Precision diagnostics based on machine learning-derived imaging signatures.
Journal
Magnetic resonance imaging
ISSN: 1873-5894
Titre abrégé: Magn Reson Imaging
Pays: Netherlands
ID NLM: 8214883
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
07
12
2018
revised:
24
04
2019
accepted:
29
04
2019
pubmed:
10
5
2019
medline:
6
5
2020
entrez:
10
5
2019
Statut:
ppublish
Résumé
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
Identifiants
pubmed: 31071473
pii: S0730-725X(18)30630-1
doi: 10.1016/j.mri.2019.04.012
pmc: PMC6832825
mid: NIHMS1531084
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
49-61Subventions
Organisme : NIBIB NIH HHS
ID : R01 EB022573
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA223816
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH112070
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA197000
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG054409
Pays : United States
Organisme : NIH HHS
ID : S10 OD023495
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS042645
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA189523
Pays : United States
Organisme : NCI NIH HHS
ID : R21 CA223358
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA161749
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001878
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.
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