognized, and the hospital setting is an ideal setting where smokers can access smoking cessation services as hospital admission can be a cue to action. Consistent delivery of good quality smoking cessation care across health services is an important focus for reducing the harm of tobacco use, especially among continued smokers. AIMS: Our objective was to document the smoking cessation medication and support services provided by specialist adult cancer hospitals across Ireland, a country with a stated tobacco endgame goal. METHODS: A cross-sectional survey based on recent national clinical guidelines was used to determine smoking cessation care delivery across eight specialist adult cancer tertiary referral university hospitals and one specialist radiotherapy center. Survey responses were collected using Qualtrics, a secure online survey software tool. The data was grouped, anonymized, and analyzed in Microsoft Excel. RESULTS: All responding hospitals demonstrated either some level of smoking cessation information or a service available to patients. However, there is substantial variation in the type and level of smoking cessation information offered, making access to smoking cessation services inconsistent and inequitable. CONCLUSION: The recently launched National Clinical Guideline for smoking cessation provides the template for all hospitals to ensure health services are in a position to contribute to Ireland's tobacco endgame goal.

Knowledge, attitudes and practices of Irish doctors regarding stop smoking care and electronic cigarettes.

BACKGROUND: Smoking continues to cause harm on a huge scale in Ireland. Doctors can help this harm through providing safe, effective and clinically sound stop smoking care, but the needs of Irish doctors in this area are largely uncharted. AIMS: We assessed the knowledge, attitudes and practices of Irish doctors regarding stop smoking care and electronic cigarettes. METHODS: An Internet-based cross-sectional survey was administered to members of the Royal College of Physicians in Ireland and the Irish College of General Practitioners. Descriptive statistics for key parameters were derived and factors associated with more consistent practice of brief intervention, a key component of stop smoking care, were analysed using chi-square testing. RESULTS: There were 250 responses (58.7% female, 53.0% aged under 45 years, 55.1% graduated in medicine before 2000 and 57.2% worked in general practice). Most (84.9%) reported often or always asking about patient's smoking behaviour, and most (86.1%) reported often or always advising patients to stop. However, providing or arranging effective stop smoking care was weak and less consistently practised, and while most (91.4%) saw it as a responsibility, few doctors (28.5%) agreed they were sufficiently trained in this area of clinical care. Confidence in the knowledge of e-cigarettes was poor. CONCLUSIONS: While there is a strong reservoir support and areas of good reported practice in stop smoking care among doctors in Ireland, the development of their knowledge and skills in arranging effective care should be supported if doctors are to fulfil their huge potential role in tackling the harm caused by smoking.

Automated Motion Correction for Myocardial Blood Flow Measurements and Diagnostic Performance of 82Rb PET Myocardial Perfusion Imaging.

Motion correction (MC) affects myocardial blood flow (MBF) measurements in 82Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). Methods: In total, 565 patients who underwent PET-MPI were considered. Patients without angiographic findings were split into training (n = 112) and validation (n = 112) groups. The automated MC algorithm used simplex iterative optimization of a count-based cost function and was developed using the training group. MBF measurements with automated MC were compared with those with manual MC in the validation group. In a separate cohort, 341 patients who underwent PET-MPI and invasive coronary angiography were enrolled in the angiographic group. The predictive performance in patients with significant CAD (≥70% stenosis) was compared between MBF measurements with and without automated MC. Results: In the validation group (n = 112), MBF measurements with automated and manual MC showed strong correlations (r = 0.98 for stress MBF and r = 0.99 for rest MBF). The automatic MC took less time than the manual MC (<12 s vs. 10 min per case). In the angiographic group (n = 341), MBF measurements with automated MC decreased significantly compared with those without (stress MBF, 2.16 vs. 2.26 mL/g/min; rest MBF, 1.12 vs. 1.14 mL/g/min; MFR, 2.02 vs. 2.10; all P < 0.05). The area under the curve (AUC) for the detection of significant CAD by stress MBF with automated MC was higher than that without (AUC, 95% CI, 0.76 [0.71-0.80] vs. 0.73 [0.68-0.78]; P < 0.05). The addition of stress MBF with automated MC to the model with ischemic total perfusion deficit showed higher diagnostic performance for detection of significant CAD (AUC, 95% CI, 0.82 [0.77-0.86] vs. 0.78 [0.74-0.83]; P = 0.022), but the addition of stress MBF without MC to the model with ischemic total perfusion deficit did not reach significance (AUC, 95% CI, 0.81 [0.76-0.85] vs. 0.78 [0.74-0.83]; P = 0.067). Conclusion: Automated MC on 82Rb PET-MPI can be performed rapidly with excellent agreement with experienced operators. Stress MBF with automated MC showed significantly higher diagnostic performance than without MC.

