Radiogenomics in Colorectal Cancer.

deep learning genomics machine learning oncologic imaging radiogenomics

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
26 Feb 2021
Historique:
received: 16 01 2021
revised: 07 02 2021
accepted: 20 02 2021
entrez: 3 3 2021
pubmed: 4 3 2021
medline: 4 3 2021
Statut: epublish

Résumé

The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features-radiomics and genetic profile modifications-genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

Identifiants

pubmed: 33652647
pii: cancers13050973
doi: 10.3390/cancers13050973
pmc: PMC7956421
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

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Auteurs

Bogdan Badic (B)

National Institute of Health and Medical Research, LaTIM-Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France.

Florent Tixier (F)

National Institute of Health and Medical Research, LaTIM-Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France.

Catherine Cheze Le Rest (C)

National Institute of Health and Medical Research, LaTIM-Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France.
Department of Nuclear Medicine, University Hospital of Poitiers, 86021 Poitiers, France.

Mathieu Hatt (M)

National Institute of Health and Medical Research, LaTIM-Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France.

Dimitris Visvikis (D)

National Institute of Health and Medical Research, LaTIM-Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France.

Classifications MeSH