Journal of Cancer Research and Immuno-Oncology
Open Access

Primary Liver Cancer of Radiomics and Radiogenomics

Dal Buono^{* *}Correspondence: Dal Buono, Department of Radiology, Università Vita-Salute San Raffaele, Milan, Italy, Email:

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Description

Modern oncology relies heavily on clinical imaging techniques including Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI). In some conditions, such hepatocellular carcinoma, the Liver Imaging Reporting and Data System can replace the necessity for a tissue. The second most common cause of cancer-related death worldwide, liver cancer has a dismal prognosis despite improvements in the diagnostic protocol for the disease. The diverse biologic behaviour of liver tumours is also noted as affecting patients' survival, and tumoral heterogeneity can be a significant indicator of a patient's likelihood of having a positive clinical response to treatment. As a result, the trend in liver cancer diagnosis and therapy is shifting to be based on the biological and genomic characteristics of the tumours, and imaging can be a technique to identify these characteristics.

The majority of imaging data published from radiologic investigations contain qualitative or descriptive aspects as opposed to serologic markers. Additionally, there is still a problem with interobserver variability when radiologists characterise imaging findings. Recently, new research techniques have been developed to examine imaging biomarkers through objective and quantitative analyses of medical images, including radiologic studies. This academic field focuses on the relationship between imaging parameters and patient data, and several studies have been published in a variety of carcinomas. Although numerous attempts have been undertaken and presented at the 2017 annual conference of the Radiological Society of North America (RSNA), this cutting-edge technique is still in its infancy, particularly in the research on liver cancer. This article focuses on radiomics and radiogenomics' (or imaging genomics') early clinical applications for primary liver malignancies such HCC and intrahepatic cholangiocarcinoma.

What do imaging genomics, radiogenomics, and radiomics mean?

A study method called radiomics examines the relationship between clinical outcomes and quantitative radiologic data that has been extracted from medical pictures. Additionally, radiogenomics (also known as imaging genomics) seeks to establish connections between semantic and quantitative image data and genome and molecular measurements in order to create association maps that can be connected to clinical outcomes or other outcomes-related variables. The discovery of an objective method to convert the imaging features into digital data, which is crucial for ensuring reproducibility, is a requirement for both radiomics and radiogenomics. The typical radiomics workflow entails the following steps: 1) image acquisition, 2) region of interest identification (either manually or automatically), 3) segmentation of the region of interest (automatically, semiautomatically, or manually), 4) extraction of imaging features, and 5) data mining to create a model to predict clinical outcomes. For radiomics and radiogenomics, it is possible to retrospectively examine the medical images stored in the hospital's imaging server. The more imaging databases have enough data, the more precise predictive radiomic models may be created. However, the picture features and outcomes of predictive models may be impacted by the unpredictability of imaging quality and technical parameters for image acquisition.

Liver tumours' biologic and genomic features

Hepatocellular carcinoma is the most typical primary liver cancer. It is well recognised to be a silent killer with few signs in the early stages of the disease, typically developing in conjunction with hepatitis infections and cirrhosis leading to end-stage liver disease, as well as mostly unidentified hereditary tumour factors. The histological and molecular properties of the tumour are not taken into account by current staging systems, such as the Barcelona Clinic Liver Cancer staging system. Recent research indicates that HCCs show both intra- and interindividual genetic variability. Researchers have discovered that patient prognosis is related to pathologic features such as histologic grade of tumour, microvessel density, Microscopic Vascular Invasion (MVI), and epithelial-mesenchymal transition in addition to tumour size and multiplicity. Predicting biologic behaviour of HCC is crucial for an Effective Treatment (EMT).

An important predictor of HCC is its histologic grade, which takes into account both cellular and structural characteristics. A meta-analysis of 114 linked articles found a negative correlation between poor tumour differentiation and prognosis. A nonstandard grade distribution was the main cause of the limitation of divergences and errors. An important prognostic marker is microvessel density, which enables semi-quantitative evaluation of tumour neovascularization (CD31, CD34, and von Willebrand Factor). Hypoxia-Inducible Factors (HIFs) and Vascular Endothelial Growth Factors (VEGFs) are additional poor prognostic indicators linked to decreased overall survival and recurrencefree survival following tumour removal.