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

1
Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
2
Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy
3
Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Federica Vernuccio
Diagnostics 2021, 11(7), 1194; https://doi.org/10.3390/diagnostics11071194
Received: 19 May 2021 / Revised: 24 June 2021 / Accepted: 24 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Advances in Diagnostic Medical Imaging)
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. View Full-Text
Keywords: hepatocellular carcinoma; imaging; radiomics; machine learning; deep learning hepatocellular carcinoma; imaging; radiomics; machine learning; deep learning
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MDPI and ACS Style

Castaldo, A.; De Lucia, D.R.; Pontillo, G.; Gatti, M.; Cocozza, S.; Ugga, L.; Cuocolo, R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics 2021, 11, 1194. https://doi.org/10.3390/diagnostics11071194

AMA Style

Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics. 2021; 11(7):1194. https://doi.org/10.3390/diagnostics11071194

Chicago/Turabian Style

Castaldo, Anna, Davide R. De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, and Renato Cuocolo. 2021. "State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma" Diagnostics 11, no. 7: 1194. https://doi.org/10.3390/diagnostics11071194

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