Evidence map›Paper›PMID 42902214›Full record

ArticleBMC musculoskeletal disorders2026

Correlation of whole-body fat content and its distribution with lumbar fat content under different age, gender and BMI conditions: quantitative analysis of MRI based on deep learning.

Junjie Fang, Jiazhen Zhu, Aie Liu, Chuanli Cheng, Chao Zou, Aizhu Sheng, Fang Gan, Bin Chen, Guangyu Tang

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Article in BMC musculoskeletal disorders, 2026. 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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5 · Who and what money

Authors and funding

9 authors.

Junjie FangDepartment of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No.301 Yangchang Road, Shanghai, 200072, China.
Jiazhen ZhuDepartment of Radiotherapy Technology, Ningbo No.2 Hospital, Ningbo, Zhejiang, 315010, China.
Aie LiuDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, China.
Chuanli ChengPaul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Chao ZouPaul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Aizhu ShengDepartment of Radiology, Ningbo No.2 Hospital, No. 41 Northwest Street, Haishu District, Ningbo, Zhejiang, 315010, China.
Fang GanDepartment of Radiology, Ningbo No.2 Hospital, No. 41 Northwest Street, Haishu District, Ningbo, Zhejiang, 315010, China.
Bin ChenDepartment of Radiology, Ningbo No.2 Hospital, No. 41 Northwest Street, Haishu District, Ningbo, Zhejiang, 315010, China. binbinchen@126.com.
Guangyu TangDepartment of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No.301 Yangchang Road, Shanghai, 200072, China. tgy17@tongji.edu.cn.

Funding

National Innovation Center for Advanced Medical Devices NMED2021MS-01-004Ningbo Clinical Research Center for Medical Imaging No. 2021L003Project of National Key Clinical Specialty (Department of Medical Imaging） No. 2024017Scientific Research Fund of Zhejiang Provincial Education Department Y202249267
6 · The paper itself

Abstract

backgroundThe correlation between abnormal distribution of systemic fat deposition and fat content in the vertebral body remains unclear, and AI segmentation offers novel approach for large-scale quantitative analysis.

objectiveTo explore the effects of gender, age, and body mass index on the correlation between whole-body fat content/distribution and lumbar spine fat content using deep learning based MRI quantification technology.

methodsOne hundred thirteen volunteers (63 males/50 females; 47.7 ± 13.3 years) who underwent coronal whole-body and lumbar sagittal MRI examination were enrolled. Through automatic segmentation and quantification, total adipose tissue (TAT), body-fat percentage (BF%), liver fat fraction (LFF), subcutaneous fat fraction (SAT/TAT), internal fat fraction (IAT/TAT), abdominal subcutaneous/visceral fat ratio (ASVFR), erector spinae-multifidus fat fraction (ESFF), psoas major fat fraction (PMFF) and lumbar vertebral fat fraction (VFF) were obtained. Pearson correlation was then used for stratified analysis based on sex, age (≤ 45, 46-59, ≥ 60 years) and BMI (underweight/normal/over-weight/obese). All preset variables were entered into a Lasso model (10-fold cross-validation) to identify independent VFF determinants and to calculate R² and the root-mean-square error (RMSE).

resultsVFF correlated moderately with ESFF and PMFF (r_

conclusionParavertebral muscle fat infiltration and visceral-fat predominance are associated with higher lumbar marrow fat accumulation, which tends to be more effective in the female, elderly and overweight groups. The constructed multi-variable model may provide a quantitative imaging framework for future studies investigating vertebral marrow fat and bone health.

Indexed as

Adipose TissueBody Fat DistributionBody Mass IndexDeep LearningLumbar VertebraeMagnetic Resonance ImagingAdultAge FactorsFemaleHumansMaleMiddle AgedSex FactorsWhole Body ImagingDeep learningLumbar vertebral fat fractionMRIParavertebral muscle fat infiltrationWhole-body fat distribution

Identifiers

PMID42902214
PMCPMC13685091

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