TY - JOUR
T1 - A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging
AU - Khagi, Bijen
AU - Belousova, Tatiana
AU - Short, Christina M.
AU - Taylor, Addison
AU - Nambi, Vijay
AU - Ballantyne, Christie M.
AU - Bismuth, Jean
AU - Shah, Dipan J.
AU - Brunner, Gerd
N1 - Funding Information:
The work for this project received support from the National Institutes of Health ( R01HL137763 and K25HL121149 both to GB) and the American Heart Association ( 13BGIA16720014 to GB).
Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2024/2
Y1 - 2024/2
N2 - Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.
AB - Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.
KW - Atherosclerosis
KW - Machine learning
KW - Magnetic resonance imaging
KW - Peripheral artery disease
KW - Texture features
KW - Intermittent Claudication
KW - Humans
KW - Peripheral Arterial Disease/diagnostic imaging
KW - Magnetic Resonance Imaging/methods
KW - Muscle, Skeletal/diagnostic imaging
KW - Diabetes Mellitus
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U2 - 10.1016/j.mri.2023.11.014
DO - 10.1016/j.mri.2023.11.014
M3 - Article
C2 - 38065273
AN - SCOPUS:85180340108
SN - 0730-725X
VL - 106
SP - 31
EP - 42
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
ER -