Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives.

artificial intelligence digestive system tumors machine learning medical imaging non-invasive prediction nuclear medicine positron emission tomography radiomics

Journal

View (Beijing, China)
ISSN: 2688-268X
Titre abrégé: View (Beijing)
Pays: China
ID NLM: 9918248414606676

Informations de publication

Date de publication:
Dec 2023
Historique:
medline: 5 1 2024
pubmed: 5 1 2024
entrez: 5 1 2024
Statut: ppublish

Résumé

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Identifiants

pubmed: 38179181
doi: 10.1002/VIW.20230032
pmc: PMC10766416
pii:
doi:

Types de publication

Journal Article

Langues

eng

Déclaration de conflit d'intérêts

CONFLICT OF INTEREST STATEMENT Weibo Cai declares conflict of interest with the following corporations: Actithera, Inc., Rad Source Technologies, Inc., Portrai, Inc., rTR Technovation Corporation, and Four Health Global Pharmaceuticals Inc. All other authors declare they have no conflicts of interest.

Auteurs

Wenpeng Huang (W)

Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.

Zihao Tao (Z)

Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.

Muhsin H Younis (MH)

Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Weibo Cai (W)

Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Lei Kang (L)

Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.

Classifications MeSH