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Published on: April 12, 2017
Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy
Ahmed S Fahmy1, Ethan J Rowin2, Arghavan Arafati1
1Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA.
Insights
An artificial intelligence (AI) model combining deep learning and radiomics effectively screens hypertrophic cardiomyopathy (HCM) patients for myocardial scar using non-contrast MRI. This AI tool may reduce unnecessary gadolinium contrast administration in HCM diagnosis.
Area of Science:
- Cardiovascular Magnetic Resonance (CMR) Imaging
- Artificial Intelligence (AI) in Medical Diagnostics
- Cardiology and Cardiovascular Diseases
Background:
- Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) quantifies myocardial scar burden, crucial for hypertrophic cardiomyopathy (HCM) prognosis.
- Approximately 50% of HCM patients lack scar, yet undergo repeated gadolinium-based CMR scans throughout their lives.
- There is a need for non-contrast methods to identify HCM patients without scar, avoiding repeated gadolinium administration.
Purpose of the Study:
- To develop an artificial intelligence (AI)-based screening model to identify HCM patients without myocardial scar.
- To utilize radiomics and deep learning (DL) features from balanced steady state free precession (bSSFP) cine sequences for scar screening.
- To reduce the need for gadolinium-based contrast agents in routine HCM follow-up.
Main Methods:
- Three AI models were evaluated: radiomics, deep learning (DL), and a combined DL-Radiomics approach.
- Models were trained and tested on 759 HCM patients from a multi-center/vendor study using bSSFP cine images.
- Model generalizability was assessed using an external dataset of 100 HCM patients; performance was measured by area-under-receiver-operating curve (AUC).
Main Results:
- The DL-Radiomics model achieved higher AUC than DL or radiomics alone in both internal (0.83 vs. 0.77/0.78) and external (0.74 vs. 0.64/0.71) datasets.
- The DL-Radiomics model correctly identified 43% (internal) and 28% (external) of patients without scar.
- Radiomics and DL models identified 42% (internal) and 16-23% (external) of scar-negative patients, respectively.
Conclusions:
- An AI model integrating DL and radiomics features from bSSFP cine images serves as an effective scar screening tool prior to gadolinium administration in HCM.
- This combined AI approach surpasses individual DL or radiomics models for scar screening in HCM patients.
- Further research is necessary to enhance the accuracy and generalizability of the AI model for broader clinical utility.
Background:
Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.
Methods:
We evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC).
Results:
The DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively.
Conclusions:
A DL-Radiomics AI model using bSSFP cine images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.
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Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...