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

Zhizheng Zhuo^#¹, Ningnannan Zhang^#², Feng Ao^#^{3

4}, Tiantian Hua³, Yunyun Duan³, Xiaolu Xu³, Jinyuan Weng⁵, Guanmei Cao³, Kuncheng Li⁶, Fuqing Zhou⁷, Haiqing Li⁸, Yongmei Li⁹, Xuemei Han¹⁰, Sven Haller¹¹, Frederik Barkhof^{12

13}, Geli Hu¹⁴, Fudong Shi^{15

16}, Xinghu Zhang¹⁷, Decai Tian¹⁷, Yaou Liu¹⁸

Affiliations

¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 18612161210@163.com.
² Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
³ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁴ Department of Radiology, Renmin Hospital, Hubei University of Medicine, Shiyan, China.
⁵ Department of Medical Imaging Product, Neusoft Group Ltd., Shenyang, People's Republic of China.
⁶ Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
⁷ Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China.
⁸ Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
⁹ Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
¹⁰ Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
¹¹ Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland.
¹² Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.
¹³ Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, UK.
¹⁴ Clinical and Technical Support, Philips Healthcare, Beijing, China.
¹⁵ China National Clinical Research Center for Neurological Diseases, Beijing, China.
¹⁶ Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
¹⁷ Center for Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
¹⁸ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. liuyaou@bjtth.org.

^# Contributed equally.

PMID: 39470796
DOI: 10.1007/s00330-024-11157-w

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

Zhizheng Zhuo et al. Eur Radiol. 2025 Mar.

. 2025 Mar;35(3):1228-1241.

doi: 10.1007/s00330-024-11157-w. Epub 2024 Oct 29.

Authors

Affiliations

¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 18612161210@163.com.
² Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
³ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁴ Department of Radiology, Renmin Hospital, Hubei University of Medicine, Shiyan, China.
⁵ Department of Medical Imaging Product, Neusoft Group Ltd., Shenyang, People's Republic of China.
⁶ Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
⁷ Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China.
⁸ Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
⁹ Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
¹⁰ Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
¹¹ Department of Imaging and Medical Informatics, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Geneva, Switzerland.
¹² Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands.
¹³ Queen Square Institute of Neurology and Center for Medical Image Computing, University College London, London, UK.
¹⁴ Clinical and Technical Support, Philips Healthcare, Beijing, China.
¹⁵ China National Clinical Research Center for Neurological Diseases, Beijing, China.
¹⁶ Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China.
¹⁷ Center for Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
¹⁸ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. liuyaou@bjtth.org.

^# Contributed equally.

PMID: 39470796
DOI: 10.1007/s00330-024-11157-w

Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusion: Spatial abnormality maps can explain heterogeneity in cognitive and physical performance in RRMS and may contribute to stratified management.

Key points: 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.

Keywords: Brain abnormality; Cognition; Deep learning; Disability; Relapsing-remitting multiple sclerosis.

PubMed Disclaimer

Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Yaou Liu. Conflict of interest: The authors of this manuscript declare relationships with the following companies: Philips (G.H.) and Neusoft, Group Ltd (J.W.). F.B. is a consultant for Bayer-Schering, Biogen-Idec, GeNeuro, Ixico, Merck-Serono, Novartis, and Roche. He has received grants, or grants are pending, from the Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) initiative, the Biomedical Research Centre at University College London Hospitals, the Dutch MS Society, ECTRIMS–MAGNIMS, EU-H2020, the Dutch Research Council (NWO), the UK MS Society, and the National Institute for Health Research, University College London. He has received payments for the development of educational presentations from Ixico and his institution from Biogen-Idec and Merck. He is on the editorial board of Radiology, Neuroradiology, Multiple Sclerosis Journal, and Neurology. The remaining authors declare no conflicts of interest. Statistics and biometry: Yaou Liu, Frederik Barkhof, and Sven Haller kindly provided statistical advice for this manuscript. Zhizheng Zhuo (one of the authors) has significant statistical expertise. No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board (Beijing Tiantan Hospital, Capital Medical University) approval was obtained. Study subjects or cohorts overlap: None of the study subjects or cohorts have been previously reported. Methodology: Retrospective Observational Multicenter study

Comment in

On how SAM might help improving personalized treatments in relapsing-remitting multiple sclerosis.
Le NQK. Le NQK. Eur Radiol. 2025 Mar;35(3):1225-1227. doi: 10.1007/s00330-024-11190-9. Epub 2024 Nov 15. Eur Radiol. 2025. PMID: 39545982 No abstract available.

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Comment in

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical