State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Abstract

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

Current Imaging Diagnosis of Hepatocellular Carcinoma

“…The research on Artificial Intelligence (AI) has greatly expanded in the last few years. The application of AI in HCC imaging has demonstrated promising results regarding differentiation from other lesions, prediction of grading and microvascular invasion, identification of specific molecular profile, prediction of response to treatment or post-operative recurrence, and guidance on treatment selection [ 178 , 179 , 180 ]. However, validation of these results in larger, prospective, multicenter studies is required in the years to come and before AI proves its clinical utility.…”

The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment

“…Each item contributes to a final percentage score for the paper, allowing for a quantitative assessment of methodological quality. The value of the RQS is also confirmed by its use across various topics in the recent literature [ 18 – 20 ]. An additional advantage of the RQS is the possibility to use its final score to perform statistical analyses with other variables.…”

Imaging diagnosis of hepatocellular carcinoma: Future directions with special emphasis on hepatobiliary magnetic resonance imaging and contrast-enhanced ultrasound

“…Detailed information on deep learning and convolutional neural network is available in another focused review [ 118 ]. Regarding HCC imaging, deep learning has been studied not only for diagnosis but also for other fields such as segmentation, pathologic grading, and prediction of treatment response and prognosis [ 120 ]. In this article, we briefly introduce several studies on deep learning for the detection and diagnosis of HCC.…”