Radiomics for liver tumours

Abstract

Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

“…Liver is one of the main sites of metastasis [25] and hepatocellular carcinoma (HCC) is the most common primary tumor, representing the second leading cause of death in oncological patients and the first in patients affected by cirrhosis [25,132]. In recent years, many studies have been looking for possible applications of radiomics in the study of hepatic lesions [133][134][135][136]. Nowadays, many applications have been consolidated: from early diagnosis to posttreatment evaluation and prognosis predictions [137].…”

Development of a Predictive Immune-Related Gene Signature Associated With Hepatocellular Carcinoma Patient Prognosis

“… 5 , 6 As it can eliminate any underlying disease while maintaining organ function, hepatic transplantation is the optimal treatment for HCC patients. 7 , 8 However, the number of livers available for transplantation is limited, and this treatment can be expensive and associated with risks of immune-mediated transplant rejection. Important, such transplantation often fails to improve patient quality of life or to prolong survival.…”

An Overview of Artificial Intelligence in Oncology

“…Radiomics methods can inform models that successfully predict treatment response and/or side effects from cancer treatments [ 23 ]. There is a variety of cancer types where radiomics can be applied such as liver, brain, and lung tumors [ 24 , 25 ]. Deep learning using radiomic features from brain MRI has the ability to differentiate brain gliomas from brain metastasis with similar performance to trained neuroradiologists [ 26 ] Monitoring: the aforementioned techniques can be used to monitor a particular lesion (e.g.…”