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.
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.
Authors: Christian B van der Pol; Christopher S Lim; Claude B Sirlin; Trevor A McGrath; Jean-Paul Salameh; Mustafa R Bashir; An Tang; Amit G Singal; Andreu F Costa; Kathryn Fowler; Matthew D F McInnes Journal: Gastroenterology Date: 2018-11-13 Impact factor: 22.682
Authors: Y Asayama; A Nishie; K Ishigami; Y Ushijima; Y Takayama; D Okamoto; N Fujita; K Morita; M Obara; H Honda Journal: Clin Radiol Date: 2016-03-02 Impact factor: 2.350
Authors: Michael Schwier; Joost van Griethuysen; Mark G Vangel; Steve Pieper; Sharon Peled; Clare Tempany; Hugo J W L Aerts; Ron Kikinis; Fiona M Fennessy; Andriy Fedorov Journal: Sci Rep Date: 2019-07-01 Impact factor: 4.379
Authors: Colin Dunn; Bo Lin; Nicole E Rich; Madhukar S Patel; Purva Gopal; Amit G Singal Journal: Clin Gastroenterol Hepatol Date: 2022-03-17 Impact factor: 13.576
Authors: Lei Ye; Yuntian Chen; Hui Xu; Zhaoxiang Wang; Haixia Li; Jin Qi; Jing Wang; Jin Yao; Jiaming Liu; Bin Song Journal: Front Cell Dev Biol Date: 2022-02-25