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...
Saved in:
Published in | Journal of magnetic resonance imaging Vol. 59; no. 3; pp. 837 - 848 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.03.2024
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
ISSN | 1053-1807 1522-2586 1522-2586 |
DOI | 10.1002/jmri.28904 |
Cover
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 |
Author_xml | – sequence: 1 givenname: Zi‐Chen orcidid: 0000-0003-0206-2136 surname: Wang fullname: Wang, Zi‐Chen organization: Shanghai Jiao Tong University – sequence: 2 givenname: Zhang‐Zhengyi surname: Fan fullname: Fan, Zhang‐Zhengyi organization: Shanghai Jiao Tong University – sequence: 3 givenname: Xi‐Yuan surname: Liu fullname: Liu, Xi‐Yuan organization: Shanghai Jiao Tong University – sequence: 4 givenname: Ming‐Jie surname: Zhu fullname: Zhu, Ming‐Jie organization: Shanghai Jiao Tong University – sequence: 5 givenname: Shan‐Shan surname: Jiang fullname: Jiang, Shan‐Shan organization: Philips Healthcare – sequence: 6 givenname: Song surname: Tian fullname: Tian, Song organization: Philips Healthcare – sequence: 7 givenname: Bing‐Hua orcidid: 0000-0002-2718-8416 surname: Chen fullname: Chen, Bing‐Hua email: chenbinghua0311@163.com organization: Shanghai Jiao Tong University – sequence: 8 givenname: Lian‐Ming orcidid: 0000-0001-7381-5436 surname: Wu fullname: Wu, Lian‐Ming email: wlmssmu@126.com organization: Shanghai Jiao Tong University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37431848$$D View this record in MEDLINE/PubMed |
BookMark | eNp9kc1u3CAUhVGVqvlpN32ACCmbKpJTwDjAMpo0mVSTVqpmj-7Y1w0jGxzwtPLbh8lMs4iirgDd7xzgnGNy4INHQj5zdsEZE1_XfXQXQhsm35EjXglRiEpfHuQ9q8qCa6YOyXFKa8aYMbL6QA5LJUuupT4i4zXiQBcI0Tv_m7Yh0muX6uh652F0wdPQ0vk0YBxjGB5cTWcQGxf6KQwwPkwUfLOfo0_uD9J59hq3JggJaTa4_3VHf2SvPFtyeg9D-kjet9Al_LRfT8jy5ttyNi8WP2_vZleLopacy0IpVa8QjNFN3TChW5DKAHCzPRnRtCgboYxQpbkEVa3KBoGtpC5VbYzQ5Qn5srMdYnjcYBptn7-GXQcewyZZoSspy4ozldGzV-g6bKLPj7P5pgxWgrNMne6pzarHxg45JoiT_RdnBs53QB1DShHbF4Qzu-3Kbruyz11lmL2Cazc-Zz5GcN3bEr6T_HUdTv8xt99z6jvNEz_2phY |
CitedBy_id | crossref_primary_10_1007_s00330_024_11337_8 crossref_primary_10_1002_jmri_29021 crossref_primary_10_1055_a_2285_3481 crossref_primary_10_3390_computers14020036 crossref_primary_10_1186_s12880_024_01301_9 crossref_primary_10_1007_s13139_024_00850_9 crossref_primary_10_1016_j_acra_2024_03_032 crossref_primary_10_1016_j_cjca_2024_03_011 crossref_primary_10_3389_fcvm_2024_1421013 |
Cites_doi | 10.1016/j.crad.2017.04.019 10.1002/clc.22761 10.1161/CIRCGEN.119.002500 10.1186/s12968-017-0389-8 10.1007/s11906-020-1017-9 10.1093/eurheartj/ehy339 10.1016/j.jcmg.2018.11.024 10.3348/kjr.2021.0815 10.3390/s23063321 10.1109/CVPR.2016.90 10.1016/j.crad.2020.11.001 10.1038/s41746-020-00376-2 10.1002/jmri.26866 10.1093/ehjci/jex007 10.1016/j.ejrad.2018.03.013 10.1111/j.1365-2559.2004.01835.x 10.1007/s10554-021-02462-2 10.1161/CIRCIMAGING.115.003285 10.1136/heartjnl-2015-308764 10.1161/01.HYP.0000107251.49515.c2 10.1016/j.mri.2012.05.001 10.1016/j.jacc.2010.11.013 10.1158/0008-5472.CAN-17-0339 10.1007/s00330-020-07454-9 10.1007/s10741-017-9627-2 10.1016/S0735-1097(02)01712-6 10.1186/s12968-016-0313-7 10.1016/j.ijcard.2020.03.002 10.1093/eurheartj/ehu284 10.1007/s00330-011-2065-y 10.1136/heartjnl-2011-301528 10.1172/JCI31044 10.3389/fcvm.2020.00025 10.1007/s11604-013-0238-0 |
