Zhang Shuaitong, Mu Wei, Dong Di, Wei Jingwei, Fang Mengjie, Shao Lizhi, Zhou Yu, He Bingxi, Zhang Song, Liu Zhenyu, Liu Jianhua, Tian Jie
School of Engineering Medicine, Beihang University, Beijing, China.
Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China.
Health Data Sci. 2023 Feb 6;3:0005. doi: 10.34133/hds.0005. eCollection 2023.
Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.
We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.
AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
消化系统肿瘤（DSNs）是癌症相关死亡的主要原因，5年生存率低于20%。包括内镜图像、全切片图像、计算机断层扫描图像和磁共振图像在内的医学图像主观评估在DSNs临床实践中起着至关重要的作用，但性能有限且放射科医生或病理科医生的工作量增加。人工智能（AI）在医学图像分析中的应用有望增强医学图像的视觉解读，这不仅可以使复杂的评估过程自动化，还能将医学图像转化为与肿瘤异质性相关的定量成像特征。
我们简要介绍了AI用于医学图像分析的方法，然后回顾其临床应用，包括对食管癌、胃癌、结直肠癌和肝细胞癌这4种典型DSNs的临床辅助诊断、治疗反应评估和预后预测。
AI技术在支持DSNs的临床诊断和治疗决策方面具有巨大潜力。在将其应用于DSNs临床实践之前，应克服若干技术问题。
