Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study. Issue 3 (5th August 2018)

• Record Type: Journal Article
• Authors: Chen, Yuheng; Duan, Wenna; Sehrawat, Parshant; Chauhan, Vaibhav; Alfaro, Freddy J; Gavrieli, Anna; Qiao, Xingye; Novak, Vera; Dai, Weiying
• Is Part Of: Journal of magnetic resonance imaging. Volume 49:Issue 3(2019)
• Page Start: 834
• Page End: 844
• Publication Date: 2018-08-05
• Subjects: type 2 diabetes mellitus -- machine learning -- perfusion diabetes pattern score; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26256
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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.