ContentType | Journal Article |
Copyright | 2023 International Society for Magnetic Resonance in Medicine. 2024 International Society for Magnetic Resonance in Medicine |
Copyright_xml | – notice: 2023 International Society for Magnetic Resonance in Medicine. – notice: 2024 International Society for Magnetic Resonance in Medicine |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7TK 8FD FR3 K9. P64 7X8 |
DOI | 10.1002/jmri.28904 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Neurosciences Abstracts Technology Research Database Engineering Research Database ProQuest Health & Medical Complete (Alumni) Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) Engineering Research Database Biotechnology Research Abstracts Technology Research Database Neurosciences Abstracts Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
DatabaseTitleList | ProQuest Health & Medical Complete (Alumni) MEDLINE MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 1522-2586 |
EndPage | 848 |
ExternalDocumentID | 37431848 10_1002_jmri_28904 JMRI28904 |
Genre | researchArticle Research Support, Non-U.S. Gov't Journal Article |
GrantInformation_xml | – fundername: Shanghai Jiao Tong University School of Medicine, The 15th innovation training program funderid: 1521Y035 – fundername: Shanghai Jiao Tong University School of Medicine, The 15th innovation training program grantid: 1521Y035 |
GroupedDBID | --- -DZ .3N .GA .GJ .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 24P 31~ 33P 3O- 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 53G 5GY 5RE 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHHS AAHQN AAIPD AAMNL AANHP AANLZ AAONW AASGY AAWTL AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ABJNI ABLJU ABOCM ABPVW ABQWH ABXGK ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACGOF ACIWK ACMXC ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFRAH AFWVQ AFZJQ AHBTC AHMBA AIACR AIAGR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 C45 CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 F5P FEDTE FUBAC G-S G.N GNP GODZA H.X HBH HDBZQ HF~ HGLYW HHY HHZ HVGLF HZ~ IX1 J0M JPC KBYEO KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M65 MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG OVD P2P P2W P2X P2Z P4B P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K RGB RIWAO RJQFR ROL RWI RX1 RYL SAMSI SUPJJ SV3 TEORI TWZ UB1 V2E V8K V9Y W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WIN WJL WOHZO WQJ WRC WUP WVDHM WXI WXSBR XG1 XV2 ZXP ZZTAW ~IA ~WT AAYXX AEYWJ AGHNM AGQPQ AGYGG CITATION AAMMB AEFGJ AGXDD AIDQK AIDYY CGR CUY CVF ECM EIF NPM 7QO 7TK 8FD FR3 K9. P64 7X8 |
ID | FETCH-LOGICAL-c4114-777cbea998dcd028fa479aa19cd0292dfe4d27927396a75b3dea0b4837c99283 |
IEDL.DBID | DR2 |
ISSN | 1053-1807 1522-2586 |
IngestDate | Fri Jul 11 02:18:14 EDT 2025 Fri Jul 25 12:30:03 EDT 2025 Mon Jul 21 06:04:36 EDT 2025 Tue Jul 01 03:56:59 EDT 2025 Thu Apr 24 23:05:17 EDT 2025 Wed Jan 22 16:14:50 EST 2025 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 3 |
