Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke

In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted--DWI, T(1)-weighted--T1WI, T(2)-weighted--T2WI, and proton density weighted--PDWI) for inputs, and the chroni...

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Published inPloS one Vol. 6; no. 8; p. e22626
Main Authors Bagher-Ebadian, Hassan, Jafari-Khouzani, Kourosh, Mitsias, Panayiotis D., Lu, Mei, Soltanian-Zadeh, Hamid, Chopp, Michael, Ewing, James R.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 10.08.2011
Public Library of Science (PLoS)
Subjects
Adult
Aged
Artificial neural networks
Biology
Cerebral infarction
Cerebral Infarction - complications
Cerebral Infarction - diagnosis
Cerebral Infarction - pathology
Demography
Driving while intoxicated
Female
Humans
Infarction
Ischemia
Lesions
Magnetic Resonance Imaging
Male
Medicine
Middle Aged
Network analysis
Neural networks
Neural Networks (Computer)
Optimization
Patients
Prognosis
Proton density (concentration)
ROC Curve
Stroke
Stroke - complications
Stroke - diagnosis
Stroke - pathology
Stroke patients
Training
United States > US
Detroit Michigan
Michigan
Online AccessGet full text

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Abstract In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted--DWI, T(1)-weighted--T1WI, T(2)-weighted--T2WI, and proton density weighted--PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
AbstractList In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted--DWI, T(1)-weighted--T1WI, T(2)-weighted--T2WI, and proton density weighted--PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted--DWI, T(1)-weighted--T1WI, T(2)-weighted--T2WI, and proton density weighted--PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T 1 -weighted – T1WI, T 2 -weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated ( r  = 0.80, p <0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T1-weighted – T1WI, T2-weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted--DWI, T(1)-weighted--T1WI, T(2)-weighted--T2WI, and proton density weighted--PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T.sub.1 -weighted - T1WI, T.sub.2 -weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
Audience Academic
Author Soltanian-Zadeh, Hamid
Ewing, James R.
Lu, Mei
Bagher-Ebadian, Hassan
Jafari-Khouzani, Kourosh
Mitsias, Panayiotis D.
Chopp, Michael
AuthorAffiliation 5 Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
1 Department of Neurology, Henry Ford Hospital, Detroit, Michigan, United States of America
2 Department of Physics, Oakland University, Rochester, Michigan, United States of America
7 Department of Neurology, Wayne State University, Detroit, Michigan, United States of America
Beijing Normal University, China
3 Department of Diagnostic Radiology, Henry Ford Hospital, Detroit, Michigan, United States of America
6 Department of Physiology, Wayne State University, Detroit, Michigan, United States of America
4 Department of Biostatistics and Research Epidemiology, Henry Ford Hospital, Detroit, Michigan, United States of America
AuthorAffiliation_xml – name: 4 Department of Biostatistics and Research Epidemiology, Henry Ford Hospital, Detroit, Michigan, United States of America
– name: 7 Department of Neurology, Wayne State University, Detroit, Michigan, United States of America
– name: 2 Department of Physics, Oakland University, Rochester, Michigan, United States of America
– name: 5 Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
– name: 6 Department of Physiology, Wayne State University, Detroit, Michigan, United States of America
– name: Beijing Normal University, China
– name: 3 Department of Diagnostic Radiology, Henry Ford Hospital, Detroit, Michigan, United States of America
– name: 1 Department of Neurology, Henry Ford Hospital, Detroit, Michigan, United States of America
Author_xml – sequence: 1
  givenname: Hassan
  surname: Bagher-Ebadian
  fullname: Bagher-Ebadian, Hassan
– sequence: 2
  givenname: Kourosh
  surname: Jafari-Khouzani
  fullname: Jafari-Khouzani, Kourosh
– sequence: 3
  givenname: Panayiotis D.
  surname: Mitsias
  fullname: Mitsias, Panayiotis D.
– sequence: 4
  givenname: Mei
  surname: Lu
  fullname: Lu, Mei
– sequence: 5
  givenname: Hamid
  surname: Soltanian-Zadeh
  fullname: Soltanian-Zadeh, Hamid
– sequence: 6
  givenname: Michael
  surname: Chopp
  fullname: Chopp, Michael
– sequence: 7
  givenname: James R.
  surname: Ewing
  fullname: Ewing, James R.
BackLink https://www.ncbi.nlm.nih.gov/pubmed/21853039$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2011 Public Library of Science
2011 Bagher-Ebadian et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Bagher-Ebadian et al. 2011
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DocumentTitleAlternate Predicting Final Extent of Ischemic Infarction
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Conceived and designed the experiments: HB-E. Performed the experiments: PDM. Analyzed the data: KJ-K. Contributed reagents/materials/analysis tools: KJ-K. Wrote the paper: HB-E JE. Gave technical consultation: HS-Z. Conducted the statistical analysis: ML. Directed and supervised the study: MC. Supervised the team: JRE.
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Snippet In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of...
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StartPage e22626
SubjectTerms Adult
Aged
Artificial neural networks
Biology
Cerebral infarction
Cerebral Infarction - complications
Cerebral Infarction - diagnosis
Cerebral Infarction - pathology
Demography
Driving while intoxicated
Female
Humans
Infarction
Ischemia
Lesions
Magnetic Resonance Imaging
Male
Medicine
Middle Aged
Network analysis
Neural networks
Neural Networks (Computer)
Optimization
Patients
Prognosis
Proton density (concentration)
ROC Curve
Stroke
Stroke - complications
Stroke - diagnosis
Stroke - pathology
Stroke patients
Training
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Title Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke
URI https://www.ncbi.nlm.nih.gov/pubmed/21853039
https://www.proquest.com/docview/1307532653
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http://dx.doi.org/10.1371/journal.pone.0022626
Volume 6
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