Computational pathology in precision oncology: Evolution from task-specific models to foundation models

doi:10.1097/CM9.0000000000003790

CSpace

	Computational pathology in precision oncology: Evolution from task-specific models to foundation models
	Wang, Yuhao 1,2; Gu, Yunjie 1,2; Zhang, Xueyuan 3; Wang, Baizhi 1,2; Wang, Rundong 1,2; Li, Xiaolong 1,2; Liu, Yudong 4; Qu, Fengmei 5; Ren, Fei 4; Yan, Rui 1,2; Zhou, S. Kevin 1,2,6,7
	2025-11-20
发表期刊	CHINESE MEDICAL JOURNAL
ISSN	0366-6999
卷号	138 期号:22 页码:2868-2878
摘要	With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
关键词	Computational pathology Artificial intelligence Deep learning Foundation models Precision oncology
DOI	10.1097/CM9.0000000000003790
收录类别	SCI
语种	英语
WOS研究方向	General & Internal Medicine
WOS类目	Medicine, General & Internal
WOS记录号	WOS:001619101600006
出版者	LIPPINCOTT WILLIAMS & WILKINS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/43074
专题	中国科学院计算技术研究所
通讯作者	Ren, Fei; Yan, Rui; Zhou, S. Kevin
作者单位	1.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China 2.USTC, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou 215123, Jiangsu, Peoples R China 3.Chongqing Zhijian Life Technol Co LTD, Chongqing 400039, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China 5.Jinfeng Lab, Chongqing 401329, Peoples R China 6.Jiangsu Prov Key Lab Multimodal Digital Twin Techn, Suzhou 215123, Jiangsu, Peoples R China 7.Univ Sci & Technol China, State Key Lab Precis & Intelligent Chem, Hefei 230026, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yuhao,Gu, Yunjie,Zhang, Xueyuan,et al. Computational pathology in precision oncology: Evolution from task-specific models to foundation models[J]. CHINESE MEDICAL JOURNAL,2025,138(22):2868-2878.
APA	Wang, Yuhao.,Gu, Yunjie.,Zhang, Xueyuan.,Wang, Baizhi.,Wang, Rundong.,...&Zhou, S. Kevin.(2025).Computational pathology in precision oncology: Evolution from task-specific models to foundation models.CHINESE MEDICAL JOURNAL,138(22),2868-2878.
MLA	Wang, Yuhao,et al."Computational pathology in precision oncology: Evolution from task-specific models to foundation models".CHINESE MEDICAL JOURNAL 138.22(2025):2868-2878.