Keywords | deep learning hypertensive heart disease magnetic resonance imaging hypertrophic cardiomyopathy |
Language | English |
License | 2023 International Society for Magnetic Resonance in Medicine. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c4114-777cbea998dcd028fa479aa19cd0292dfe4d27927396a75b3dea0b4837c99283 |
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. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ORCID | 0000-0002-2718-8416 0000-0001-7381-5436 0000-0003-0206-2136 |
PMID | 37431848 |
PQID | 2922855210 |
PQPubID | 1006400 |
PageCount | 12 |
ParticipantIDs | proquest_miscellaneous_2854435107 proquest_journals_2922855210 pubmed_primary_37431848 crossref_primary_10_1002_jmri_28904 crossref_citationtrail_10_1002_jmri_28904 wiley_primary_10_1002_jmri_28904_JMRI28904 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | March 2024 2024-03-00 20240301 |
PublicationDateYYYYMMDD | 2024-03-01 |
PublicationDate_xml | – month: 03 year: 2024 text: March 2024 |
PublicationDecade | 2020 |
PublicationPlace | Hoboken, USA |
PublicationPlace_xml | – name: Hoboken, USA – name: United States – name: Nashville |
PublicationSubtitle | JMRI |
PublicationTitle | Journal of magnetic resonance imaging |
PublicationTitleAlternate | J Magn Reson Imaging |
PublicationYear | 2024 |
Publisher | John Wiley & Sons, Inc Wiley Subscription Services, Inc |
Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc |
References | 2017; 40 2004; 44 2002; 39 2021; 4 2011 2020; 142 2017; 22 2018; 102 2019; 12 2022; 23 2016; 102 2011; 57 2016; 18 2015; 8 2020; 306 2012; 30 2017; 72 2020; 7 2021; 38 2018; 39 2021; 76 2020; 2022 2021; 31 2007; 117 2023; 23 2020; 51 2017; 77 2013; 31 2018 2011; 21 2014; 35 2017; 19 2016 2017; 18 2020; 22 2014; 100 2003; 42 e_1_2_5_27_1 e_1_2_5_28_1 e_1_2_5_25_1 e_1_2_5_23_1 e_1_2_5_24_1 e_1_2_5_21_1 e_1_2_5_22_1 e_1_2_5_29_1 e_1_2_5_20_1 e_1_2_5_40_1 Isensee F (e_1_2_5_31_1) 2018 e_1_2_5_15_1 e_1_2_5_38_1 e_1_2_5_14_1 Bonow RO (e_1_2_5_2_1) 2011 e_1_2_5_17_1 e_1_2_5_36_1 e_1_2_5_9_1 e_1_2_5_16_1 Cetin I (e_1_2_5_26_1) 2018 Ommen SR (e_1_2_5_33_1) 2020; 142 e_1_2_5_37_1 e_1_2_5_8_1 e_1_2_5_11_1 e_1_2_5_34_1 e_1_2_5_7_1 e_1_2_5_10_1 e_1_2_5_35_1 e_1_2_5_6_1 e_1_2_5_13_1 e_1_2_5_32_1 e_1_2_5_5_1 e_1_2_5_12_1 Moez A (e_1_2_5_39_1) 2020 e_1_2_5_4_1 e_1_2_5_3_1 e_1_2_5_19_1 e_1_2_5_18_1 e_1_2_5_30_1 37737641 - J Magn Reson Imaging. 2024 Mar;59(3):849-850. doi: 10.1002/jmri.29021 |
References_xml | – year: 2011 – volume: 72 start-page: 835 issue: 10 year: 2017 end-page: 843 article-title: Hypertrophic cardiomyopathy and left ventricular hypertrophy in hypertensive heart disease with mildly reduced or preserved ejection fraction: Insight from altered mechanics and native T1 mapping publication-title: Clin Radiol – start-page: 120 year: 2018 end-page: 129 – volume: 2022 year: 2020 – volume: 30 start-page: 1323 issue: 9 year: 2012 end-page: 1341 article-title: 3D slicer as an image computing platform for the quantitative imaging network publication-title: Magn Reson Imaging – volume: 19 start-page: 75 issue: 1 year: 2017 article-title: Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI) publication-title: