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Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting multiple sclerosis

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机构： [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. [2]Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China. [3]Department of Radiology, Renmin Hospital, Hubei University of Medicine, Shiyan, China. [4]Department of Medical Imaging Product, Neusoft Group Ltd., Shenyang, People’s Republic of China. [5]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China. [6]Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China. [7]Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China. [8]Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. [9]Department of Neurology, China–Japan Union Hospital of Jilin University, Changchun, China. [10]Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland. [11]Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands. [12]Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, UK. [13]Clinical and Technical Support, Philips Healthcare, Beijing, China. [14]China National Clinical Research Center for Neurological Diseases, Beijing, China. [15]Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China. [16]Center for Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

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关键词： Relapsing-remitting multiple sclerosis Deep learning Brain abnormality Cognition Disability

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We aimed to characterize the brain abnormalities that are associated with the cognitive and physical performance of patients with relapsing-remitting multiple sclerosis (RRMS) using a deep learning algorithm.Three-dimensional (3D) nnU-Net was employed to calculate a novel spatial abnormality map by T1-weighted images and 281 RRMS patients (Dataset-1, male/female = 101/180, median age [range] = 35.0 [17.0, 65.0] years) were categorized into subtypes. Comparison of clinical and MRI features between RRMS subtypes was conducted by Kruskal-Wallis test. Kaplan-Meier analysis was conducted to investigate disability progression in RRMS subtypes. Additional validation using two other RRMS datasets (Dataset-2, n = 33 and Dataset-3, n = 56) was conducted.Five RRMS subtypes were identified: (1) a Frontal-I subtype showing preserved cognitive performance and mild physical disability, and low risk of disability worsening; (2) a Frontal-II subtype showing low cognitive scores and severe physical disability with significant brain volume loss, and a high propensity for disability worsening; (3) a temporal-cerebellar subtype demonstrating lowest cognitive scores and severest physical disability among all subtypes but remaining relatively stable during follow-up; (4) an occipital subtype demonstrating similar clinical and imaging characteristics as the Frontal-II subtype, except a large number of relapses at baseline and preserved cognitive performance; and (5) a subcortical subtype showing preserved cognitive performance and low physical disability but a similar prognosis as the occipital and Frontal-II subtypes. Additional validation confirmed the above findings.Spatial abnormality maps can explain heterogeneity in cognitive and physical performance in RRMS and may contribute to stratified management.Question Can a deep learning algorithm characterize the brain abnormalities associated with the cognitive and physical performance of patients with RRMS? Findings Five RRMS subtypes were identified by the algorithm that demonstrated variable cognitive and physical performance. Clinical relevance The spatial abnormality maps derived RRMS subtypes had distinct cognitive and physical performances, which have a potential for individually tailored management.© 2024. The Author(s), under exclusive licence to European Society of Radiology.

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出版当年[2023]版：

大类 | 2 区

医学

小类 | 2 区核医学

最新[2023]版：

大类 | 2 区

医学

小类 | 2 区核医学

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出版当年[2022]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

最新[2023]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 4.7 最新[2023版] 5.2 最新五年平均 5.9 出版当年[2022版] 5.5 出版当年五年平均 7.034 出版前一年[2021版] 4.7 出版后一年[2023版]

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第一作者机构： [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

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Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting multiple sclerosis

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