Citations

Background Immune checkpoint inhibitors (ICI) improve cancer outcomes but are associated with increased atherosclerotic cardiovascular risk. Opportunistic coronary artery calcium (CAC) assessment from routine non-gated computed tomography (CT) using artificial intelligence (AI) may enable rapid cardiovascular risk stratification in this population. We evaluated the association between AI-derived CAC (AI-CAC) scores and 1-year myocardial infarction (MI) and all-cause mortality in patients receiving ICI therapy. Methods We conducted a retrospective multicenter study across three Mayo Clinic sites including patients who underwent chest CT within 3 months prior to ICI initiation between 2011 and 2022. Patients with prior coronary revascularization were excluded. AI-CAC scores derived from non-gated CT scans were categorized as 0, 1–99, 100–399, and ≥400. Cox regression analyses assessed associations with 1-year MI and all-cause mortality. Results Among 1,873 patients (mean age 66 years; 43% women), lung cancer was most common (50.3%), and 91% had at least one cardiovascular risk factor. Higher CAC burden was associated with increased 1-year MI risk (overall p=0.0032). AI-CAC was not associated with all-cause mortality (p=0.79), likely reflecting high cancer-related mortality (50.7% at 1 year). Only 15% of patients were receiving statins despite one-third having CAC >100. Conclusions Opportunistic AI-CAC assessment from routine CT scans provides modest prognostic value for MI risk among patients receiving ICI therapy and may identify opportunities to optimize preventive cardiovascular care. Elevated CAC should prompt risk factor modification rather than influence ICI treatment decisions. Prospective studies are warranted to evaluate clinical implementation and outcomes.

A vendor-neutral Agatston score enabled consistent aortic valve calcium quantification across CT systems and improved identification of severe aortic stenosis in patients with discordant echocardiographic findings.

Introduction: This study aimed to develop and evaluate a deep learning (DL) deblurring algorithm for computed tomography (CT). Phantom testing assessed whether DL deblurring could improve CT image quality and coronary artery calcium (CAC) detectability. Materials and Methods: The algorithm consisted of 2 sequential convolutional neural networks (CNNs): one for denoising and one for deblurring. After deblurring, the estimated noise from the first model was reintroduced to preserve image texture. The CNN denoising model was trained to map high-noise images to routine low-noise ones. The CNN deblurring model was trained using natural images—blurred inputs paired with sharp targets. To assess performance, an anthropomorphic phantom with 100 small calcifications was scanned on a CT system using a CAC scoring protocol. Data were acquired at clinical and high doses, then reconstructed with and without DL deblurring, using 25% of the maximum denoising strength. Detectability was defined as the ability to calculate an Agatston score (≥3 adjacent voxels >130 HU). Results: At high dose, CAC detectability increased from 39% (standard reconstruction) to 48% with deblurring. Similar detectability (39%) was achieved at the routine dose with deblurring, representing a 6% improvement compared with the original routine dose data. Discussion: Quantitative results from phantom testing demonstrate that DL deblurring can improve image quality, based on a clinically relevant, task-based CAC detection metric at clinical dose levels.

Thoracic aortic aneurysm (TAA) is a relatively common, under-recognised and lethal condition. Yet, current management remains anchored on absolute diameter for prophylactic repair, despite many dissections occurring below surgical thresholds—particularly in women. This review synthesises evidence showing that while rigorous standardisation of imaging is foundational, diameter alone is insufficient for accurate risk prediction. We appraise clinically available biomarkers beyond size: indexed metrics (Aortic Size Index, Aortic Height Index, Indexed Aortic Area) that adjust for body habitus—especially relevant in women; morphological descriptors (ascending length, arch tortuosity, aortic volume) that capture geometry; dynamic and biomechanical measures (growth rate, regional deformation, distensibility/stiffness) that reflect disease activity and advanced modalities (four-dimensional flow MRI, vascular deformation mapping) that quantify haemodynamics such as flow displacement and wall shear stress. Functional aortic markers—including central blood pressure, aortic stiffness, pulsatile arterial load and arterial age—independently predict expansion and improve risk stratification when combined with diameter. Furthermore, recognition of sex differences in TAA growth and outcomes supports sex-informed, precision aortic imaging and functional approaches to risk assessment. Future priorities include prospective multicentre validation, actionable sex-specific thresholds and multidisciplinary collaboration to translate these tools into personalised surveillance and timely intervention.