J Cardiovasc Magn Reson – volume: 306 start-page: 102 year: 2020 end-page: 108 article-title: Native T1 and T2 provide distinctive signatures in hypertrophic cardiac conditions—comparison of uremic, hypertensive and hypertrophic cardiomyopathy publication-title: Int J Cardiol – start-page: 770 year: 2016 end-page: 778 – volume: 117 start-page: 568 issue: 3 year: 2007 end-page: 575 article-title: ECM remodeling in hypertensive heart disease publication-title: J Clin Invest – volume: 31 start-page: 693 issue: 10 year: 2013 end-page: 700 article-title: MRI differentiation of cardiomyopathy showing left ventricular hypertrophy and heart failure: Differentiation between cardiac amyloidosis, hypertrophic cardiomyopathy, and hypertensive heart disease publication-title: Jpn J Radiol – volume: 76 start-page: 236.e9 issue: 3 year: 2021 end-page: 236.e19 article-title: Texture analysis applied in T1 maps and extracellular volume obtained using cardiac MRI in the diagnosis of hypertrophic cardiomyopathy and hypertensive heart disease compared with normal controls publication-title: Clin Radiol – volume: 12 start-page: 1946 issue: 10 year: 2019 end-page: 1954 article-title: Radiomic analysis of myocardial native T(1) imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy publication-title: J Am Coll Cardiol Img – start-page: 82 year: 2018 end-page: 90 – volume: 100 start-page: 662 issue: 8 year: 2014 end-page: 671 article-title: Diagnostic approach and differential diagnosis in patients with hypertrophied left ventricles publication-title: Heart (British Cardiac Society) – volume: 39 start-page: 1055 issue: 6 year: 2002 end-page: 1060 article-title: Gender differences and normal left ventricular anatomy in an adult population free of hypertension. A cardiovascular magnetic resonance study of the Framingham heart study offspring cohort publication-title: J Am Coll Cardiol – volume: 40 start-page: 1026 issue: 11 year: 2017 end-page: 1032 article-title: Early segmental relaxation abnormalities in hypertrophic cardiomyopathy for differential diagnostic of patients with left ventricular hypertrophy publication-title: Clin Cardiol – volume: 102 start-page: 61 year: 2018 end-page: 67 article-title: Texture analysis and machine learning of non‐contrast T1‐weighted MR images in patients with hypertrophic cardiomyopathy‐preliminary results publication-title: Eur J Radiol – volume: 35 start-page: 2733 issue: 39 year: 2014 end-page: 2779 article-title: 2014 ESC guidelines on diagnosis and management of hypertrophic cardiomyopathy: The task force for the diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC) publication-title: Eur Heart J – volume: 38 start-page: 841 year: 2021 end-page: 852 article-title: The diagnostic accuracy of truncated cardiovascular MR protocols for detecting non‐ischemic cardiomyopathies publication-title: Int J Cardiovasc Imaging – volume: 23 start-page: 3321 issue: 6 year: 2023 article-title: Left ventricle detection from cardiac magnetic resonance relaxometry images using visual transformer publication-title: Sensors (Basel) – volume: 44 start-page: 412 issue: 5 year: 2004 end-page: 427 article-title: The