Background: Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential to improve diagnostic imaging. Population atlases are a commonly used tool in medical imaging to facilitate this. Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, which contain significant anatomical variations.
Method: In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. Using magnetic resonance images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. We furthermore unbias them, and this way obtain a realistic representation of the population. In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys).
Results: Our pipeline effectively generates high-quality, realistic whole-body atlases with clinical applicability. The probabilistic atlases show differences in fat distribution between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects in the atlas space.
Conclusions: With this work, we make the constructed anatomical and label atlases publically available, with the expectation that they will support medical research involving whole-body MR images.
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http://dx.doi.org/10.1038/s43856-024-00670-0 | DOI Listing |
Neurology
February 2025
Department of Advanced Biomedical Sciences, University "Federico II," Naples, Italy.
Background And Objectives: Although multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses typically require advanced MRI sequences not commonly acquired in clinical practice. Using conventional MRI, we assessed cross-sectional and longitudinal structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.
Methods: In this longitudinal monocentric study, 3T structural MRI of pwMS and healthy controls (HC) was retrospectively analyzed.
Ann Med
December 2025
Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, PR China.
Objective: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.
Methods: Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes.
Discov Oncol
January 2025
Department of General Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, People's Republic of China.
Background: Pancreatic ductal adenocarcinoma (PDAC) has a heterogeneous make-up of myeloid cells that influences the therapeutic response and prognosis. However, understanding the myeloid cell at both a genetic and cellular level remains a significant challenge.
Methods: Single-cell RNA sequencing (scRNA-seq) data were downloaded from t the Tumor Immune Single-cell Hub and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database.
Mol Biol Rep
January 2025
Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Background: The methyltransferase gene family is known for its diverse biological functions and critical role in tumorigenesis. This study aimed to identify these family genes in common gastrointestinal (GI) cancers using comprehensive methodologies.
Methods: Gene identification involved analysis of scientific literature and insights from The Cancer Genome Atlas (TCGA) database.
Front Immunol
January 2025
School of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Background: Disturbances in DNA damage repair may lead to cancer. SIRT1, an NAD+-dependent deacetylase, plays a crucial role in maintaining cellular homeostasis through the regulation of processes such as histone posttranslational modifications, DNA repair, and cellular metabolism. However, a comprehensive exploration of SIRT1's involvement in pan-cancer remains lacking.
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