Quantifying disease pathology and predicting disease progression in multiple sclerosis with only clinical routine T2-FLAIR MRI

仅利用临床常规T2-FLAIR MRI量化多发性硬化症的病理变化并预测疾病进展

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Abstract

BACKGROUND: Although quantitative measures from research-quality MRI provide a means to study multiple sclerosis (MS) pathology in vivo, these metrics are often unavailable in legacy clinical datasets. OBJECTIVE: To determine how well an automatically-generated quantitative snapshot of brain pathology, measured only on clinical routine T2-FLAIR MRI, can substitute for more conventional measures on research MRI in terms of capturing multi-factorial disease pathology and providing similar clinical relevance. METHODS: MRI with both research-quality sequences and conventional clinical T2-FLAIR was acquired for 172 MS patients at baseline, and neurologic disability was assessed at baseline and five-years later. Five measures (thalamus volume, lateral ventricle volume, medulla oblongata volume, lesion volume, and network efficiency) for quantifying disparate aspects of neuropathology from low-resolution T2-FLAIR were applied to predict standard research-quality MRI measures. They were compared in regard to association with future neurologic disability and disease progression over five years. RESULTS: The combination of the five T2-FLAIR measures explained most of the variance in standard research-quality MRI. T2-FLAIR measures were associated with neurologic disability and cognitive function five-years later (R(2) = 0.279, p < 0.001; R(2) = 0.382, p < 0.001), similar to standard research-quality MRI (R(2) = 0.279, p < 0.001; R(2) = 0.366, p < 0.001). They also similarly predicted disability progression over five years (%-correctly-classified = 69.8, p = 0.034), compared to standard research-quality MRI (%-correctly-classified = 72.4%, p = 0.022) in relapsing-remitting MS. CONCLUSION: A set of five T2-FLAIR-only measures can substitute for standard research-quality MRI, especially in relapsing-remitting MS. When only clinical T2-FLAIR is available, it can be used to obtain substantially more quantitative information about brain pathology and disability than is currently standard practice.

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