Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps

Background Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learni...

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Published inJournal of magnetic resonance imaging Vol. 59; no. 3; pp. 837 - 848
Main Authors Wang, Zi‐Chen, Fan, Zhang‐Zhengyi, Liu, Xi‐Yuan, Zhu, Ming‐Jie, Jiang, Shan‐Shan, Tian, Song, Chen, Bing‐Hua, Wu, Lian‐Ming
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.03.2024
Wiley Subscription Services, Inc
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Online AccessGet full text
ISSN1053-1807
1522-2586
1522-2586
DOI10.1002/jmri.28904

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Abstract Background Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. Purpose To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. Study Type Retrospective. Population 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). Field Strength/Sequence 3.0T; Balanced steady‐state free precession, phase‐sensitive inversion recovery (PSIR) and multislice native T1 mapping. Assessment Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL‐myo), myocardial ring bounding box (DL‐box) and the surrounding tissue without myocardial ring (DL‐nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. Statistical Tests Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann–Whitney U‐test and Chi‐square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. Results DL‐myo, DL‐box, and DL‐nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702–0.959), 0.766 (0.617–0.915), 0.795 (0.654–0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352–0.738) and 0.800 (0.655–0.944) in the testing set. Data Conclusion The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. Level of Evidence 4 Technical Efficacy Stage 2
AbstractList BackgroundNative T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated.PurposeTo examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods.Study TypeRetrospective.Population128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17).Field Strength/Sequence3.0T; Balanced steady‐state free precession, phase‐sensitive inversion recovery (PSIR) and multislice native T1 mapping.AssessmentCompare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL‐myo), myocardial ring bounding box (DL‐box) and the surrounding tissue without myocardial ring (DL‐nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve.Statistical TestsAccuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann–Whitney U‐test and Chi‐square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant.ResultsDL‐myo, DL‐box, and DL‐nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702–0.959), 0.766 (0.617–0.915), 0.795 (0.654–0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352–0.738) and 0.800 (0.655–0.944) in the testing set.Data ConclusionThe DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode.Level of Evidence4Technical Efficacy Stage2
Background Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. Purpose To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. Study Type Retrospective. Population 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). Field Strength/Sequence 3.0T; Balanced steady‐state free precession, phase‐sensitive inversion recovery (PSIR) and multislice native T1 mapping. Assessment Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL‐myo), myocardial ring bounding box (DL‐box) and the surrounding tissue without myocardial ring (DL‐nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. Statistical Tests Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann–Whitney U‐test and Chi‐square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. Results DL‐myo, DL‐box, and DL‐nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702–0.959), 0.766 (0.617–0.915), 0.795 (0.654–0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352–0.738) and 0.800 (0.655–0.944) in the testing set. Data Conclusion The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. Level of Evidence 4 Technical Efficacy Stage 2
Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated. To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods. Retrospective. 128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17). 3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping. Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve. Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant. DL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set. The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode. 4 TECHNICAL EFFICACY STAGE: 2.
Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated.BACKGROUNDNative T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is that global native T1 remains modest discrimination performance and radiomics requires feature extraction beforehand. Deep learning (DL) is a promising technique in differential diagnosis. However, its feasibility for discriminating HCM and HHD has not been investigated.To examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods.PURPOSETo examine the feasibility of DL in differentiating HCM and HHD based on T1 images and compare its diagnostic performance with other methods.Retrospective.STUDY TYPERetrospective.128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17).POPULATION128 HCM patients (men, 75; age, 50 years ± 16) and 59 HHD patients (men, 40; age, 45 years ± 17).3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping.FIELD STRENGTH/SEQUENCE3.0T; Balanced steady-state free precession, phase-sensitive inversion recovery (PSIR) and multislice native T1 mapping.Compare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve.ASSESSMENTCompare HCM and HHD patients baseline data. Myocardial T1 values were extracted from native T1 images. Radiomics was implemented through feature extraction and Extra Trees Classifier. The DL network is ResNet32. Different input including myocardial ring (DL-myo), myocardial ring bounding box (DL-box) and the surrounding tissue without myocardial ring (DL-nomyo) were tested. We evaluate diagnostic performance through AUC of ROC curve.Accuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant.STATISTICAL TESTSAccuracy, sensitivity, specificity, ROC, and AUC were calculated. Independent t test, Mann-Whitney U-test and Chi-square test were adopted for HCM and HHD comparison. P < 0.05 was considered statistically significant.DL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set.RESULTSDL-myo, DL-box, and DL-nomyo models showed an AUC (95% confidential interval) of 0.830 (0.702-0.959), 0.766 (0.617-0.915), 0.795 (0.654-0.936) in the testing set. AUC of native T1 and radiomics were 0.545 (0.352-0.738) and 0.800 (0.655-0.944) in the testing set.The DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode.DATA CONCLUSIONThe DL method based on T1 mapping seems capable of discriminating HCM and HHD. Considering diagnostic performance, the DL network outperformed the native T1 method. Compared with radiomics, DL won an advantage for its high specificity and automated working mode.4 TECHNICAL EFFICACY STAGE: 2.LEVEL OF EVIDENCE4 TECHNICAL EFFICACY STAGE: 2.
Author Wu, Lian‐Ming
Tian, Song
Fan, Zhang‐Zhengyi
Wang, Zi‐Chen
Jiang, Shan‐Shan
Chen, Bing‐Hua
Zhu, Ming‐Jie
Liu, Xi‐Yuan
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Keywords deep learning
hypertensive heart disease
magnetic resonance imaging
hypertrophic cardiomyopathy
Language English
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Notes Contract grant sponsor: Shanghai Jiao Tong University School of Medicine, The 15th innovation training program; Contract grant number: 1521Y035.
The first two authors contributed equally to this work.
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Snippet Background Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current...
Native T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current problem is...
BackgroundNative T1 and radiomics were used for hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) differentiation previously. The current...
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SubjectTerms Cardiomyopathy
Cardiomyopathy, Hypertrophic
Cardiovascular disease
Cardiovascular diseases
Deep Learning
Diagnostic systems
Differential diagnosis
Feasibility
Feature extraction
Field strength
Heart Diseases
Humans
Hypertension
hypertensive heart disease
hypertrophic cardiomyopathy
Machine learning
magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Mapping
Medical imaging
Middle Aged
Performance evaluation
Population studies
Radiomics
Retrospective Studies
Statistical analysis
Statistical tests
Title Deep Learning for Discrimination of Hypertrophic Cardiomyopathy and Hypertensive Heart Disease on MRI Native T1 Maps
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fjmri.28904
https://www.ncbi.nlm.nih.gov/pubmed/37431848
https://www.proquest.com/docview/2922855210
https://www.proquest.com/docview/2854435107
Volume 59
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