Identification of fibrosis in hypertrophic cardiomyopathy: a radiomic study on cardiac magnetic resonance cine imaging

ChatGPT as an effective tool for quality evaluation of radiomics research

OBJECTIVES

This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.

METHODS

Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.

RESULTS

In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840-0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756-0.919) and 0.914 (95% CI: 0.855-0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9-3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.

CONCLUSION

ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.

KEY POINTS

Question Is ChatGPT effective and reliable in evaluating radiomics research quality based on RQS? Findings ChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment. Clinical relevance ChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.

Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review

Background and Objective

Radiomics is an emerging technology that facilitates the quantitative analysis of multi-modal cardiac magnetic resonance imaging (MRI). This study aims to introduce a standardized workflow for applying radiomics to non-ischemic cardiomyopathies, enabling clinicians to comprehensively understand and implement this technology in clinical practice.

Methods

A computerized literature search (up to August 1, 2024) was conducted using PubMed to identify relevant studies on the roles and workflows of radiomics in non-ischemic cardiomyopathy. Expert discussions were also held to ensure the accuracy and relevance of the findings. Only English-language publications were reviewed.

Key Content and Findings

The paper details the essential processes of radiomics, including feature extraction, feature engineering, model construction, and data analysis. It emphasizes the role of MRI in assessing cardiac structure and function and demonstrates how MRI-based radiomics can aid in diagnosing and differentiating non-ischemic cardiomyopathies such as hypertrophic cardiomyopathy, dilated cardiomyopathy, and myocarditis. The study also investigates various cardiac MRI sequences to enhance the clinical application of radiomics.

Conclusions

The standardized radiomics workflow presented in this study aims to assist clinicians in effectively utilizing MRI-based radiomics for the diagnosis and management of non-ischemic cardiomyopathies, thereby improving clinical decision-making.

Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance

Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment. Most recent research has focused on segmentation of the left ventricular myocardium and blood pool. Although many algorithms have shown a level comparable to that of human experts, some problems, such as poor performance of basal and apical segmentation and false identification of myocardial structure, remain. Segmentation of myocardial fibrosis is another research hotspot, and most patient cohorts of such studies have hypertrophic cardiomyopathy. Whether the above methods are applicable to other patient groups requires further study. The use of automated CMR interpretation for the diagnosis and prognosis assessment of cardiovascular diseases demonstrates great clinical potential. However, prospective large-scale clinical trials are needed to investigate the real-word application of AI technology in clinical practice.

Machine learning and radiomics for ventricular tachyarrhythmia prediction in hypertrophic cardiomyopathy: insights from an MRI-based analysis

BACKGROUND

Myocardial fibrosis is often detected in patients with hypertrophic cardiomyopathy (HCM), which causes left ventricular (LV) dysfunction and tachyarrhythmias.

PURPOSE

To evaluate the potential value of a machine learning (ML) approach that uses radiomic features from late gadolinium enhancement (LGE) and cine images for the prediction of ventricular tachyarrhythmia (VT) in patients with HCM.

MATERIAL AND METHODS

Hyperenhancing areas of LV myocardium on LGE images were manually segmented, and the segmentation was propagated to corresponding areas on cine images. Radiomic features were extracted using the PyRadiomics library. The least absolute shrinkage and selection operator (LASSO) method was employed for radiomic feature selection. Our model development employed the TabPFN algorithm, an adapted Prior-Data Fitted Network design. Model performance was evaluated graphically and numerically over five-repeat fivefold cross-validation. SHapley Additive exPlanations (SHAP) were employed to determine the relative importance of selected radiomic features.

RESULTS

Our cohort consisted of 60 patients with HCM (73.3% male; median age = 51.5 years), among whom 17 had documented VT during the follow-up. A total of 1612 radiomic features were extracted for each patient. The LASSO algorithm led to a final selection of 18 radiomic features. The model achieved a mean area under the receiver operating characteristic curve of 0.877, demonstrating good discrimination, and a mean Brier score of 0.119, demonstrating good calibration.