pathology of hypertrophic cardiomyopathy publication-title: Histopathology – volume: 18 start-page: 744 issue: 7 year: 2017 end-page: 751 article-title: Tissue characterization by T1 and T2 mapping cardiovascular magnetic resonance imaging to monitor myocardial inflammation in healing myocarditis publication-title: Eur Heart J Cardiovasc Imaging – volume: 7 start-page: 25 year: 2020 article-title: Deep learning for cardiac image segmentation: A review publication-title: Front Cardiovasc Med – volume: 31 start-page: 3931 issue: 6 year: 2021 end-page: 3940 article-title: Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images publication-title: Eur Radiol – volume: 39 start-page: 3021 issue: 33 year: 2018 end-page: 3104 article-title: 2018 ESC/ESH guidelines for the management of arterial hypertension publication-title: Eur Heart J – volume: 8 issue: 12 year: 2015 article-title: T1 mapping in discrimination of hypertrophic phenotypes: Hypertensive heart disease and hypertrophic cardiomyopathy: Findings from the international T1 multicenter cardiovascular magnetic resonance study publication-title: Circ Cardiovasc Imaging – volume: 23 start-page: 581 issue: 6 year: 2022 end-page: 597 article-title: Differential diagnosis of thick myocardium according to histologic features revealed by multiparametric cardiac magnetic resonance imaging publication-title: Korean J Radiol – volume: 142 start-page: e558 issue: 25 year: 2020 end-page: e631 article-title: 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: A report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines publication-title: Circulation – volume: 22 start-page: 415 issue: 4 year: 2017 end-page: 430 article-title: T(1) mapping in cardiac MRI publication-title: Heart Fail Rev – volume: 57 start-page: 891 issue: 8 year: 2011 end-page: 903 article-title: Assessment of myocardial fibrosis with cardiovascular magnetic resonance publication-title: J Am Coll Cardiol – volume: 102 start-page: 1087 issue: 14 year: 2016 end-page: 1094 article-title: Distinguishing ventricular septal bulge versus hypertrophic cardiomyopathy in the elderly publication-title: Heart – volume: 51 start-page: 1336 issue: 5 year: 2020 end-page: 1356 article-title: Cardiac T(1) mapping: Techniques and applications publication-title: J Magn Reson Imaging – volume: 4 start-page: 5 issue: 1 year: 2021 article-title: Deep learning‐enabled medical computer vision publication-title: NPJ Digit Med – volume: 21 start-page: 1383 issue: 7 year: 2011 end-page: 1389 article-title: Cardiac MRI assessed left ventricular hypertrophy in differentiating hypertensive heart disease from hypertrophic cardiomyopathy attributable to a sarcomeric gene mutation publication-title: Eur Radiol – volume: 42 start-page: 1206 issue: 6 year: 2003 end-page: 1252 article-title: Seventh report of the joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure publication-title: Hypertension – volume: 12 issue: 5 year: 2019 article-title: Whole exome sequencing reveals a large genetic heterogeneity and revisits the causes of hypertrophic cardiomyopathy publication-title: Circ Genom Precis Med – volume: 22 start-page: 11 issue: 2 year: 