Abstract Background Myocardial feature-tracking (FT) strain from cardiovascular magnetic resonance (CMR) has incremental prognostic value beyond traditional imaging biomarkers. Clinical use is limited by the absence of population-specific healthy reference ranges. This study provides age-, sex-, and ethnicity-specific reference ranges for CMR-derived FT strain from the Healthy Hearts Consortium (HHC). Methods The HHC is an international collaboration comprising CMR scans from 9,088 verified healthy adults across six centres, covering the entire adult age spectrum and with diverse ethnic background. Standardised CMR acquisition protocols were used. Image analysis was performed by two experienced readers following pre-specified standard operating procedures and using two commercially available software platforms (cvi42®, Circle Cardiovascular Imaging Inc.; suiteHEART®, Neosoft LLC). The following metrics were extracted: i) global FT strain metrics from both ventricles (longitudinal, circumferential, and radial) and both atria (longitudinal); ii) strain rate; iii) Mitral/Tricuspid annular plane systolic excursion (MAPSE, TAPSE); iv) rapid strain (long-axis, junctional strain) from the left ventricle and both atria. Quality control included visual inspection of all images and the removal of biologically non-sensical values and extreme statistical outliers. Reference ranges were calculated by age group (deciles: 20-80 years), sex (male/female), and ethnicity (White, Black, Chinese, South Asian, Mixed/Other) using the 95% prediction interval method. Technical variations due to scanner vendor, software platform, and field strength (1.5T vs. 3.0T), were analysed. Results The study included 4,452 men (49%) and 4,636 women (51%). Ages ranged from 18 to 83 (mean 61±13) years. The sample included diverse ethnic backgrounds [White (81.7%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.7%), and Chinese (3.7%)]. Women demonstrated significantly higher absolute strain values than men across. Absolute myocardial strain values were higher in younger age groups and lower in older age groups, most notably in left global circumferential and longitudinal strain. Ethnic differences in strain were most pronounced between White and Black as well as between White and Chinese individuals regarding left and right ventricular strain values, while left and right atrial strain values as well as rapid strain values, were similar across ethnic groups. Conclusion This study provides comprehensive, population-specific reference ranges for FT-CMR-derived myocardial strain, addressing a clinical knowledge gap. These findings inform clinical interpretation, improving diagnostic accuracy and risk stratification in different populations. This work represents a significant step towards a broader clinical implementation of myocardial strain analysis in routine cardiovascular care, ensuring more accurate classification of findings across varying demographic groups.

