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Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study
Author(s) -
Chen Yuheng,
Duan Wenna,
Sehrawat Parshant,
Chauhan Vaibhav,
Alfaro Freddy J,
Gavrieli Anna,
Qiao Xingye,
Novak Vera,
Dai Weiying
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26256
Subject(s) - medicine , logistic regression , type 2 diabetes mellitus , feature selection , population , artificial intelligence , machine learning , diabetes mellitus , computer science , endocrinology , environmental health
Background Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood–brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine‐learning methodologies to identify a T2DM‐related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. Purpose To develop a machine‐learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM‐related network pattern, and association of the pattern with cognitive performance/disease severity. Study Type A cross‐sectional study and prospective longitudinal study with a 2‐year time interval. Population Seventy‐three subjects (41 T2DM patients and 32 controls) aged 50–85 years old at baseline, and 42 subjects (19 T2DM and 23 controls) aged 53–88 years old at 2‐year follow‐up. Field Strength/Sequence 3T pseudocontinuous arterial spin‐labeling MRI. Assessment Machine‐learning‐based pipeline (principal component analysis, feature selection, and logistic regression classifier) to generate the T2DM‐related network pattern and the individual scores associated with the pattern. Statistical Tests Linear regression analysis with gray matter volume and education years as covariates. Results The machine‐learning‐based method is superior to the widely used univariate group comparison method with increased test accuracy, test area under the curve, test positive predictive value, adjusted McFadden's R square of 4%, 12%, 7%, and 24%, respectively. The pattern‐related individual scores are associated with diabetes severity variables, mobility, and cognitive performance at baseline ( P < 0.05, | r | > 0.3). More important, the longitudinal change of individual pattern scores is associated with the longitudinal change of HbA1c ( P = 0.0053, r = 0.64), and baseline cholesterol ( P = 0.037, r = 0.51). Data Conclusion The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients. Further validation is needed from a larger study. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:834–844.