Effect of Active Cancer on the Cardiac Phenotype: A Cardiac Magnetic Resonance Imaging-Based Study of Myocardial Tissue Health and Deformation in Patients With Chemotherapy-Naïve Cancer.

Background The overlap between cancer and cardiovascular care continues to expand, with intersections emerging before, during, and following cancer therapies. To date, emphasis has been placed on how cancer therapeutics influence downstream cardiac health. However, whether active malignancy itself influences chamber volumes, function, or overall myocardial tissue health remains uncertain. We sought to perform a comprehensive cardiovascular magnetic resonance-based evaluation of cardiac health in patients with chemotherapy-naïve cancer with comparison with a healthy volunteer population. Methods and Results Three-hundred and eighty-one patients with active breast cancer or lymphoma before cardiotoxic chemotherapy exposure were recruited in addition to 102 healthy volunteers. Both cohorts underwent standardized cardiovascular magnetic resonance imaging with quantification of chamber volumes, ejection fraction, and native myocardial T1. Left ventricular mechanics were incrementally assessed using three-dimensional myocardial deformation analysis, providing global longitudinal, circumferential, radial, and principal peak-systolic strain amplitude and systolic strain rate. The mean age of patients with cancer was 53.8±13.4 years; 79% being women. Despite similar left ventricular ejection fraction, patients with cancer showed smaller chambers, increased strain amplitude, and systolic strain rate in both conventional and principal directions, and elevated native T1 versus sex-matched healthy volunteers. Adjusting for age, sex, hypertension, and diabetes mellitus, the presence of cancer remained associated with these cardiovascular magnetic resonance parameters. Conclusions The presence of cancer is independently associated with alterations in cardiac chamber size, function, and objective markers of tissue health. Dedicated research is warranted to elucidate pathophysiologic mechanisms underlying these findings and to explore their relevance to the management of patients with cancer referred for cardiotoxic therapies.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy.

The diagnosis of cardiomyopathy states may benefit from machine-learning (ML) based approaches, particularly to distinguish those states with similar phenotypic characteristics. Three-dimensional myocardial deformation analysis (3D-MDA) has been validated to provide standardized descriptors of myocardial architecture and deformation, and may therefore offer appropriate features for the training of ML-based diagnostic tools. We aimed to assess the feasibility of automated disease diagnosis using a neural network trained using 3D-MDA to discriminate hypertrophic cardiomyopathy (HCM) from its mimic states: cardiac amyloidosis (CA), Anderson-Fabry disease (AFD), and hypertensive cardiomyopathy (HTNcm). 3D-MDA data from 163 patients (mean age 53.1 ± 14.8 years; 68 females) with left ventricular hypertrophy (LVH) of known etiology was provided. Source imaging data was from cardiac magnetic resonance (CMR). Clinical diagnoses were as follows: 85 HCM, 30 HTNcm, 30 AFD, and 18 CA. A fully-connected-layer feed-forward neural was trained to distinguish HCM vs. other mimic states. Diagnostic performance was compared to threshold-based assessments of volumetric and strain-based CMR markers, in addition to baseline clinical patient characteristics. Threshold-based measures provided modest performance, the greatest area under the curve (AUC) being 0.70. Global strain parameters exhibited reduced performance, with AUC under 0.64. A neural network trained exclusively from 3D-MDA data achieved an AUC of 0.94 (sensitivity 0.92, specificity 0.90) when performing the same task. This study demonstrates that ML-based diagnosis of cardiomyopathy states performed exclusively from 3D-MDA is feasible and can distinguish HCM from mimic disease states. These findings suggest strong potential for computer-assisted diagnosis in clinical practice.

A novel combined fluid dynamic and strain analysis approach identified abdominal aortic aneurysm rupture.

Clinical decision-making for surgical repair of abdominal aortic aneurysms based on maximum aortic diameter presents limitations as rupture can occur below threshold for some aneurysms, whereas others are stable at large sizes. The proposed approach combines hemodynamics and geometric indices with in vivo deformation analysis to assess local weakening of the aortic wall for a case of impending rupture that was confirmed during open surgical repair. A new combined index, the Regional Rupture Potential, is introduced to help the assessment of individual aneurysms and their rupture risk, providing a rationale for clinical decisions.

3D myocardial deformation analysis from cine MRI as a marker of amyloid protein burden in cardiac amyloidosis: validation versus T1 mapping.

Cardiac amyloidosis (CA) is a significant contributor to heart failure with preserved ejection fraction and is appreciating expanding therapeutic options. Non-invasive tools aimed at accurate identification and surveillance of therapeutic response are of immediate and expanding need. While native and post-contrast T1 mapping quantify expansion of the extra-cellular compartment from amyloid protein deposition, 3D strain analysis of non-contrast cine images offers unique advantages relevant to high prevalence of renal insufficiency in this population and reduced dependency on field strength, pulse sequence, and vendor implementation. We aimed to evaluate global and segmental associations between 3D strain and T1 mapping in patients with cardiac amyloidosis. Twenty consecutive patients with confirmed CA were recruited and underwent a standardized cardiovascular magnetic resonance imaging protocol at 3 T including using multi-planar cine imaging and T1 mapping using a shortened modified look-locker inversion recovery sequence. T1 mapping was performed pre- and (when permitted by renal function) post-contrast and measured for segmental T1 values. Spatially-matched 3D strain-based measures were similarly calculated. Mean left ventricular ejection fraction was 61 ± 21% (range 30-73%). Mean global native T1 was 1308 ± 96 ms. Post-contrast T1 and partition coefficient were 558 ± 104 ms and 0.85 ± 0.31, respectively. Global myocardial strain values were 8.1 ± 2.9% in the longitudinal direction, - 9.2 ± 3.4% in the circumferential direction, and 41.7 ± 22.8% in the maximum principal direction. Segmental analyses confirmed relative worsening in T1 values and reductions in strain values in the basal myocardial segments with relative sparing of the apical segments. Significant associations between T1 and strain-based measures were observed globally and segmentally, with the strongest associations found both globally and segmentally in the circumferential and minimum principal directions of deformation. This study identifies strong associations between 3D myocardial strain and T1-mapping based markers of regional amyloid protein deposition. These findings support expanded investigation of myocardial strain as a surrogate marker of response to novel therapeutic strategies in patients with cardiac amyloidosis.