CONCLUSION

Radiomics-based ML models are promising for predicting VT in patients with HCM during the follow-up period. Developing predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis.

[Clincial Research Progress in Using Magnetic Resonance Imaging to Assess Myocardial Fibrosis in Hypertrophic Cardiomyopathy]

Hypertrophic cardiomyopathy (HCM) is the most common type of primary cardiomyopathy that causes sudden cardiac death in adolescents and athletes. With over 1 million HCM patients, China has the largest population of HCM patients in the world, and the total number of cases is increasing year on year. Myocardial fibrosis is the most important histopathological characterization in HCM and is regarded as the primary cause of malignant ventricular arrhythmia, cardiac remodeling, and heart failure. At present, cardiac magnetic resonance imaging (MRI) serves as the gold-standard imaging modality for noninvasive evaluation of myocardial fibrosis. Several techniques, such as late gadolinium enhancement and T1 mapping, are showing considerable promise for potential applications. These techniques have emerged as viable imaging approaches to the elucidation of HCM tissue characterization. They are also helpful in predicting the long-term prognosis of patients. Herein, we summarized recent advances in using cardiac MRI to assess myocardial fibrosis in HCM from four perspectives, including late gadolinium enhancement, T1 mapping, T1ρ mapping, and MRI-based radiomics and machine learning models.

Next-Generation Cardiac Magnetic Resonance Imaging Techniques for Characterization of Myocardial Disease

Purpose of the Review

Many novel cardiac magnetic resonance imaging (cMR) techniques have been developed for diagnosis, risk stratification, and monitoring of myocardial disease. The field is changing rapidly with advances in imaging technology. The purpose of this review is to give an update on next-generation cMR techniques with promising developments for clinical translation in the last two years, and to outline clinical applications.

Recent Findings

There has been increasing widespread clinical adoption of T1/T2 mapping into standard of care clinical practice. Development of auto segmentation has enabled clinical integration, with potential applications to minimize the use of contrast. Advances in diffusion tensor imaging, multiparametric mapping with cardiac MRI fingerprinting, automated quantitative perfusion mapping, metabolic imaging, elastography, and 4D flow are advancing the ability of cMR to provide further quantitative characterization to enable deep myocardial disease phenotyping. Together these advanced imaging features further augment the ability of cMR to contribute to novel disease characterization and may provide an important platform for personalized medicine.

Summary

Next-generation cMR techniques provide unique quantitative imaging features that can enable the identification of imaging biomarkers that may further refine disease classification and risk prediction. However, widespread clinical application continues to be limited by ground truth validation, reproducibility of the techniques across vendor platforms, increased scan time, and lack of widespread availability of advanced cardiac MRI physicists and expert readers. However, these techniques show great promise in minimizing the need for invasive testing, may elucidate novel pathophysiology, and may provide the ability for more accurate diagnosis of myocardial disease.

Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy

RATIONALE AND OBJECTIVES

This study aims to evaluate the capability of machine learning algorithms in utilizing radiomic features extracted from cine-cardiac magnetic resonance (CMR) sequences for differentiating between ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM).

MATERIALS AND METHODS

This retrospective study included 115 cardiomyopathy patients subdivided into ICM (n = 64) and DCM cohorts (n = 51). We collected invasive clinical (IC), noninvasive clinical (NIC), and combined clinical (CC) feature subsets. Radiomic features were extracted from regions of interest (ROIs) in the left ventricle (LV), LV cavity (LVC), and myocardium (MYO). We tested 10 classical machine learning classifiers and validated them through fivefold cross-validation. We compared the efficacy of clinical feature-based models and radiomics-based models to identify the superior diagnostic approach.

