Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.

IF 3 4区 医学 Q1 Medicine Translational gastroenterology and hepatology Pub Date : 2022-10-25 eCollection Date: 2022-01-01 DOI:10.21037/tgh-20-242
Michal Kawka, Aleksander Dawidziuk, Long R Jiao, Tamara M H Gall
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引用次数: 9

Abstract

Hepatocellular carcinoma (HCC) is a significant cause of morbidity and mortality worldwide. Despite significant advancements in detection and treatment of HCC, its management remains a challenge. Artificial intelligence (AI) has played a role in medicine for several decades, however, clinically applicable AI-driven solutions have only started to emerge, due to gradual improvement in sensitivity and specificity of AI, and implementation of convoluted neural networks. A review of the existing literature has been conducted to determine the role of AI in HCC, and three main domains were identified in the search: detection, characterisation and prediction. Implementation of AI models into detection of HCC has immense potential, as AI excels at analysis and integration of large datasets. The use of biomarkers, with the rise of '-omics', can revolutionise the detection of HCC. Tumour characterisation (differentiation between benign masses, HCC, and other malignant tumours, as well as staging and grading) using AI was shown to be superior to classical statistical methods, based on radiological and pathological images. Finally, AI solutions for predicting treatment outcomes and survival emerged in recent years with the potential to shape future HCC guidelines. These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice. However, AI research on HCC has several limitations, hindering its clinical adoption; small sample size, single-centre data collection, lack of collaboration and transparency, lack of external validation, and model overfitting all results in low generalisability of the results that currently exist. AI has potential to revolutionise detection, characterisation and prediction of HCC, however, for AI solutions to reach widespread clinical adoption, interdisciplinary collaboration is needed, to foster an environment in which AI solutions can be further improved, validated and included in treatment algorithms. In conclusion, AI has a multifaceted role in HCC across all aspects of the disease and its importance can increase in the near future, as more sophisticated technologies emerge.

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人工智能在肝细胞癌的检测、表征和预测中的应用综述。
肝细胞癌(HCC)是世界范围内发病率和死亡率的重要原因。尽管在HCC的检测和治疗方面取得了重大进展,但其管理仍然是一个挑战。人工智能(AI)在医学领域已经发挥了几十年的作用,但由于人工智能的敏感性和特异性逐渐提高,以及卷积神经网络的实施,临床应用的人工智能驱动的解决方案才刚刚开始出现。对现有文献进行了回顾,以确定AI在HCC中的作用,并在搜索中确定了三个主要领域:检测,表征和预测。将人工智能模型应用于HCC检测具有巨大的潜力,因为人工智能擅长分析和整合大型数据集。随着“组学”的兴起,生物标志物的使用可以彻底改变HCC的检测。基于放射学和病理图像,使用人工智能进行肿瘤表征(良性肿块、HCC和其他恶性肿瘤的区分,以及分期和分级)优于经典的统计方法。最后,近年来出现了用于预测治疗结果和生存的人工智能解决方案,有可能塑造未来的HCC指南。这些基于临床数据和图像提取特征相结合的人工智能算法也可以支持临床决策,特别是治疗选择。然而,人工智能对HCC的研究存在一些局限性,阻碍了其临床应用;小样本量、单中心数据收集、缺乏协作和透明度、缺乏外部验证和模型过拟合,都导致目前存在的结果的低普遍性。人工智能有可能彻底改变HCC的检测、表征和预测,然而,为了使人工智能解决方案在临床得到广泛采用,需要跨学科合作,以营造一个环境,使人工智能解决方案可以进一步改进、验证并纳入治疗算法。总之,人工智能在HCC中具有多方面的作用,涉及疾病的各个方面,随着更复杂技术的出现,其重要性在不久的将来会增加。
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来源期刊
CiteScore
8.20
自引率
0.00%
发文量
1
期刊介绍: Translational Gastroenterology and Hepatology (Transl Gastroenterol Hepatol; TGH; Online ISSN 2415-1289) is an open-access, peer-reviewed online journal that focuses on cutting-edge findings in the field of translational research in gastroenterology and hepatology and provides current and practical information on diagnosis, prevention and clinical investigations of gastrointestinal, pancreas, gallbladder and hepatic diseases. Specific areas of interest include, but not limited to, multimodality therapy, biomarkers, imaging, biology, pathology, and technical advances related to gastrointestinal and hepatic diseases. Contributions pertinent to gastroenterology and hepatology are also included from related fields such as nutrition, surgery, public health, human genetics, basic sciences, education, sociology, and nursing.
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