A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images

Cardiac preservation using ex vivo organ perfusion: new therapies for the treatment of heart failure by harnessing the power of growth factors using BMP mimetics like THR-184

As heart transplantation continues to be the gold standard therapy for end-stage heart failure, the imbalance between the supply of hearts, and the demand for them, continues to get worse. In the US alone, with less than 4,000 hearts suitable for transplant and over 100,000 potential recipients, this therapy is only available to a very few. The use of hearts Donated after Circulatory Death (DCD) and Donation after Brain Death (DBD) using ex vivo machine perfusion (EVMP) is a promising approach that has already increased the availability of suitable organs for heart transplantation. EVMP offers the promise of enabling the expansion of the overall number of heart transplants and lower rates of early graft dysfunction. These are realized through (1) safe extension of the time between procurement and transplantation and (2) ex vivo assessment of preserved hearts. Notably, ex vivo perfusion has facilitated the donation of DCD hearts and improved the success of transplantation. Nevertheless, DCD hearts suffer from serious preharvest ischemia/reperfusion injury (IRI). Despite these developments, only 40% of hearts offered for transplantation can be utilized. These devices do offer an opportunity to evaluate donor hearts for transplantation, resuscitate organs previously deemed unsuitable for transplantation, and provide a platform for the development of novel therapeutics to limit cardiac injury. Bone Morphogenetic Protein (BMP) signaling is a new target which holds the potential for ameliorating myocardial IRI. Recent studies have demonstrated that BMP signaling has a significant role in blocking the deleterious effects of injury to the heart. We have designed novel small peptide BMP mimetics that act via activin receptor-like kinase (ALK3), a type I BMP receptor. They are capable of (1) inhibiting inflammation and apoptosis, (2) blocking/reversing the epithelial-mesenchymal transition (EMT) and fibrosis, and (3) promoting tissue regeneration. In this review, we explore the promise that novel therapeutics, including these BMP mimetics, offer for the protection of hearts against myocardial injury during ex vivo transportation for cardiac transplantation. This protection represents a significant advance and a promising ex vivo therapeutic approach to expanding the donor pool by increasing the number of transplantable hearts.

Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence

In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI-driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluations and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions. Coronary artery calcium score (CACS) and CT angiography (CTA) provide high-resolution images that facilitate the detailed anatomical evaluation of coronary plaque burden. New tools such as myocardial CT perfusion (CTP) and fractional flow reserve (FFRCT) have been developed to add a functional evaluation of the stenosis. Moreover, epicardial adipose tissue (EAT) is gaining interest as its role in coronary artery plaque development has been deepened. Seen the great added value of these tools, the demand for new exams has increased such as the burden on imagers. Due to its ability to fast compute multiple data, AI can be helpful in both the acquisition and post-processing phases. AI can possibly reduce radiation dose, increase image quality, and shorten image analysis time. Moreover, different types of data can be used for risk assessment and patient risk stratification. Recently, the focus of the scientific community on AI has led to numerous studies, especially on CACS and CTA. This narrative review concentrates on AI's role in the post-processing of CACS, CTA, FFRCT, CTP, and EAT, discussing both current capabilities and future directions in the field of cardiac imaging.

Predicting Left Ventricular Adverse Remodeling After Transcatheter Aortic Valve Replacement: A Radiomics Approach

RATIONALE AND OBJECTIVES

To develop a radiomics model based on cardiac computed tomography (CT) for predicting left ventricular adverse remodeling (LVAR) in patients with severe aortic stenosis (AS) who underwent transcatheter aortic valve replacement (TAVR).

MATERIALS AND METHODS

Patients with severe AS who underwent TAVR from January 2019 to December 2022 were recruited. The cohort was divided into adverse remodeling group and non-adverse remodeling group based on LVAR occurrence, and further randomly divided into a training set and a validation set at an 8:2 ratio. Left ventricular radiomics features were extracted from cardiac CT. The least absolute shrinkage and selection operator regression was utilized to select the most relevant radiomics features and clinical features. The radiomics features were used to construct the Radscore, which was then combined with the selected clinical features to build a nomogram. The predictive performance of the models was evaluated using the area under the curve (AUC), while the clinical value of the models was assessed using calibration curves and decision curve analysis.

