State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.
State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.
复制标题
DOI：
10.3390/diagnostics11071194
复制
发表时间：
2021-06-30
期刊：
影响因子：
--
通讯作者：
Cuocolo R
中科院分区：
文献类型：
--
作者：
Castaldo A;De Lucia DR;Pontillo G;Gatti M;Cocozza S;Ugga L;Cuocolo R
最常见的肝脏恶性肿瘤是肝细胞癌（HCC），它也与高死亡率相关。 HCC 通常是在慢性肝病的情况下发展的，早期诊断以及对高危患者的准确筛查对于这些患者的适当和有效的管理至关重要。虽然 HCC 的影像学特征在诊断阶段已明确，但仍会出现具有挑战性的病例，并且当前的预后和预测模型的准确性有限。放射组学和机器学习 (ML) 提供了解决这些问题的新工具，并可能带来科学突破，从而影响临床实践并改善患者的治疗结果。在这篇综述中，我们将概述这些技术在不同模式和一系列应用的 HCC 成像中的应用。这些包括病变分割、诊断、预后建模和治疗反应预测。最后，讨论了目前阻碍放射组学和机器学习临床应用的局限性，以及推动该领域发展并超越纯粹学术努力的未来必要发展。
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.
登录
查看更多内容
影响因子：
5.9
作者：
Hamm, Charlie A.;Wang, Clinton J.;Letzen, Brian
通讯作者：
Letzen, Brian
基于两阶段多视图学习框架的超声造影计算机辅助诊断肝脏肿瘤
影响因子：
2.1
作者：
Guo, Le-Hang;Wang, Dan;Xu, Hui-Xiong
通讯作者：
Xu, Hui-Xiong
影响因子：
3.3
作者：
Guo, Donghui;Gu, Dongsheng;Zheng, Hong
通讯作者：
Zheng, Hong
影响因子：
2.3
作者：
Bharti, Puja;Mittal, Deepti;Ananthasivan, Rupa
通讯作者：
Ananthasivan, Rupa
影响因子：
3.8
作者：
Han, Xiao
通讯作者：
Han, Xiao