Bakrania Anita, Joshi Narottam, Zhao Xun, Zheng Gang, Bhat Mamatha
Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
SwaBz Systems Incorporated, Toronto, ON, Canada.
Pharmacol Res. 2023 Mar;189:106706. doi: 10.1016/j.phrs.2023.106706. Epub 2023 Feb 20.
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
肝癌是全球癌症相关死亡的第四大主要原因。在过去十年中，人工智能（AI）领域的突破激发了癌症领域算法的发展。最近越来越多的研究通过诊断图像分析、生物标志物发现和预测个性化临床结果，评估了机器学习（ML）和深度学习（DL）算法在肝癌患者的预筛查、诊断和管理中的应用。尽管这些早期人工智能工具前景广阔，但仍迫切需要解释人工智能的“黑匣子”，并努力实现其部署以确保最终的临床可转化性。某些新兴领域，如用于靶向肝癌治疗的RNA纳米医学，也可能受益于人工智能的应用，特别是在纳米制剂研发方面，因为它们在很大程度上仍依赖冗长的试错实验。在本文中，我们提出了人工智能在肝癌领域的现状以及人工智能在肝癌诊断和管理中面临的挑战。最后，我们讨论了人工智能在肝癌应用中的未来前景，以及如何在纳米医学中采用多学科方法使用人工智能来加速个性化肝癌药物从实验室到临床的转化。