Abstract Introduction Cardiovascular magnetic resonance (CMR) imaging enables precise, non-invasive assessment of cardiac structure and function, yet the clinical utility of CMR metrics is limited by the lack of comprehensive, population-specific reference values. By directly reanalyzing data from a large, multi-ethnic cohort rather than relying solely on pooled literature, could enable the establishment of standardized reference ranges and severity grading thresholds. Aim To provide comprehensive, age-, sex-, and ethnicity-stratified CMR reference ranges and severity grading thresholds for key ventricular and atrial parameters. Methods We analyzed over 9,000 CMR scans from the Healthy Hearts Consortium (HHC), comprising six international cohorts. All individuals were free from overt cardiovascular disease and major comorbidities, ensuring a verified healthy sample. Two automated segmentation software solutions—cvi42 (Circle Cardiovascular Imaging) and suiteHEART (Neosoft)—were used to quantify left and right ventricular volumes, myocardial mass, and atrial volumes in standard cine stacks. All endorsed segmentation modes were applied. Quality control protocol combined expert visual review and statistical outlier analysis. We derived 95% prediction intervals (PI) to define the normal range, with additional severity thresholds for mild (95–99.73% PI), moderate (99.73–99.99% PI), and severe (>99.99% PI) deviation from the reference range. Results The final dataset comprised of 9,059 verified healthy adults aged 18–83 years (mean 61 ± 13 years), with a near-equal sex distribution (51% women). Demographics included White (81.6%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.8%), and Chinese (3.7%) participants. Results demonstrated that sex, age, and ethnicity strongly influenced cardiac indices. Men had higher indexed LV and RV volumes and greater myocardial mass than women. Black participants presented the highest indexed LV myocardial mass across age groups, whereas Chinese participants had comparatively lower myocardial mass values (Figure 1). In contrast, atrial volumes remained relatively stable across age categories. Comparative analysis showed minor differences between the two software tools, particularly for atrial parameters; however, derived ejection fractions remained consistent. We established age-, sex-, and ethnicity-stratified reference ranges for key CMR parameters. To facilitate clinical interpretation, we defined severity thresholds corresponding to mild, moderate, and severe abnormalities. Conclusion This large-scale, international effort addresses prior limitations in CMR interpretation by offering robust, contemporary reference ranges and severity grading applicable to diverse populations and imaging protocols. Our severity grading thresholds support consistency in clinical reporting and decision-making, enabling clearer identification of early subclinical changes and more accurate monitoring of disease progression.Subset of CMR value ranges in females

Abstract Background Mitral valve prolapse (MVP) is the most common valvular abnormality in the population. Emerging evidence suggests that MVP is associated with left ventricular (LV) remodelling and dysfunction regardless of mitral regurgitation (MR) severity mimicking an early stage of dilated cardiomyopathy. However, prior studies have provided scattered results and were limited by small sample size and poor adjustment for confounders. Purpose To assess early cardiac chamber remodelling associated with MVP in absence of significant MR. Methods This multicentre, retrospective case-control study included patients with isolated MVP without significant MR undergoing CMR including late gadolinium enhancement (LGE). Healthy controls (HC) were selected from local databases and included subjects without cardiovascular pathology or significant comorbidities, with normal CMR parameters defined as comprised within normality ranges reported in the literature. Propensity-score-matching (PSM) was performed in a 1:1 nearest neighbour without replacement method (calliper 0.1), based on age, sex, body-surface-area, hypertension, and diabetes. The impact of MVP on cardiac remodelling was quantified using absolute standardized mean differences (SMDs). Results A total of 587 MVP patients and 583 HC were included, with 517 pairs successfully matched after PSM. Compared to HC, MVP patients showed greater left atrial (LA) area (12.2 [10.5 – 14.1] vs. 11.0 [9.5 – 12.4] cm²/m²), left ventricular (LV) end-diastolic-volume (86.3 [72.4–101.4] vs. 76.3 [66.1–86.0] ml/m²), and lower LV ejection-fraction (60.0% [55.2–65.6] vs. 61.0% [58.0–66.0], all p<0.001). MVP patients also showed greater right atrial area, right ventricular end-diastolic-volume, and higher right ventricular ejection-fraction compared to HC (Figure 1). The greatest impact of MVP on remodelling was observed for LV-end-diastolic volume and left atrial size (SMD ~0.55–0.62, respectively), (Figure 2). Among patients with MVP, remodelling was most pronounced in patients with bi-leaflet MVP and LGE. Conclusions In the absence of significant MR, MVP is associated with cardiac chambers’ remodelling with a particular emphasis on LV and LA dilatation.Cardiac chamber parameters in HC vs. MVP Effect size of MVP on cardiac remodeling