RESULTS

In the validation set, the Gaussian naive Bayes (GNB) model outperformed the other models in all categories, with areas under the curve (AUCs) of 0.879 for IC_GNB, 0.906 for NIC_GNB, and 0.906 for CC_GNB. Among the radiomics models, the MYO_LASSOCV_MLP model demonstrated the highest AUC (0.919). In the test set, the MYO_RFECV_GNB radiomics model achieved the highest AUC (0.857), surpassing the performance of the three clinical feature models (IC_GNB: 0.732; NIC_GNB: 0.75; CC_GNB: 0.786).

CONCLUSION

Radiomics models leveraging MYO images from cine-CMR exhibit promising potential for differentiating ICM from DCM, indicating the significant clinical application scope of such models.

CLINICAL RELEVANCE STATEMENT

The integration of radiomics models and machine learning methods utilizing cine-CMR sequences enhances the diagnostic capability to distinguish between ICM and DCM, minimizes examination risks for patients, and potentially reduces the duration of medical imaging procedures.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease

BACKGROUND

The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension.

METHODS

132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity.

RESULTS

The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively.

CONCLUSIONS

Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

Cardiovascular Magnetic Resonance-Based Tissue Characterization in Patients With Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common hereditable cardiomyopathy that affects between 1:200 to 1:500 of the general population. The role of cardiovascular magnetic resonance (CMR) imaging in the management of HCM has expanded over the past 2 decades to become a key informant of risk in this patient population, delivering unique insights into tissue health and its influence on future outcomes. Numerous mature CMR-based techniques are clinically available for the interrogation of tissue health in patients with HCM, inclusive of contrast and noncontrast methods. Late gadolinium enhancement imaging remains a cornerstone technique for the identification and quantification of myocardial fibrosis with large cumulative evidence supporting value for the prediction of arrhythmic outcomes. T1 mapping delivers improved fidelity for fibrosis quantification through direct estimations of extracellular volume fraction but also offers potential for noncontrast surrogate assessments of tissue health. Water-sensitive imaging, inclusive of T2-weighted dark blood imaging and T2 mapping, have also shown preliminary potential for assisting in risk discrimination. Finally, emerging techniques, inclusive of innovative multiparametric methods, are expanding the utility of CMR to assist in the delivery of comprehensive tissue characterization toward the delivery of personalized HCM care. In this narrative review we summarize the contemporary landscape of CMR techniques aimed at characterizing tissue health in patients with HCM. The value of these respective techniques to identify patients at elevated risk of future cardiovascular outcomes are highlighted.

Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure

Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.

Cardiomyocyte hyperplasia and immaturity but not hypertrophy are characteristic features of patients with RASopathies

AIMS

RASopathies are caused by mutations in genes that alter the MAP kinase pathway and are marked by several malformations with cardiovascular disorders as the predominant cause of mortality. Mechanistic insights in the underlying pathogenesis in affected cardiac tissue are rare. The aim of the study was to assess the impact of RASopathy causing mutations on the human heart.

METHODS AND RESULTS

Using single cell approaches and histopathology we analyzed cardiac tissue from children with different RASopathy-associated mutations compared to age-matched dilated cardiomyopathy (DCM) and control hearts. The volume of cardiomyocytes was reduced in RASopathy conditions compared to controls and DCM patients, and the estimated number of cardiomyocytes per heart was ~4-10 times higher. Single nuclei RNA sequencing of a 13-year-old RASopathy patient (carrying a PTPN11 c.1528C > G mutation) revealed that myocardial cell composition and transcriptional patterns were similar to <1 year old DCM hearts. Additionally, immaturity of cardiomyocytes is shown by an increased MYH6/MYH7 expression ratio and reduced expression of genes associated with fatty acid metabolism. In the patient with the PTPN11 mutation activation of the MAP kinase pathway was not evident in cardiomyocytes, whereas increased phosphorylation of PDK1 and its downstream kinase Akt was detected.

CONCLUSION

In conclusion, an immature cardiomyocyte differentiation status appears to be preserved in juvenile RASopathy patients. The increased mass of the heart in such patients is due to an increase in cardiomyocyte number (hyperplasia) but not an enlargement of individual cardiomyocytes (hypertrophy).