Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.
HCC
MRI
biomarker
early detection
prognosis
radiogenomics
Journal
Alimentary pharmacology & therapeutics
ISSN: 1365-2036
Titre abrégé: Aliment Pharmacol Ther
Pays: England
ID NLM: 8707234
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
revised:
08
06
2021
received:
10
05
2021
accepted:
25
07
2021
pubmed:
15
8
2021
medline:
18
9
2021
entrez:
14
8
2021
Statut:
ppublish
Résumé
Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.
Sections du résumé
BACKGROUND
Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology.
AIMS
To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation.
METHODS
We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies.
RESULTS
A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard.
CONCLUSIONS
Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.
Identifiants
pubmed: 34390014
doi: 10.1111/apt.16563
pmc: PMC8435007
mid: NIHMS1728166
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Review
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
890-901Subventions
Organisme : NIDDK NIH HHS
ID : T32 DK062708
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA230694
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA230669
Pays : United States
Informations de copyright
© 2021 John Wiley & Sons Ltd.
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