2023
DOI: 10.3748/wjg.v29.i1.43
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Current status and future perspectives of radiomics in hepatocellular carcinoma

Abstract: Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable qua… Show more

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Cited by 11 publications
(7 citation statements)
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“…Another important ndings emerge from the XAI analysis. Overall, imaging features extracted from VOIs of the largest liver tumors have consistently proven to be the most relevant variables in our models, in line with previous radiomics studies 5,8,[27][28][29] . However, the most intriguing aspect of our study is the relatively high importance of features derived from non-tumoral VOIs.…”
Section: Discussionsupporting
confidence: 84%
“…Another important ndings emerge from the XAI analysis. Overall, imaging features extracted from VOIs of the largest liver tumors have consistently proven to be the most relevant variables in our models, in line with previous radiomics studies 5,8,[27][28][29] . However, the most intriguing aspect of our study is the relatively high importance of features derived from non-tumoral VOIs.…”
Section: Discussionsupporting
confidence: 84%
“…WJG v29i1: I found the paper “Current status and future perspectives of radiomics in hepatocellular carcinoma” to be very well-conceived and executed[ 1 ].…”
Section: Hot Articles and Insufficient Articlesmentioning
confidence: 99%
“…Thus, a non-invasive additional support might improve the discrimination between benign and malignant LNs. In this contest, radiomics could be a useful imaging tool, there being the expectancy that it will influence treatment decision making and predict patient prognosis based on nodal microarchitecture, microenvironment, and heterogeneity by extracting quantitative features from volumetric LN segmentation [11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%