Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases

• Published in: World journal of gastroenterology : WJG Vol. 29; no. 3; pp. 521 - 535
• Main Authors: Caruso, Martina, Stanzione, Arnaldo, Prinster, Anna, Pizzuti, Laura Micol, Brunetti, Arturo, Maurea, Simone, Mainenti, Pier Paolo
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 21.01.2023
• Subjects: Colorectal Neoplasms - diagnostic imaging; Colorectal Neoplasms - pathology; Colorectal Neoplasms - therapy; Diffusion Magnetic Resonance Imaging; Humans; Liver Neoplasms - drug therapy; Liver Neoplasms - therapy; Magnetic Resonance Imaging; Minireviews; Tomography, X-Ray Computed - methods; Prediction response; Computed tomography; Magnetic resonance imaging; Parametric imaging; Positron emission tomography; Colorectal cancer metastases
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v29.i3.521

In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.