Fang M J, Dong D, Tian J
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Zhonghua Wei Chang Wai Ke Za Zhi. 2025 May 25;28(5):473-480. doi: 10.3760/cma.j.cn441530-20250301-00075.
Peritoneal metastasis is a key factor in the poor prognosis of advanced gastrointestinal cancer patients. Traditional radiological diagnostic faces challenges such as insufficient sensitivity. Through technologies like radiomics and deep learning, artificial intelligence can deeply analyze the tumor heterogeneity and microenvironment features in medical images, revealing markers of peritoneal metastasis and constructing high-precision predictive models. These technologies have demonstrated advantages in tasks such as predicting peritoneal metastasis, assessing the risk of peritoneal recurrence, and identifying small metastatic foci during surgery. This paper summarizes the representative progress and application prospects of medical imaging artificial intelligence in the diagnosis and treatment of peritoneal metastasis, and discusses potential development directions such as multimodal data fusion and large model. The integration of medical imaging artificial intelligence with clinical practice is expected to advance personalized and precision medicine in the diagnosis and treatment of peritoneal metastasis in gastrointestinal cancers.
腹膜转移是晚期胃肠道癌患者预后不良的关键因素。传统放射学诊断面临着灵敏度不足等挑战。通过放射组学和深度学习等技术，人工智能可以深入分析医学图像中的肿瘤异质性和微环境特征，揭示腹膜转移的标志物并构建高精度预测模型。这些技术在预测腹膜转移、评估腹膜复发风险以及在手术中识别小转移灶等任务中已展现出优势。本文总结了医学影像人工智能在腹膜转移诊断和治疗中的代表性进展及应用前景，并探讨了多模态数据融合和大模型等潜在发展方向。医学影像人工智能与临床实践的整合有望推动胃肠道癌腹膜转移诊断和治疗中的个性化与精准医学发展。