2020 article-title: Pathophysiology of hypertensive heart disease: Beyond left ventricular hypertrophy publication-title: Curr Hypertens Rep – volume: 18 start-page: 92 issue: 1 year: 2016 article-title: Histologic validation of myocardial fibrosis measured by T1 mapping: A systematic review and meta‐analysis publication-title: J Cardiovasc Magn Reson – volume: 77 start-page: e104 issue: 21 year: 2017 end-page: e107 article-title: Computational radiomics system to decode the radiographic phenotype publication-title: Cancer Res – ident: e_1_2_5_8_1 doi: 10.1016/j.crad.2017.04.019 – volume: 142 start-page: e558 issue: 25 year: 2020 ident: e_1_2_5_33_1 article-title: 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: A report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines publication-title: Circulation – ident: e_1_2_5_9_1 doi: 10.1002/clc.22761 – ident: e_1_2_5_3_1 doi: 10.1161/CIRCGEN.119.002500 – ident: e_1_2_5_18_1 doi: 10.1186/s12968-017-0389-8 – ident: e_1_2_5_17_1 doi: 10.1007/s11906-020-1017-9 – ident: e_1_2_5_36_1 doi: 10.1093/eurheartj/ehy339 – ident: e_1_2_5_10_1 doi: 10.1016/j.jcmg.2018.11.024 – ident: e_1_2_5_12_1 doi: 10.3348/kjr.2021.0815 – ident: e_1_2_5_29_1 doi: 10.3390/s23063321 – ident: e_1_2_5_40_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_5_11_1 doi: 10.1016/j.crad.2020.11.001 – ident: e_1_2_5_28_1 doi: 10.1038/s41746-020-00376-2 – ident: e_1_2_5_16_1 doi: 10.1002/jmri.26866 – ident: e_1_2_5_20_1 doi: 10.1093/ehjci/jex007 – ident: e_1_2_5_27_1 doi: 10.1016/j.ejrad.2018.03.013 – ident: e_1_2_5_15_1 doi: 10.1111/j.1365-2559.2004.01835.x – volume-title: Braunwald's heart disease E‐book: A textbook of cardiovascular medicine. year: 2011 ident: e_1_2_5_2_1 – ident: e_1_2_5_25_1 doi: 10.1007/s10554-021-02462-2 – volume-title: PyCaret: An open source, low‐code machine learning library in python year: 2020 ident: e_1_2_5_39_1 – ident: e_1_2_5_7_1 doi: 10.1161/CIRCIMAGING.115.003285 – ident: e_1_2_5_13_1 doi: 10.1136/heartjnl-2015-308764 – ident: e_1_2_5_35_1 doi: 10.1161/01.HYP.0000107251.49515.c2 – ident: e_1_2_5_37_1 doi: 10.1016/j.mri.2012.05.001 – ident: e_1_2_5_23_1 doi: 10.1016/j.jacc.2010.11.013 – start-page: 82 volume-title: ACDC and MMWHS challenges year: 2018 ident: e_1_2_5_26_1 – ident: e_1_2_5_38_1 doi: 10.1158/0008-5472.CAN-17-0339 – ident: e_1_2_5_22_1 doi: 10.1007/s00330-020-07454-9 – ident: e_1_2_5_21_1 doi: 10.1007/s10741-017-9627-2 – start-page: 120 volume-title: Statistical atlases and computational models of the heart. ACDC and MMWHS challenges year: 2018 ident: e_1_2_5_31_1 – ident: e_1_2_5_34_1 doi: 10.1016/S0735-1097(02)01712-6 – ident: e_1_2_5_19_1 doi: 10.1186/s12968-016-0313-7 – ident: e_1_2_5_24_1 doi: 10.1016/j.ijcard.2020.03.002 – ident: e_1_2_5_32_1 doi: 10.1093/eurheartj/ehu284 – ident: e_1_2_5_5_1 doi: 10.1007/s00330-011-2065-y – ident: e_1_2_5_14_1 doi: 10.1136/heartjnl-2011-301528 – ident: e_1_2_5_4_1 doi: 10.1172/JCI31044 – ident: e_1_2_5_30_1 doi: 10.3389/fcvm.2020.00025 – ident: e_1_2_5_6_1 doi: 10.1007/s11604-013-0238-0 – reference: 37737641 - J Magn Reson Imaging. 2024 Mar;59(3):849-850. doi: 10.1002/jmri.29021 |
SSID | ssj0009945 |
Score | 2.497161 |
