MRI Measures of Fat Distribution and Risk of Cancer.
Cancer epidemiology, biomarkers & prevention a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology(2025)
Albert Einstein College of Medicine
Abstract
BACKGROUND:Excess adiposity has been associated with an increased risk of several types of cancer. The relationship between fat tissue distribution in the body and these outcomes is less well known. Using data from the UK Biobank imaging substudy, we evaluated the prospective relationship between MRI-derived measurements of adipose tissue distribution and the risk of the major site-specific cancers associated with obesity. METHODS:Between 2014 and 2023, MRI measurements on adipose tissue distribution and volume were obtained from 49,044 (52.2% women) cancer-free UK Biobank participants. Quantitative MRI data included volumes of visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT), total abdominal fat/height squared (TAT/h2), and muscle fat infiltration (MFI). Cox proportional hazard models adjusted for cancer-specific risk factors were used to generate HRs and 95% confidence intervals. RESULTS:Incident cancer cases of the breast (N = 179), endometrium (n = 30), colorectum (n = 145), and kidney (n = 50) were ascertained over a median follow-up of 4.5 years. In women, VAT, TAT/h2, and MFI were positively associated with a risk of postmenopausal breast cancer, and ASAT was associated with an increased risk of endometrial cancer. In men, VAT and TAT/h2 were positively associated with a risk of colorectal cancer, whereas ASAT was associated with an increased risk of kidney cancer. CONCLUSIONS:The present study showed that increasing volumes of VAT, ASAT, and MFI were associated with cancers at specific organ sites, indicating a potential role for adipose tissue distribution in influencing cancer risk. IMPACT:Both visceral and subcutaneous fat may have an impact on the risk of certain cancers.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest