Li James Weiquan, Wang Lai Mun, Ichimasa Katsuro, Lin Kenneth Weicong, Ngu James Chi-Yong, Ang Tiing Leong
Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.
Academic Medicine Center, Duke-NUS Medical School, Singapore.
Clin Endosc. 2024 Jan;57(1):24-35. doi: 10.5946/ce.2023.036. Epub 2023 Sep 25.
The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.
人工智能领域正在迅速发展，人们对利用它来预测T1期结直肠癌淋巴结转移风险产生了兴趣。准确预测淋巴结侵犯可能会减少接受不必要手术的患者数量；相反，评估不足将导致肿瘤治疗效果欠佳。本叙述性综述旨在总结目前关于深度学习预测T1期结直肠癌淋巴结转移概率的文献，重点介绍潜在应用领域以及可能限制其推广性和临床实用性的障碍。
