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.
Highlights
Colorectal cancer (CRC) remains the second leading cause of cancer-related death in the developed world [1]
We outline how imaging techniques could reproducibly characterize the histopathological features of Colorectal liver metastases (CRLM), with several matched imaging and histology examples to illustrate these features and discuss the oncological relevance of these features
Hepatocyte-specific contrast agents could be useful in determining a ‘replacement’ growth pattern. This is similar to how microvascular invasion (MVI) can be identified with hepatocellular carcinoma (HCC) as reproducible hypoperfusion of liver parenchyma surrounding the tumour due to subtle tumour infiltration [28]
Summary
Drew Maclean 1,2*, Maria Tsakok 3, Fergus Gleeson 4, David J. Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. This information is usually only available after resection, and of limited value in treatment planning. 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 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).
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