Systems medicine: total body positron emission tomography meets omics

Omics imaging has emerged as an interdisciplinary field focused on integrating data collected from biomedical images and omics analysis. Historically, there is precedent for omics imaging to serve as a superior model for prediction of human diseases, including brain disorders and cancer, versus single technique models (e.g., only imaging data or only omics data). Most of the previous studies in the field of omics imaging have focused on single organ analysis. However, the advent of total-body Positron Emission Tomography (PET) imaging for clinical use has the potential to transform this landscape by enabling high sensitivity or high throughput multi-organ analysis with methodologies previously established for analysis of omics datasets, such as connectome and pathway analysis tools. Conversely, traditional omics analysis, which lack spatial and structural multi-organ information, can benefit from total-body PET imaging of molecular targets in vivo across multiple organs in humans. In this commentary, the importance of whole-person research enabled by total-body PET, integration of total-body PET with omics techniques and examples successful case studies of imaging omics are described. Although the field of imaging omics is relatively new, discoveries already enabled by this field provide seminal evidence of its importance to advance human disease diagnosis, prognosis and treatment.

Authors

Institutions

Publication Details

Journal
British Journal of Radiology
Published
2026-02-26
DOI
https://doi.org/10.1093/bjr/tqag050
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Systems medicine: total body positron emission tomography meets omics

Angus Jacobs, Athena Chan, Adriana A. S. Tavares
British Journal of Radiology
Medical Imaging Techniques and Applications
article

Systems medicine: total body positron emission tomography meets omics

Angus Jacobs, Athena Chan, Adriana A. S. Tavares
article en

Abstract

Omics imaging has emerged as an interdisciplinary field focused on integrating data collected from biomedical images and omics analysis. Historically, there is precedent for omics imaging to serve as a superior model for prediction of human diseases, including brain disorders and cancer, versus single technique models (e.g., only imaging data or only omics data). Most of the previous studies in the field of omics imaging have focused on single organ analysis. However, the advent of total-body Positron Emission Tomography (PET) imaging for clinical use has the potential to transform this landscape by enabling high sensitivity or high throughput multi-organ analysis with methodologies previously established for analysis of omics datasets, such as connectome and pathway analysis tools. Conversely, traditional omics analysis, which lack spatial and structural multi-organ information, can benefit from total-body PET imaging of molecular targets in vivo across multiple organs in humans. In this commentary, the importance of whole-person research enabled by total-body PET, integration of total-body PET with omics techniques and examples successful case studies of imaging omics are described. Although the field of imaging omics is relatively new, discoveries already enabled by this field provide seminal evidence of its importance to advance human disease diagnosis, prognosis and treatment.

British Journal of Radiology
University of Edinburgh (GB)
Openalex Percentile: Top 43%
Medical Imaging Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.