Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound.

ABSTRACT: This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by

SUBMITTER: Turco S 

PROVIDER: S-EPMC9188683 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature