Evidence map›Paper›PMID 40121488›Full record

ArticleBMC musculoskeletal disorders2025

Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.

Taiya Chen, Haoran Zhu, Yingyi Hu, Yang Huang, Wengan He, Yizhen Luo, Zeqi Wu, Diangang Fang, Longwei Sun, Hongwu Zeng and 1 more

Abstract read
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In one paragraph

Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Taiya Chen *Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.
Haoran Zhu *School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Yingyi HuDepartment of Radiology, Shenzhen Children's Hospital, China Medical University, Shenzhen, China.
Yang HuangDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
Wengan HeShenzhen ZhenData Intelligent Technology Co., Ltd, Shenzhen, China.
Yizhen LuoDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
Zeqi WuDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
Diangang FangDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
Longwei SunDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
Hongwu ZengDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China. homerzeng@126.com.
Zhiyong LiDepartment of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China. li_33@126.com.

Funding

Sanming Project of Medicine in Shenzhen No.SZSM202011005Shenzhen Municipal Science and Technology Plan Project No.JCYJ20230807093815031
6 · The paper itself

Abstract

objectivesDuchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-weighted Dixon sequences to increase the accuracy of distinguishing DMD and BMD in the early disease stage.

methodsWe retrospectively analysed MRI data from 62 patients aged 36-60 months with muscular dystrophy, including 41 with DMD and 21 with BMD. Radiomic features were extracted from in-phase, opposed-phase, water, fat, and postprocessed fat fraction images. We employed a deep learning segmentation method to segment regions of interest automatically. Feature selection included the Mann‒Whitney U test for identifying significant features, Pearson correlation analysis to remove collinear features, and the LASSO regression method to select features with nonzero coefficients. These selected features were then used in various machine learning algorithms to construct the classification model, and their diagnostic performance was compared.

resultsOur proposed radiomic and machine learning methods effectively distinguished early DMD and BMD. The machine learning models significantly outperformed the radiologists in terms of accuracy (81.2-90.6% compared with 69.4%), specificity (71.0-86.0% compared with 19.0%), and F1 score (85.2-92.6% compared with 80.5%), while maintaining relatively high sensitivity (85.6-95.0% compared with 95.1%).

conclusionRadiomics based on Dixon sequences combined with machine learning methods can effectively distinguish between DMD and BMD in the early stages, providing a new and effective tool for the early diagnosis of these muscular dystrophies. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Machine LearningMagnetic Resonance ImagingMuscular Dystrophy, DuchenneChild, PreschoolDiagnosis, DifferentialEarly DiagnosisFemaleHumansMaleRadiomicsRetrospective StudiesBecker muscular dystrophyDuchenne muscular dystrophyEarly diagnosisMachine learningMuscle MRIRadiomics

Identifiers

PMID40121488
PMCPMC11929326

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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.