RESULTS

A total of 273 patients were finally enrolled, including 71 with adverse remodeling and 202 with non-adverse remodeling. 12 radiomics features and five clinical features were extracted to construct the radiomics model, clinical model, and nomogram, respectively. The radiomics model outperformed the clinical model (training AUC: 0.799 vs. 0.760; validation AUC: 0.766 vs. 0.755). The nomogram showed highest accuracy (training AUC: 0.859, validation AUC: 0.837) and was deemed most clinically valuable by decision curve analysis.

CONCLUSION

The cardiac CT-based radiomics features could predict LVAR after TAVR in patients with severe AS.

Computer-Assisted Algorithm for Quantification of Fibrosis by Native Cardiac CT: A Pilot Study

Background/Objectives: Recent advances in artificial intelligence, particularly in cardiac imaging, can potentially enhance patients' diagnosis and prognosis and identify novel imaging markers. We propose an automated, computer-aided algorithm utilizing native cardiac computed tomography (CT) imaging to identify myocardial fibrosis. This study aims to evaluate its performance compared to CMR markers of fibrosis in a cohort of patients diagnosed with breast cancer. Methods: The study included patients diagnosed with early HER2+ breast cancer, who presented LV dysfunction (LVEF < 50%) and myocardial fibrosis detected on CMR at the time of diagnosis. The patients were also evaluated by cardiac CT, and the extracted images were processed for the implementation of the automatic, computer-assisted algorithm, which marked as fibrosis every pixel that fell within the range of 60-90 HU. The percentage of pixels with fibrosis was subsequently compared with CMR parameters. Results: A total of eight patients (n = 8) were included in the study. High positive correlations between the algorithm's result and the ECV fraction (r = 0.59, p = 0.126) and native T1 (r = 0.6, p = 0.112) were observed, and a very high positive correlation with LGE of the LV(g) and the LV-LGE/LV mass percentage (r = 0.77, p = 0.025; r = 0.81, p = 0.015). A very high negative correlation was found with GLS (r = -0.77, p = 0.026). The algorithm presented an intraclass correlation coefficient of 1 (95% CI 0.99-1), p < 0.001. Conclusions: The present pilot study proposes a novel promising imaging marker for myocardial fibrosis, generated by an automatic algorithm based on native cardiac CT images.

Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy

Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches.

Multimodality Imaging in Advanced Heart Failure for Diagnosis, Management and Follow-Up: A Comprehensive Review

Advanced heart failure (AHF) presents a complex landscape with challenges spanning diagnosis, management, and patient outcomes. In response, the integration of multimodality imaging techniques has emerged as a pivotal approach. This comprehensive review delves into the profound significance of these imaging strategies within AHF scenarios. Multimodality imaging, encompassing echocardiography, cardiac magnetic resonance imaging (CMR), nuclear imaging and cardiac computed tomography (CCT), stands as a cornerstone in the care of patients with both short- and long-term mechanical support devices. These techniques facilitate precise device selection, placement, and vigilant monitoring, ensuring patient safety and optimal device functionality. In the context of orthotopic cardiac transplant (OTC), the role of multimodality imaging remains indispensable. Echocardiography offers invaluable insights into allograft function and potential complications. Advanced methods, like speckle tracking echocardiography (STE), empower the detection of acute cell rejection. Nuclear imaging, CMR and CCT further enhance diagnostic precision, especially concerning allograft rejection and cardiac allograft vasculopathy. This comprehensive imaging approach goes beyond diagnosis, shaping treatment strategies and risk assessment. By harmonizing diverse imaging modalities, clinicians gain a panoramic understanding of each patient's unique condition, facilitating well-informed decisions. The aim is to highlight the novelty and unique aspects of recently published papers in the field. Thus, this review underscores the irreplaceable role of multimodality imaging in elevating patient outcomes, refining treatment precision, and propelling advancements in the evolving landscape of advanced heart failure management.