Abstract Background Myocardial strain derived from cardiovascular magnetic resonance (CMR) using feature tracking (FT) offers incremental prognostic value beyond traditional imaging markers. However, its broader clinical application is hindered by the lack of population-specific reference ranges. This study establishes age-, sex-, and ethnicity-specific normative data for FT-CMR-derived myocardial strain metrics from the Healthy Hearts Consortium (HHC). Methods The HHC is a large, international, multicohort initiative comprising CMR data from 9,088 rigorously screened healthy adults across six centers. The cohort spans the full adult age range and includes greater ethnic diversity than any comparable dataset. CMR scans were acquired using standardized protocols. Image analysis followed pre-specified standard operating procedures and was performed by two experienced readers using two commercial platforms (CVI42®, Circle Cardiovascular Imaging; suiteHEART®, Neosoft). Metrics extracted included: (i) global longitudinal, circumferential, and radial FT strain for both ventricles and atria; (ii) strain rate; (iii) annular plane systolic excursion (MAPSE, TAPSE); and (iv) rapid strain metrics (long-axis, junctional strain) for the left ventricle and atria. Comprehensive quality control involved visual review and exclusion of biologically implausible or extreme outlier values. Reference ranges were calculated using the 95% prediction interval method, stratified by age deciles (20–80 years), sex, and ethnicity (White, Black, Chinese, South Asian, Mixed/Other). The impact of technical factors—scanner vendor, field strength (1.5T vs 3.0T), and software platform—was also assessed. Results The study included 4,452 men (49%) and 4,636 women (51%), aged 18 to 83 years (mean 61 ± 13). The cohort was ethnically diverse: White (81.7%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.7%), and Chinese (3.7%). Women consistently exhibited higher absolute strain values than men. A clear age-related decline in strain was observed, particularly in left ventricular global circumferential and longitudinal strain. Ethnic differences were most pronounced between White and Black, and White and Chinese individuals for RV and LV strain metrics; in contrast, RA, LA, and rapid strain metrics showed minimal ethnic variation. Conclusion This study delivers the most comprehensive age-, sex-, and ethnicity-specific reference ranges to date for FT-CMR-derived myocardial strain. These normative values support improved clinical interpretation, aiding diagnosis and risk stratification in diverse populations. The findings mark a pivotal advancement towards the routine integration of myocardial strain assessment in cardiovascular imaging practice.

Foundation models can streamline imaging workflows, improve diagnostic accuracy, enable personalized treatment, and, via integrated generative artificial intelligence, create synthetic images to augment scarce real-world data; however, successful adoption requires explainability, trust, and workflow integration.

Abstract Background The association of body composition with checkpoint inhibitor outcomes in melanoma is a matter of ongoing debate. In this study, we aim to investigate body mass index (BMI) alongside computed tomography (CT)-derived body composition metrics in the largest cohort to date. Methods Patients treated with first-line anti-PD1 ± anti-CTLA4 for advanced melanoma were retrospectively identified from 11 melanoma centers in The Netherlands. From baseline CT scans, 5 body composition metrics were extracted: subcutaneous adipose tissue index, visceral adipose tissue index, skeletal muscle index, density, and gauge. These metrics were correlated in univariable and multivariable Cox proportional hazards analysis with progression-free survival, overall survival, and melanoma-specific survival (PFS, OS, and MSS). Results A total of 1471 eligible patients were included. Median PFS and OS were 9.1 and 38.1 months, respectively. Worse PFS was observed in underweight patients (multivariable hazard ratio [HR] = 1.86, 95% CI = 1.14 to 3.06). Furthermore, prolonged OS was observed in patients with higher skeletal muscle density (multivariable HR = 0.88, 95% CI = 0.81 to 0.97) and gauge (multivariable HR = 0.61, 95% CI = 0.82 to 0.998), whereas higher visceral adipose tissue index was associated with worse OS (multivariable HR = 1.12, 95% CI = 1.04 to 1.22). No association with survival outcomes was found for overweight, obesity, or subcutaneous adipose tissue. Conclusion Our findings suggest that underweight BMI is associated with worse PFS, whereas higher skeletal muscle density and lower visceral adipose tissue index were associated with improved OS. These associations were independent of known prognostic factors, including sex, age, performance status, and extent of disease. No significant association between higher BMI and survival outcomes was observed.