scholarly journals Radiofrequency combined with immunomodulation for hepatocellular carcinoma: State of the art and innovations

2020 ◽  
Vol 26 (17) ◽  
pp. 2040-2048 ◽  
Author(s):  
Adriano Carneiro da Costa ◽  
Mikael Sodergren ◽  
Kumar Jayant ◽  
Fernando Santa Cruz ◽  
Duncan Spalding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art

State-of-the-art ultrasonography of hepatocellular carcinoma

2006 ◽  
Vol 58 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Jae Young Lee ◽  
Byung Ihn Choi ◽  
Joon Koo Han ◽  
Jeong Min Lee ◽  
Se Hyung Kim
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art

Percutaneous treatment of hepatocellular carcinoma: State of the art and innovations

2018 ◽  
Vol 68 (4) ◽  
pp. 783-797 ◽  
Author(s):  
Jean-Charles Nault ◽  
Olivier Sutter ◽  
Pierre Nahon ◽  
Nathalie Ganne-Carrié ◽  
Olivier Séror
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art ◽  
Percutaneous Treatment

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

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1194
Author(s):  
Anna Castaldo ◽  
Davide Raffaele De Lucia ◽  
Giuseppe Pontillo ◽  
Marco Gatti ◽  
Sirio Cocozza ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Patient Outcomes ◽  
State Of The Art ◽  
Effective Management ◽  
Lesion Segmentation ◽  
Liver Malignancy ◽  
Modeling And Prediction ◽  
Imaging Characteristics ◽  
Risk Patients ◽  
Improve Patient

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.

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

2021 ◽  
Vol 13 (11) ◽  
pp. 1599-1615
Author(s):  
Shan Yao ◽  
Zheng Ye ◽  
Yi Wei ◽  
Han-Yu Jiang ◽  
Bin Song
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art

scholarly journals Reply to comments on “State of the art in magnetic resonance imaging of hepatocellular carcinoma”: the role of DWI

2019 ◽  
Vol 53 (3) ◽  
pp. 371-372
Author(s):  
Natally Horvat ◽  
Serena Monti ◽  
Brunna Clemente Oliveira ◽  
Camila Carlos Tavares Rocha ◽  
Romina Grazia Giancipoli ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Hepatocellular Carcinoma ◽  
Magnetic Resonance ◽  
State Of The Art ◽  
Resonance Imaging

State of the art: hepatocellular carcinoma

2014 ◽  
Vol 10 (15s) ◽  
pp. 1-6 ◽  
Author(s):  
Luigi Bolondi
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art
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