PMID- 31038407
OWN - NLM
STAT- MEDLINE
DCOM- 20200127
LR  - 20220410
IS  - 1527-1315 (Electronic)
IS  - 0033-8419 (Linking)
VI  - 291
IP  - 3
DP  - 2019 Jun
TI  - Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced 
      Cardiac Cine MRI.
PG  - 606-617
LID - 10.1148/radiol.2019182304 [doi]
AB  - Background Renal impairment is common in patients with coronary artery disease 
      and, if severe, late gadolinium enhancement (LGE) imaging for myocardial 
      infarction (MI) evaluation cannot be performed. Purpose To develop a fully 
      automatic framework for chronic MI delineation via deep learning on non-contrast 
      material-enhanced cardiac cine MRI. Materials and Methods In this retrospective 
      single-center study, a deep learning model was developed to extract motion 
      features from the left ventricle and delineate MI regions on nonenhanced cardiac 
      cine MRI collected between October 2015 and March 2017. Patients with chronic MI, 
      as well as healthy control patients, had both nonenhanced cardiac cine (25 phases 
      per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations 
      were used for the training data set and 20% for the independent testing data set. 
      Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic 
      performance was assessed by analysis of the area under the receiver operating 
      characteristic curve (AUC). MI area and MI area percentage from nonenhanced 
      cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t 
      test, and Bland-Altman analysis. Results Study participants included 212 patients 
      with chronic MI (men, 171; age, 57.2 years +/- 12.5) and 87 healthy control 
      patients (men, 42; age, 43.3 years +/- 15.5). Using the full cardiac cine MRI, the 
      per-segment sensitivity and specificity for detecting chronic MI in the 
      independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. 
      There were no differences between nonenhanced cardiac cine and LGE MRI analyses 
      in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area 
      (6.2 cm(2) +/- 2.8 vs 5.5 cm(2) +/- 2.3, respectively; P = .27; correlation 
      coefficient, r = 0.88), and MI area percentage (21.5% +/- 17.3 vs 18.5% +/- 15.4; P = 
      .17; correlation coefficient, r = 0.89). Conclusion The proposed deep learning 
      framework on nonenhanced cardiac cine MRI enables the confirmation (presence), 
      detection (position), and delineation (transmurality and size) of chronic 
      myocardial infarction. However, future larger-scale multicenter studies are 
      required for a full validation. Published under a CC BY 4.0 license. Online 
      supplemental material is available for this article. See also the editorial by 
      Leiner in this issue.
FAU - Zhang, Nan
AU  - Zhang N
AUID- ORCID: 0000-0002-8309-4262
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Yang, Guang
AU  - Yang G
AUID- ORCID: 0000-0001-7344-7733
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Gao, Zhifan
AU  - Gao Z
AUID- ORCID: 0000-0002-1576-4439
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Xu, Chenchu
AU  - Xu C
AUID- ORCID: 0000-0002-8253-1686
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Zhang, Yanping
AU  - Zhang Y
AUID- ORCID: 0000-0002-5678-3038
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Shi, Rui
AU  - Shi R
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Keegan, Jennifer
AU  - Keegan J
AUID- ORCID: 0000-0002-8080-8442
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Xu, Lei
AU  - Xu L
AUID- ORCID: 0000-0002-8499-0448
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Zhang, Heye
AU  - Zhang H
AUID- ORCID: 0000-0003-3828-0629
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Fan, Zhanming
AU  - Fan Z
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
FAU - Firmin, David
AU  - Firmin D
AUID- ORCID: 0000-0003-3894-7489
AD  - From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical 
      University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., 
      Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England 
      (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College 
      London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced 
      Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui 
      University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun 
      Yat-Sen University, Shenzhen, China (H.Z.).
LA  - eng
GR  - PG/16/78/32402/BHF_/British Heart Foundation/United Kingdom
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190430
PL  - United States
TA  - Radiology
JT  - Radiology
JID - 0401260
SB  - IM
CIN - Radiology. 2019 Jun;291(3):618-619. PMID: 31039075
MH  - Adult
MH  - Aged
MH  - Chronic Disease
MH  - Databases, Factual
MH  - *Deep Learning
MH  - Female
MH  - Humans
MH  - Image Interpretation, Computer-Assisted/*methods
MH  - Magnetic Resonance Imaging, Cine/*methods
MH  - Male
MH  - Middle Aged
MH  - Myocardial Infarction/*diagnostic imaging
MH  - Retrospective Studies
MH  - Sensitivity and Specificity
EDAT- 2019/05/01 06:00
MHDA- 2020/01/28 06:00
CRDT- 2019/05/01 06:00
PHST- 2019/05/01 06:00 [pubmed]
PHST- 2020/01/28 06:00 [medline]
PHST- 2019/05/01 06:00 [entrez]
AID - 10.1148/radiol.2019182304 [doi]
PST - ppublish
SO  - Radiology. 2019 Jun;291(3):606-617. doi: 10.1148/radiol.2019182304. Epub 2019 Apr 
      30.