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Abstract Details
Systematic review: Radiomics for the diagnosis and prognosis of hepatocellular carcinoma
Aliment Pharmacol Ther. 2021 Aug 12 doi: 10.1111/apt.16563. Online ahead of print.
Emily Harding-Theobald1, Jeremy Louissaint1, Bharat Maraj1, Edward Cuaresma1, Whitney Townsend2, Mishal Mendiratta-Lala3, Amit G Singal4, Grace L Su1, Anna S Lok1, Neehar D Parikh1
Author information
1Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA.
2Division of Library Sciences, University of Michigan, Ann Arbor, MI, USA.
3Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
4Division of Digestive and Liver Diseases, University of Texas Southwestern, Dallas, TX, USA.
Abstract
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.