Radiomics of hepatocellular carcinoma
Sara Lewis, Stefanie Hectors, Bachir Taouli
Springer Science+Business Media, LLC, part of Springer Nature 2020
Radiomics is defined as the quantitative extraction, analysis and modeling of a large amount features from medical images (mostly CT and MRI images, but also US and PET), in relation to prediction targets, such as clinical end-points, and pathological and genomic features.
Hepatocellular carcinoma (HCC), the third leading cause of cancer-related death in the US and the fourth worldwide, is a heterogeneous and therapy-resistant disease. The HCC characterization at the individual patient level is urgently needed [1, 2, 3].
The aim of the authors is to promote the use of radiomics in clinical practice in order to obtain, using non-invasive methods, information about the tumor, reflective of HCC heterogeneity and aggressiveness, to stratify patients to a personalized therapy.
Several studies have demonstrated the validity of radiomics in the recognition of many HCC features: from histopathological characterization to the prediction of response to therapy, from the risk of recurrence to the prediction of patient’s outcome; all this succeeding in overcoming the limits of invasiveness and of small sample size of liver biopsy.
Among the main characteristics of HCC detected by radiomics, has been proposed the presence of microvascular invasion (MVI, defined by the invasion of tumor cells into a vascular space lined by endothelium), for which specific features have been elaborated: the “radiogenomic venous invasion” (RVI) score and the “two-trait predictor of venous invasion” (TTPVI). The role of radiomics in the detection of MVI is crucial, as the MVI has been reported as the strongest independent predictor of early tumor recurrence and poor prognosis [4, 5, 6].
Genetic and immune phenotypes also contribute to define the prognosis of patient affected by HCC: several studies have shown the validity of radiomics in the detection of these tumor features. In particular, a study involving 39 patients has proven the possibility, starting from qualitative CT and MRI imaging data, of demonstrating a significative association of these phenotypes with gene signatures of aggressive HCC phenotype, with odds ratios (OR) ranging from 4.44 to 12.73 (p < 0.045) [7, 8, 9].
Radiomics plays a role throughout all the diagnostic and therapeutic process of HCC, as it can be used to predict the tumor’s response to therapy. Kim et al. have demonstrated the superiority of a combined model in which are associated pre-treatment radiomics features and clinical factors of the patients (HR 19.88; p < 0.0001) in predicting patient survival, compared to either the clinical or imaging data alone [10].
Finally, outcome has been investigated in patients after surgical and non-surgical therapies. In particular, recurrence of disease after surgery is a frequent occurrence (from 31,6 to 40% of patients), depending on whether they perform a liver transplant or a partial resection, respectively [11, 12]. In addition, a pre-operative MRI imaging study involving 100 patients has identified the radiomics feature most predictive of ER after partial resection, regardless of the HCC size: the entropy, the manifestation of tumor heterogeneity [11, 12, 13].
Nowadays there is an increasing evidence that encourages the implementation of radiomics in all stages of the diagnostic and therapeutic process of HCC; although its use is currently limited by several factors, such as the need for dedicated softwares and trainings, which result in additional costs, and the lack of standardization in the various stages of radiomics studies.
In conclusion, a greater diffusion of radiomics in clinical practice could translate into a concrete benefit for the patients in terms of outcome, and its use could be considered also as an instrument for early diagnosis.
References:
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- Taouli, B., et al., Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results. Eur Radiol, 2017. 27(11): p. 4472-4481.
- Kim, J., et al., Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics. AJR Am J Roentgenol, 2018. 211(5): p. 1026-1034.
- Shah, S.A., et al., Recurrence after liver resection for hepatocellular carcinoma: risk factors, treatment, and outcomes. Surgery, 2007. 141(3): p. 330-9.
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Dr. Caterina Di Manna is a first-year radiology resident on the “Sapienza, University of Rome” training scheme in Italy. She completed her undergraduate medical degree at “Sapienza, University of Rome” in 2019. She joined the Medical Imaging Department in 2021 where she is undertaking training in diagnostic and interventional radiology.
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