Radiomics in hepatocellular carcinoma: A state-of-the-art review

Yao, Shan; Zheng, Yongliu; Yi, Wei; Jiang, Hanyu; Song, Bin

doi:10.4251/wjgo.v13.i11.1599

Cited by 23 publications

(13 citation statements)

References 104 publications

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“…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.…”

Section: Artificial Intelligencementioning

confidence: 99%

Current Imaging Diagnosis of Hepatocellular Carcinoma

Chartampilas

Rafailidis

Georgopoulou

et al. 2022

Cancers

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Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.

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Section: Artificial Intelligencementioning

confidence: 99%

Current Imaging Diagnosis of Hepatocellular Carcinoma

Chartampilas

Rafailidis

Georgopoulou

et al. 2022

Cancers

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“…The pre-operative prediction of tumor grade is valuable in the prediction of long-term survival as well as therapeutic response. It may even play role in treatment planning, as high-grade tumors may require more aggressive treatment and a wider safety resection margin [ 80 , 81 , 82 , 83 ]. Mao et al [ 84 ] designed an ML model to predict high-grade HCC based on first-order, second-order, higher-order, and shape features derived from arterial and venous phases of triphasic CT utilizing recursive feature elimination and eXtreme Gradient Boosting (XGBoost).…”

Section: Managment Of Hccmentioning

confidence: 99%

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Fahmy

Alksas

Elnakib

et al. 2022

Cancers

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Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

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“…[67] Variation in image quality and software, lack of transparency in model construction with artificial intelligence ("black box" warning), radiologist operator dependency for image segmentation, and lack of large enough cohorts with early-stage HCC, limit the application of radiomics for HCC detection and surveillance. [68,69] Aside from radiomics, there has been interest in developing a more cost-effective imaging techniques with less physical harms, such as an abbreviated MRI. [70] Different abbreviated MRI approaches with and without contrast are being investigated, all of which have the advantage of being simpler, less expensive to perform; and safer due to the use of noncontrast MRI than regular MRI.…”

Section: Future Direction Serum Biomarkersmentioning

confidence: 99%

Current Challenges and Future Direction in Surveillance for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

Cholankeril

El–Serag

2022

Semin Liver Dis

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The burden for hepatocellular carcinoma (HCC) attributed to nonalcoholic fatty liver disease (NAFLD) continues to grow in parallel with rising global trends in obesity. The risk of HCC is elevated among patients with NAFLD-related cirrhosis to a level that justify surveillance based on cost-effectiveness argument. The quality of current evidence for HCC surveillance in all patients with chronic liver disease is poor, and even lower in those with NAFLD. For a lack of more precise risk-stratification tools, current approaches to defining a target population in non-cirrhotic NAFLD are limited to noninvasive tests for liver fibrosis, as a proxy for liver-related morbidity and mortality. Beyond etiology and severity of liver disease, traditional and metabolic risk factors, such as diabetes mellitus, older age, male gender and tobacco smoking, are not enough for HCC risk stratification for surveillance efficacy and effectiveness in NAFLD. There is an association between molecular and genetic factors and HCC risk in NAFLD, and risk models integrating both clinical and genetic factors will be key to personalizing HCC risk. Herein in this review, we discuss concerns regarding defining a target population, surveillance test accuracy, surveillance underuse, and other cost-effective considerations for HCC surveillance in individuals with NAFLD.

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Radiomics in hepatocellular carcinoma: A state-of-the-art review

Cited by 23 publications

References 104 publications

Current Imaging Diagnosis of Hepatocellular Carcinoma

Current Imaging Diagnosis of Hepatocellular Carcinoma

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Current Challenges and Future Direction in Surveillance for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

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