人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望

彭东阁; 万子叶; 卢宁

doi:10.19401/j.cnki.1007-3639.2025.05.008

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人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望

综述 | 更新时间：2025-12-31

- 人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望
- Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective
- 中国癌症杂志 2025年35卷第5期页码：496-504
- 作者机构：
  
  1. 新疆医科大学研究生院，新疆乌鲁木齐 830000
  2. 中国人民解放军新疆军区总医院肿瘤科，新疆乌鲁木齐 830000
- 作者简介：
  
  [ "彭东阁（ORCID: 0000-001-9469-0775），博士研究生在读。" ]
  卢宁（ORCID: 0009-0001-5383-9876），博士研究生，主任医师、教授，中国人民解放军新疆军区总医院肿瘤科主任。
- 基金信息：
  
  新疆维吾尔自治区科技厅“天山英才”科技创新领军人才(2023TSYCLJ0040)
- DOI：10.19401/j.cnki.1007-3639.2025.05.008
  中图分类号： R735.2
- 收稿：2024-10-13，
  
  修回：2025-03-15，
  
  纸质出版：2025-05-30
- 稿件说明：
移动端阅览
彭东阁, 万子叶, 卢宁. 人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望[J]. 中国癌症杂志, 2025,35(5):496-504.

Dongge PENG, Ziye WAN, Ning LU. Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective[J]. China Oncology, 2025, 35(5): 496-504.
彭东阁, 万子叶, 卢宁. 人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望[J]. 中国癌症杂志, 2025,35(5):496-504. DOI： 10.19401/j.cnki.1007-3639.2025.05.008.

Dongge PENG, Ziye WAN, Ning LU. Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective[J]. China Oncology, 2025, 35(5): 496-504. DOI： 10.19401/j.cnki.1007-3639.2025.05.008.

摘要

胃癌是全球范围内高发的恶性肿瘤之一，具有起病隐匿、早期诊断困难、进展期预后不良等特点。传统诊断技术受主观因素影响较大，且在准确率和效率方面存在局限，难以满足精准医学的临床需求。近年来，人工智能（artificial intelligence，AI）技术，尤其是基于深度学习（deep learning，DL）的快速发展，为胃癌的精准诊疗带来了全新的机遇。AI辅助胃镜诊断可显著提升病变检出率及诊断效率，AI驱动的影像组学模型可精准预测肿瘤浸润深度、淋巴结及腹膜转移情况，而AI辅助病理学系统的应用则可以显著提高诊断的准确率和效率。此外，结合多组学数据的AI模型在化疗和靶向治疗反应预测以及个体化预后评估方面亦展现出巨大潜力。然而，AI技术在胃癌领域的临床转化仍面临诸多挑战，包括数据标准化不统一、模型泛化能力不足及算法可解释性较弱等问题。因此，本文系统综述AI技术在胃癌诊断、疗效评估及预后预测方面的最新研究进展，深入探讨当前技术所面临的核心挑战，并展望未来AI在胃癌精准诊疗中的发展趋势，以期推动AI技术的广泛应用和临床转化，最终实现胃癌诊疗的精准化和个体化，改善患者的临床预后。

Abstract

Gastric cancer remains one of the most prevalent and lethal malignancies worldwide

characterized by an insidious onset

challenges in early detection

and a poor prognosis in advanced stages. Conventional diagnostic approaches are often constrained by subjective interpretation and inherent limitations in accuracy and efficiency

rendering them insufficient to meet the demands of precision medicine. In recent years

the rapid advancement of artificial intelligence (AI)

particularly deep learning (DL)-based techniques

has opened new avenues for the precise diagnosis and management of gastric cancer. Emerging evidence suggests that AI-assisted endoscopic systems significantly enhance lesion detection rates and diagnostic efficiency

while AI-driven radiomics models offer precise predictions of tumor invasion depth

lymph node involvement

and peritoneal metastasis. Additionally

AI-powered pathology analysis has markedly improved both diagnostic accuracy and efficiency. Moreover

integrative AI models leveraging multi-omics data have demonstrated great potential in predicting responses to chemotherapy and targeted therapies

as well as facilitating personalized prognostic assessments. However

despite these promising advancements

the clinical implementation of AI in gastric cancer remains hindered by challenges such as the lack of standardized datasets

limited model generalizability

and insufficient algorithm interpretability. This review systematically synthesized the latest advancements in AI applications for gastric cancer diagnosis

treatment response evaluation

and prognostic prediction. Furthermore

it critically examined key technical challenges in current AI methodologies and explored future directions in AI-driven precision medicine for gastric cancer. By addressing these challenges

we aimed to foster the widespread adoption and clinical translation of AI technologies

ultimately advancing precision oncology and improving patient outcomes.

关键词

Keywords

references

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