COVID-19 incidence and outcome by affluence/deprivation across three pandemic waves in Ireland: A retrospective cohort study using routinely collected data.

BACKGROUND: Since the pandemic onset, deprivation has been seen as a significant determinant of COVID-19 incidence and mortality. This study explores outcomes of COVID-19 in the context of material deprivation across three pandemic waves in Ireland. METHODS: Between 1st March 2020 and 13th May 2021, 252,637 PCR-confirmed COVID-19 cases were notified in Ireland. Cases were notified to the national Computerised Infectious Disease Reporting (CIDR) system. Each case was geo-referenced and assigned a deprivation category according to the Haase-Pratschke (HP) Deprivation Index. Regression modelling examined three outcomes: admission to hospital; admission to an intensive care unit (ICU) and death. RESULTS: Deprivation increased the likelihood of contracting COVID-19 in all age groups and across all pandemic waves, except for the 20-39 age group. Deprivation, age, comorbidity and male gender carried increased risk of hospital admission. Deprivation was not a factor in predicting ICU admission or death, and diagnosis in wave 2 was associated with the lowest risk of all three outcomes. CONCLUSIONS: Our study suggests that COVID-19 spreads easily through all strata of society and particularly in the more deprived population; however this was not a consistent finding. Ireland is ethnically more homogenous than other countries reporting a larger deprivation gradient, and in such societies, structural racial differences may contribute more to poor COVID outcomes than elements of deprivation.

Social Connectedness and Smoking among Adolescents in Ireland: An Analysis of the Health Behaviour in Schoolchildren Study.

Continuing progress with preventing smoking initiation is a key to the tobacco endgame. Home- and school-based social networks shape the health behaviour of children and adolescents. This study described the relationship between social connectedness and smoking behaviour in school-aged children in Ireland. The 2014 Irish Health Behaviour in School-aged Children (HBSC) surveyed self-reported smoking status and measured perceptions of social connectedness and support with validated and reliable questions across a random stratified sample of 9623 schoolchildren (aged 10-19). Overall, 8% of school-aged children reported smoking, in the last 30 days 52% reported smoking daily, and prevalence increased with age (p < 0.001). Compared with schoolchildren who did not smoke, perceptions of social connectedness and perceptions of support at home, from peers, and at school were significantly poorer for schoolchildren who smoked across all measures examined (p < 0.001). The poorest rated measures were for school connectedness and teacher support for smokers. Policies and practices that build and support positive environments for schoolchildren must continue to be prioritised if progress on preventing smoking initiation is to be sustained.

Is the public ready for a tobacco-free Ireland? A national survey of public knowledge and attitudes to tobacco endgame in Ireland.

AIM: Ireland will not meet the tobacco endgame goal set in its 2013 Tobacco-Free Ireland (TFI) policy of reducing smoking prevalence to less than 5% by 2025. Public opinion on tobacco endgame, a key lever to realise this goal, is uncharted in Ireland. This study aimed to measure public knowledge and attitudes to tobacco endgame. METHODS: A telephone-administered cross-sectional survey of 1000 randomly dialled members of the general public was conducted in 2022. Prevalence of awareness, perceived achievability and support for the TFI goal and tobacco endgame measures was calculated and compared across tobacco product use status. Logistic regression identified factors independently associated with goal support. FINDINGS: Although TFI goal awareness was low (34.0%), support was high (74.6%), although most (60.2%) believed it achievable beyond 2025. Product-focused measures were popular while support for supply-focused measures was mixed: for example, 86.1% supported nicotine content reduction while 40.3% supported user licencing. Phasing out tobacco sales was highly supported (82.8%); for most, this was contingent on support for currently addicted users. TFI goal support was independently associated with female sex (adjusted odds ratio (aOR) 1.47, 95% CI 1.05 to 2.07), higher education (aOR 1.80, 95% CI 1.21 to 2.66) and non-tobacco product use (aOR 2.67, 95% CI 1.66 to 4.30). CONCLUSIONS: Despite low awareness, tobacco endgame support is strong in Ireland. Public appetite for radically reducing tobacco product appeal and availability combined with public views on endgame achievability subject to extended timelines should be used to re-invigorate tobacco endgame discussion and planning in countries at risk of failing to meet declared targets.

Public attitudes to implementing financial incentives in stopsmoking services in Ireland.

INTRODUCTION: Financial incentives improve stop-smoking service outcomes. Views on acceptability can influence implementation success. To inform implementation planning in Ireland, public attitudes on financial incentives to stop smoking were measured. METHODS: A cross-sectional telephone survey was administered to 1000 people in Ireland aged ≥15 years in 2022, sampled through random digit dialing. The questionnaire included items on support for financial incentives under different conditions. Prevalence of support was calculated with 95% Confidence Intervals (CIs) and multiple logistic regression identified associated factors using adjusted odds ratios (AORs) with 95% CIs. RESULTS: Almost half (47.0%, 95% CI: 43.9-50.1) of the participants supported at least one type of financial incentive to stop smoking, with support more prevalent for shopping vouchers (43.3%, 95% CI: 40.3-46.5) than cash payments (32.1%, 95% CI: 29.2-35.0). Support was similar for universal and income-restricted schemes. Of those who supported financial incentives, the majority (60.6%) believed the maximum amount given on proof of stopping smoking should be under €250 (median=100, range: 1-7000). Compared to their counterparts, those of lower education level (AOR=1.49; 95% CI: 1.10-2.03, p=0.010) and tobacco/e-cigarette users (AOR=1.43; 95% CI: 1.02-2.03, p=0.041) were significantly more likely to support either financial incentive type, as were younger people. CONCLUSIONS: While views on financial incentives to stop smoking in Ireland were mixed, the intervention is more acceptable in groups experiencing the heaviest burden of smoking-related harm and most capacity to benefit. Engagement and communication must be integral to planning for successful implementation to improve stop-smoking service outcomes.

Impact of the COVID-19 pandemic on cancer care in Ireland - Perspectives from a COVID-19 and Cancer Working Group.

Upon the COVID-19 pandemic onset in Ireland, cancer service disruptions occurred due to prioritisation of COVID-19 related care, redeployment of staff, initial pausing of screening, diagnostic, medical and surgical oncology procedures, staff shortages due to COVID-19 infection and impacts on the physical and mental health of cancer healthcare workers. This was coupled with reluctance among people with symptoms suspicious for cancer to attend for clinical evaluation, due to concerns of contracting the virus. This was further compounded by a cyber-attack on national health service IT systems on May 14th 2021. The Irish Cancer Society, a national cancer charity with a role in advocacy, research and patient supports, convened a multi-disciplinary stakeholder group (COVID-19 and Cancer Working Group) to reflect on and understand the impact of the pandemic on cancer patients and services in Ireland, and discuss potential mitigation strategies. Perspectives on experiences were gathered across domains including timeliness of data acquisition and its conversion into intelligence, and the resourcing of cancer care to address cancer service impacts. The group highlighted aspects for future research to understand the long-term pandemic impact on cancer outcomes, while also highlighting potential strategies to support cancer services, build resilience and address delayed diagnosis. Additional measures include the need for cancer workforce recruitment and retention, increased mental health supports for both patients and oncology professionals, improvements to public health messaging, a near real-time multimodal national cancer database, and robust digital and physical infrastructure to mitigate impacts of the current pandemic and future challenges to cancer care systems.

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.

BACKGROUND: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed. OBJECTIVES: The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups. METHODS: Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC). RESULTS: During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external). CONCLUSIONS: The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.

Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events.

BACKGROUND: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging. OBJECTIVES: The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans. METHODS: The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans. RESULTS: Automatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (-0.02; 95% CI: -0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19). CONCLUSIONS: DL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.