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... |
SourceID | proquest pubmed crossref wiley |
SourceType | Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 837 |
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 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT-MwEB4hDojL8tylvGQEF5BS8nCaROKCWlBBKgdUJC6ryLGd3fJIqjY9wK9nxklTFRAS3BLFGTsej_05M_4G4EjzQCuP-1aIa6NFDFxWwh3Hkm4qPE8GrnLo7HDvptW949f3_v0CnE3PwpT8EPUPN7IMM1-TgYtkfDojDX14Hg2a5CYjMlDHaxFxfud2xh0VRSZDMeIHz3JCO6i5Sd3T2avzq9EHiDmPWM2Sc7kCf6eNLSNNHpuTImnK13c8jj_9mlX4VWFRdl4OnjVY0Nk6LPUqb_sGFB2th6xiYP3HEN6yzoCmGQqfIYWyPGVd3MiOilE-_D-QrG2iW59fckp0_MJEpqrnZZg866KsgoSQU4ihgN7tFbsx3OOs77CeGI43oX950W93rSpLgyVRqRzheSATLXDbpqRCtJIKHkRCOBHdRa5KNVfEUhh4UUsEfuIpLeyEiOxlFCG4-Q2LWZ7pLWBahbiBsVOeSp-nCqW6wpFahFwmNk-DBhxPlRXLisGcEmk8xSX3shtTL8amFxtwWJcdlrwdn5baneo8rmx3HGOj3dBHWGM34KB-jFZHrhSR6XyCZULiDcT5DBv1pxwrdTUegbKQhw04MRr_ov74GrvZXG1_p_AOLLuIrcpQuF1YLEYTvYfYqEj2jQ28AasPCtY |
linkProvider | Wiley-Blackwell |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB4VKhUuUN4LtDUqlyJlycMhyRGxReGRPaCtxC1ybKddHslqN3uAX8-ME7KiVEj0lsiO7Xg89mfP-BuAfc0DrTzuWyGujRYxcFkZdxxLurnwPBm4yqG7w0n_KP7Fz6_968Y3h-7C1PwQ7YEbaYaZr0nB6UD6cMYaenM_HnbJTsbn4CMZ6Egve1cz9qgoMjGKEUF4lhPaQctO6h7Ovn25Hr0CmS8xq1l0TpfryKoTw1VIvia33WmVdeXjX0yO__0_n2GpgaPsuB4_K_BBF6vwKWkM7mtQ9bQesYaE9TdDhMt6Q5ppyIOGZMrKnMW4lx1X43L0ZyjZiXFwvX8oKdbxAxOFatJrT3kWY1kVFUJ2IYYFJFdnrG_ox9nAYYkYTdZhcPpzcBJbTaAGS6JcOSL0QGZa4M5NSYWAJRc8iIRwInqLXJVrroioMPCiIxH4mae0sDPispdRhPhmA-aLstBbwLQKcQ9j5zyXPs8VluoKR2oRcpnZPA868ONZWqlsSMwplsZdWtMvuyn1Ymp6sQPf27yjmrrjn7l2n4WeNuo7SbHRbugjsrE7sNcmo-KRNUUUupxinpCoA3FKw0Zt1oOlrcYjXBbysAMHRuRv1J-eYzebp-33ZP4GC_EguUwvz_oXO7DoItSqPeN2Yb4aT_UXhEpV9tUoxBNLYA7z |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dT9swED8xJqG9sG_oBszT9sKklHw4TSLxgihVYWs1oU7iZYocf0D5SKKSPrC_fndOmooxIW1viXI5Oz6f_bv4_DPAZ80jrQIeOjHOjQ4xcDkZ9zxH-kYEgYx85dHe4dG4N_zBT87CsxXYX-yFqfkh2h9u5Bl2vCYHL5XZW5KGXt7Mpl1aJuNP4CnvYXhFkOh0SR6VJPaIYgQQgePFbtSSk_p7y3fvT0cPMOZ9yGrnnMFz-LmobZ1qctWdV1lX_vqDyPF_P-cFrDdglB3UveclrOj8FayNmuX211D1tS5ZQ8F6zhDfsv6UxhnKnyGLssKwIUays2pWlBdTyQ5teuvNXUEnHd8xkavmeZ0nz4aoqyIltCrEUMHo9JiNLfk4m3hsJMrbNzAZHE0Oh05zTIMj0aoc8XkkMy0wblNSIVwxgkeJEF5Cd4mvjOaKaAqjIOmJKMwCpYWbEZO9TBJEN29hNS9yvQlMqxgjGNdwI0NuFGr1hSe1iLnMXG6iDuwujJXKhsKcTtK4TmvyZT-lVkxtK3bgUytb1sQdf5XaWtg8bZz3NsVK-3GIuMbtwMf2MbodraWIXBdzlImJOBAHNKzURt1X2mICQmUxjzvwxVr8kfLTE2xme_XuX4Q_wNr3_iD9djz--h6e-Yiz6rS4LVitZnO9jTipynasO_wGGhkNog |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning+for+Discrimination+of+Hypertrophic+Cardiomyopathy+and+Hypertensive+Heart+Disease+on+MRI+Native+T1+Maps&rft.jtitle=Journal+of+magnetic+resonance+imaging&rft.au=Zi%E2%80%90Chen+Wang&rft.au=Zhang%E2%80%90Zhengyi+Fan&rft.au=Xi%E2%80%90Yuan+Liu&rft.au=Ming%E2%80%90Jie+Zhu&rft.date=2024-03-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1053-1807&rft.eissn=1522-2586&rft.volume=59&rft.issue=3&rft.spage=837&rft.epage=848&rft_id=info:doi/10.1002%2Fjmri.28904&rft.externalDBID=NO_FULL_TEXT |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-1807&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-1807&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-1807&client=summon |