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中华普通外科学文献(电子版) ›› 2025, Vol. 19 ›› Issue (04) : 274 -280. doi: 10.3877/cma.j.issn.1674-0793.2025.04.010

综述

病理组学在胃癌诊治中的应用与挑战
郑仁杰1,2, 张尊庶1,2, 黄陈1,2,3,()   
  1. 1 239001 滁州,安徽医科大学附属滁州医院胃肠外科
    2 239001 滁州市公济胃肠肿瘤研究所
    3 200080 上海,上海交通大学医学院附属第一人民医院胃肠外科
  • 收稿日期:2025-06-26 出版日期:2025-08-01
  • 通信作者: 黄陈
  • 基金资助:
    国家自然科学基金面上项目(82472921,82072662)

Application and challenges of artificial intelligence-based pathomics in the diagnosis and management of gastric cancer

Renjie Zheng1,2, Zunshu Zhang1,2, Chen Huang,1,2,3()   

  1. 1 Department of Gastrointestinal Surgery, the Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou 239001, China
    2 Chuzhou Gongji Gastrointestinal Cancer Institute, Chuzhou 239001, China
    3 Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University of Medicine, Shanghai 200080, China
  • Received:2025-06-26 Published:2025-08-01
  • Corresponding author: Chen Huang
引用本文:

郑仁杰, 张尊庶, 黄陈. 病理组学在胃癌诊治中的应用与挑战[J/OL]. 中华普通外科学文献(电子版), 2025, 19(04): 274-280.

Renjie Zheng, Zunshu Zhang, Chen Huang. Application and challenges of artificial intelligence-based pathomics in the diagnosis and management of gastric cancer[J/OL]. Chinese Archives of General Surgery(Electronic Edition), 2025, 19(04): 274-280.

胃癌是来源于胃黏膜上皮的恶性疾病,其发病率和死亡率在众多恶性肿瘤中均居前列,严重威胁人类的生命健康。胃癌治疗的前提是准确诊断胃癌亚型并提出最佳治疗策略,以延长患者的生存期。近年来,病理组学作为一种人工智能算法驱动的新兴组学技术,能够从全切片数字扫描图像中更准确地识别癌症亚型,分析肿瘤微环境及细胞核异形的病理组学特征,这不仅大大提高了病理诊断的效率和准确性,也有利于治疗方案选定及预后的远期评估等,具有广阔的临床应用前景。尽管胃癌的病理组学现在仍面临标准化数据稀缺,模态数据质量高度异质,缺乏可解释性、可重复性和人工智能信任问题等挑战,但目前已有大量的持续工作来解决这些问题,并促进基于人工智能的病理组学分析的临床转化。在人工智能技术的广泛应用与临床实践中病理组学数据不断完善的推动下,胃癌的精准诊疗领域正迎来创新的浪潮。

Gastric cancer is a malignant disease originating from the gastric mucosal epithelium, and its incidence and mortality are among the highest ones in the malignant tumors, posing a serious threat to human life and health. The prerequisite for gastric cancer treatment is to accurately diagnose gastric cancer subtypes and propose optimal therapeutic strategies to prolong patients’ survival. In recent years, pathomics, as an emerging histological technology driven by artificial intelligence (AI) algorithms, is capable of identifying cancer subtypes, analyzing the pathological features of tumor microenvironment and nuclear atypia more accurately from full-slice digital scanning images, which not only greatly improves the efficiency and accuracy of pathological diagnosis, but also facilitates the selection of treatment options and the long-term evaluation of prognosis, and has broad clinical application prospects. Although pathomics of gastric cancer now still faces challenges such as scarcity of standardized data, high heterogeneity of modal data quality, lack of interpretability, reproducibility and AI trust issues, there is large amount of sustained work to address these issues and to facilitate the clinical translation of AI-based pathomics. The field of precision diagnosis and treatment of gastric cancer is witnessing a wave of innovation, driven by the widespread use of AI technology and the continuous improvement of pathohistological data in clinical practice.

表1 传统病理学与AI病理组学对比概览
图1 基于AI的病理组学应用流程 A.胃癌患者组织标本;B.苏木精-伊红染色切片;C.玻片扫描影像系统;D.胃癌组织全切片数字扫描图像;E.数字病理阅片软件ImageViewer;F.胃癌肿瘤微环境中大小为500×500像素的矩形感兴趣图像(region of interest,ROI)(×20);G. CellProfiler软件自动量化分析ROI表型特征;H.病理学切片开源处理软件Qupath;I.图像处理与计算机视觉、数据分析、深度学习软件Matlab
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