Tensor-based Multimodal Learning for Predicting Pulmonary Arterial Wedge Pressure from Cardiac MRI

Conceitos essenciais

A tensor learning-based pipeline that integrates multimodal cardiac MRI and cardiac measurements to accurately predict Pulmonary Arterial Wedge Pressure, a key indicator of heart failure.

Resumo

The content describes a pipeline for predicting Pulmonary Arterial Wedge Pressure (PAWP) from cardiac Magnetic Resonance Imaging (MRI) data using tensor-based multimodal learning. The key highlights are:

Preprocessing:

Normalization of cardiac MRI scans
Automatic landmark detection with uncertainty quantification
Inter-subject registration and downsampling

Tensor Feature Learning:

Multilinear Principal Component Analysis (MPCA) to extract spatial and temporal features from cardiac MRI
Uncertainty-based binning to identify and remove poor-quality training samples

Multimodal Feature Integration:

Early and late fusion of features from short-axis, four-chamber, and cardiac measurements (left atrial volume, left ventricular mass)
Leveraging complementary information from multimodal data

Performance Evaluation:

Experiments on a large cohort of 1346 patients who underwent right heart catheterization
Significant improvement over the baseline in clinical metrics (AUC, accuracy, MCC)
Decision curve analysis confirms the clinical utility of the proposed method for screening high-risk patients

The proposed pipeline demonstrates the diagnostic value of tensor-based features and the benefits of multimodal integration for accurate prediction of PAWP, a key indicator of heart failure, from cardiac MRI data.

Estatísticas

Elevated Pulmonary Arterial Wedge Pressure (PAWP) is indicative of raised left ventricular filling pressure and reduced contractility of the heart.
PAWP can be measured by invasive and expensive Right Heart Catheterization (RHC), but simpler and non-invasive techniques could aid in better monitoring of heart failure patients.
The study included 1346 patients, of which 940 had normal PAWP (≤15 mmHg) and 406 had elevated PAWP (> 15 mmHg).
The proposed pipeline achieved significant improvements over the baseline in clinical metrics: ∆AUC = 0.1027, ∆Accuracy = 0.0628, and ∆MCC = 0.3917.

Citações

"Cardiac MRI scans contain high-dimensional spatial and temporal features generated throughout the cardiac cycle. The small number of samples compared to the high-dimensional features poses a challenge for machine learning classifiers."
"To tackle this challenge, we leverage automated landmarks with uncertainty quantification in our pipeline. We also extract complementary information from multimodal data from short-axis, four-chamber, and Cardiac Measurements (CM)."
"Decision Curve Analysis (DCA) on the performance suggests the potential clinical utility of the proposed method. The tri-modal model obtained a higher net benefit between decision threshold probabilities of 0.30 and 0.70 which implies that our method has a diagnostic value and can be used in screening high-risk patients from a large population."

Principais Insights Extraídos De

Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI

by Prasun C. Tr... às arxiv.org 04-09-2024

https://arxiv.org/pdf/2303.07540.pdf

Perguntas Mais Profundas

How can the proposed pipeline be further improved to handle multi-institutional data and enhance its generalizability

To enhance the generalizability of the proposed pipeline for handling multi-institutional data, several improvements can be implemented:

Domain Adaptation Techniques: Incorporating domain adaptation methods can help in transferring knowledge from one institution to another, mitigating the domain shift issue.
Data Harmonization: Standardizing data collection protocols and harmonizing data formats across institutions can ensure consistency and facilitate model training on diverse datasets.
Ensemble Learning: Utilizing ensemble learning techniques can combine models trained on data from different institutions, improving robustness and generalizability.
Transfer Learning: Implementing transfer learning by pre-training the model on a large dataset from multiple institutions and fine-tuning on specific institution data can enhance performance on new datasets.
External Validation: Conducting external validation on datasets from various institutions can validate the model's performance across different settings, ensuring its reliability in diverse clinical scenarios.

What other cardiac imaging modalities or clinical data could be integrated to improve the prediction of Pulmonary Arterial Wedge Pressure

Integrating additional cardiac imaging modalities and clinical data can further enhance the prediction of Pulmonary Arterial Wedge Pressure (PAWP):

Echocardiography Data: Incorporating echocardiography data, such as left ventricular ejection fraction and diastolic function parameters, can provide complementary information for more accurate predictions.
Biomarker Data: Including biomarker data like B-type natriuretic peptide (BNP) levels can offer insights into cardiac function and help in refining the prediction model.
Genetic Data: Integrating genetic information related to cardiovascular health can enable a more personalized approach to PAWP prediction and potentially identify genetic markers associated with elevated PAWP.
Hemodynamic Data: Adding hemodynamic parameters obtained from invasive or non-invasive procedures can enrich the model with direct measurements of cardiac function, aiding in more precise predictions.

How can the interpretability of the model be enhanced to provide clinicians with insights into the key features driving the PAWP prediction

Enhancing the interpretability of the model to provide clinicians with insights into key features driving PAWP prediction can be achieved through the following strategies:

Feature Importance Analysis: Conducting feature importance analysis, such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations), to highlight the contribution of each feature to the prediction.
Visualization Techniques: Using visualization methods like heatmaps or saliency maps to illustrate regions in cardiac MRI scans that significantly influence the PAWP prediction.
Clinical Correlation: Establishing a direct correlation between model predictions and clinically relevant parameters can help clinicians understand the rationale behind the predictions.
Interactive Tools: Developing interactive tools that allow clinicians to explore and manipulate input features can facilitate a deeper understanding of how different variables impact PAWP prediction.
Model Explanation Reports: Generating model explanation reports that summarize the key factors influencing each prediction in a clear and concise manner, aiding clinicians in decision-making.