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Comprehensive Imaging Characterization of Colorectal Liver Metastases.

Maclean, D., Tsakok, M., Gleeson, F., Breen, D.J., Goldin, R., Primrose, J., Harris, A. and Franklin, J., 2021. Comprehensive Imaging Characterization of Colorectal Liver Metastases. Frontiers in Oncology, 11, 730854.

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DOI: 10.3389/fonc.2021.730854

Abstract

Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.

Item Type:Article
ISSN:2234-943X
Uncontrolled Keywords:MRI ; colorectal (colon) cancer ; computed tomography ; liver ; metastasis ; radiomic biomarkers
Group:Faculty of Health & Social Sciences
ID Code:36470
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:10 Jan 2022 12:27
Last Modified:10 Jan 2022 12:27

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