Cardiotoxicity detection tool for breast cancer chemotherapy: a retrospective study

Background

Patients with breast cancer undergoing biological therapy and/or chemotherapy perform multiple radionuclide angiography (RNA) or multigated acquisition (MUGA) scans to assess cardiotoxicity. The association between RNA imaging parameters and left ventricular (LV) ejection fraction (LVEF) remains unclear.

Objectives

This study aimed to extract and evaluate the association of several novel imaging biomarkers to detect changes in LVEF in patients with breast cancer undergoing chemotherapy.

Methods

We developed and optimized a novel set of MATLAB routines called the "RNA Toolbox" to extract parameters from RNA images. The code was optimized using various statistical tests (e.g., ANOVA, Bland-Altman, and intraclass correlation tests). We quantitatively analyzed the images to determine the association between these parameters using regression models and receiver operating characteristic (ROC) curves.

Results

The code was reproducible and showed good agreement with validated clinical software for the parameters extracted from both packages. The regression model and ROC results were statistically significant in predicting LVEF (R2 = 0.40, P < 0.001) (AUC = 0.78). Some time-based, shape-based, and count-based parameters were significantly associated with post-chemotherapy LVEF (β = 0.09, P < 0.001), LVEF of phase image (β = 4, P = 0.030), approximate entropy (ApEn) (β = 11.6, P = 0.001), ApEn (diastolic and systolic) (β = 39, P = 0.002) and LV systole size (β = 0.03, P = 0.010).

Conclusions

Despite the limited sample size, we observed evidence of associations between several parameters and LVEF. We believe that these parameters will be more beneficial than the current methods for patients undergoing cardiotoxic chemotherapy. Moreover, this approach can aid physicians in evaluating subclinical cardiac changes during chemotherapy, and in understanding the potential benefits of cardioprotective drugs.

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.

Phase analysis of gated blood pool SPECT for multiple stress testing assessments of ventricular mechanical dyssynchrony in a tachycardia-induced dilated cardiomyopathy canine model

BACKGROUND

Stress-induced dyssynchrony has been shown to be independently correlated with clinical outcomes in patients with dilated cardiomyopathy (DCM) and narrow QRS complexes. However, the extent to which stress levels affect inter- and intraventricular dyssynchrony parameters remains unknown.

METHODS

Ten large dogs were submitted to tachycardia-induced DCM by pacing the right ventricular apex for 3-4 weeks to reach a target ejection fraction (EF) of 35% or less. Stress was then induced in DCM dogs by administering intravenous dobutamine up to a maximum of 20 μg·kg-1·min-1. Hemodynamic and ventricular dyssynchrony data were analyzed by left ventricular (LV) pressure measurements and gated blood pool SPECT (GBPS) imaging. In order to assess mechanical dyssynchrony in DCM subjects and compare it with that of 8 normal counterparts, we extracted the following data: count-based indices of LV contraction homogeneity index (CHI), entropy and phase standard deviation, and interventricular dyssynchrony index.

RESULTS

A significant LV intraventricular dyssynchrony (CHI: 96.4 ± 1.3% in control vs 78.6% ± 10.9% in DCM subjects) resulted in an intense LV dysfunction in DCM subjects (EF: 49.5% ± 8.4% in control vs 22.6% ± 6.0% in DCM), compared to control subjects. However, interventricular dyssynchrony did not vary significantly between the two groups. Under stress, DCM subjects showed a significant improvement in ventricular functional parameters at each level (EF: 22.6% ± 6.0% at rest vs 48.1% ± 5.8% at maximum stress). All intraventricular dyssynchrony indices showed a significant increase in magnitude of synchrony from baseline to stress levels of greater than or equal to 5 μg·kg-1·min-1 dobutamine. There were individual differences in the magnitude and pattern of change in interventricular dyssynchrony during the various levels of stress.

CONCLUSIONS

Based on GBPS analyses, different levels of functional stress, even in close intervals, can have a significant impact on hemodynamic and intraventricular dyssynchrony parameters in a DCM model with narrow QRS complex.

Radionuclide Assessment of Left Ventricular Dyssynchrony

Phase analysis of gated myocardial perfusion single-photon emission computed tomography is a widely available and reproducible measure of left ventricular (LV) dyssynchrony, which also provides comprehensive assessment of LV function, global and regional scar burden, and patterns of LV mechanical activation. Preliminary studies indicate potential use in predicting cardiac resynchronization therapy response and elucidation of mechanisms. Because advances in technology may expand capabilities for precise LV lead placement in the future, identification of specific patterns of dyssynchrony may have a critical role in guiding cardiac resynchronization therapy.