Evidence map›Paper›PMID 41317205›Full record

ArticleJournal of neurology2025

Nomograms integrating cortical morphometric metrics with 3D multi-parametric MRI radiomics for predicting disability progression and cognitive worsening in relapsing-remitting multiple sclerosis: a multi-center validation study.

Zhuo Wang, Shihang Li, Jinrui Liu, Yujing Li, Mingsong Tang, Jun Wang, Wenjing Ren, Jia Guo, Zhuo Shao, Jingqi Jiang and 8 more

Abstract readMulticenter StudyValidation Study
PubMed Publisher
In one paragraph

Article in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

18 authors.

Zhuo Wang *Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Shihang Li *Department of Radiology, People's Hospital of Inner Mongolia Autonomous Region, Hohhot, China.
Jinrui LiuDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Yujing LiDepartment of Magnetic Resonance, The People's Hospital of Baoan Shenzhen, Shenzhen, Guangdong, China.
Mingsong TangDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Jun WangDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Wenjing RenDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Jia GuoDepartment of Neurology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
Zhuo ShaoDepartment of Mathematics and Statistics, Lanzhou University, Lanzhou, China.
Jingqi JiangDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Fei JiaDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Hao LiDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Xia XuDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Guangyao LiuDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Yanli JiangDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Yucheng DingDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China.
Kai AiDepartment of Clinical and Technical Support, Philips Healthcare, Xi'an, China.
Jing ZhangDepartment of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Chengguan District, Lanzhou, 730030, China. ery_zhangjing@lzu.edu.cn.ORCID http://orcid.org/0000-0002-1678-5688

Funding

Gansu Province Clinical Research Center for Functional and Molecular Imaging 21JR7RA438Key R&D Plan of Gansu Province 23YFFA0041Lanzhou University Second Hospital "Cuiying Technology Innovation Plan" Applied Basic Research Project CY2022-MS-B 01National Natural Science Foundation of China 81960309National Natural Science Foundation of China 82160326Science and Technology Program Project of Lanzhou Municipal Science and Technology Bureau 2023-2-1
6 · The paper itself

Abstract

objectiveTo develop individualized nomograms integrating cortical morphometric measures with 3D multi-parametric MRI radiomics to predict disability progression (DP) and cognitive worsening (CW) in patients with relapsing-remitting multiple sclerosis (RRMS). MATERIALS AND

methodsIn this multicenter study, 191 RRMS patients from two centers were divided into internal (training and validation sets, n = 158) and external validation (n = 33) sets. All patients underwent clinical and neuropsychological evaluations at both baseline and 2-year follow-up visits. Cortical morphometric metrics were extracted from 3D T1W images, with radiomics features were assessed within MS plaques on 3D DIR, 3D FLAIR, and 3D T1W images. Four models-clinical-only, radiomics-only, cortical morphometric-only, and a combined model-were developed. A nomogram was developed based on a multivariable logistic regression model to provide individualized probability estimates of DP and CW. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

resultsThe combined nomogram outperformed models using clinical, radiomic, or cortical morphometric features alone in predicting DP, achieving an area under the curve (AUC) (95% confidence interval [CI]) of 0.950 (0.878-0.994) in the internal cohort and 0.904 (0.781-0.987) in the external cohort. Similarly, the nomogram for CW demonstrated excellent performance, with AUCs of 0.916 (0.831-0.984) and 0.889 (0.752-0.981) in the respective cohorts. Decision curve analysis confirmed the clinical utility of the nomograms.

conclusionCortical atrophy, reduced morphological complexity, and high heterogeneity of MS lesions play significant roles in explaining DP and CW in MS. Nomograms integrating clinical indicators, cortical morphometric features, and 3D multi-parametric MRI radiomics, shows potential as a clinical tool for predicting disease progression, facilitating individualized management in RRMS patients.

Indexed as

Cerebral CortexCognitive DysfunctionMultiparametric Magnetic Resonance ImagingMultiple Sclerosis, Relapsing-RemittingNomogramsAdultDisease ProgressionFemaleHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedRadiomicsYoung AdultCognitive worseningDisability progressionMultiple sclerosisRadiomicsSurface-based morphometry

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.