人工智能在肝癌诊治中的应用进展

Application of artificial intelligence in diagnosis and treatment of liver cancer

目的了解人工智能(AI)及其在肝癌管理中的研究进展情况。方法复习近年来关于AI在肝癌诊断和治疗中研究进展的相关文献并加以综述。结果在诊断方面,深度学习能精准快速地完成肝脏的影像学定位和分割,有助于对肝癌准确诊断,而放射组学在辅助肝癌诊断、预测预后方面也有较高价值。在治疗方面,尽管AI干预肝癌手术过程仍有较大难度,但其可为肝癌患者制定个体化手术方案,得以实现“精准肝切除”,并可提前发出术中出血等预警信号。融合成像技术可协助规划射频消融手术流程,实现消融针的精确放置。此外,AI能为介入及放射治疗的效果甚至肿瘤后期进展提供预测参考。病理分析也因AI的引入而更加快速准确且能识别一些肉眼难以辨别的细节和特征纹理。在肝移植方面,AI基于风险预测模型对供肝分配的指导则有助于更好地利用有限的供肝资源。最后,AI几乎可用于所有治疗方式的预后预测。结论AI通过一系列数据挖掘方法并结合统计学分析构建功能完善的模型,用于肝癌诊断、辅助治疗、预后预测等方面,较传统医学模式更高效、精确和自动化。

Objective To better understand artificial intelligence(AI)and its application in management of liver cancer.Method The relevant literatures about AI in the diagnosis and treatment of liver cancer in recent years were reviewed.Results In terms of diagnosis,the deep learning could precisely and quickly complete the imaging localization and segmentation of the liver,which was helpful for the diagnosis,while radiomics had a high value in assisting the diagnosis of liver cancer,predicting the postoperative recurrence and long-term survival of patients with liver cancer.In regard of treatment,although it was still difficult for AI to intervene in liver surgery,it had significant advantages in formulating individualized operation scheme for patients with liver cancer,which enabled precise hepatectomy and was helpful for prediction of intraoperative bleeding.AI fusion imaging could provide assistance in operation plan making and realize the precise placement of ablation needle.AI was able to predict the tumor response or even tumor progression after interventional therapy and radiotherapy.Pathological analysis was also facilitated by AI and was able to identify some details and feature textures that were difficult to manually distinguish.For transplantation,guidance of AI on the allocation of donor livers based on hazards models helped make better use of limited organ resources.AI could be applied in prognosis prediction in almost all treatment modalities.Conclusions AI provides more efficient and precise diagnosis,treatment support and prognosis than conventional medical process in liver cancer,generally by constructing a fully functional model based on a series of data mining methods combined with statistical analysis.