Computational pathology in precision oncology: Evolution from task-specific models to foundation models.
10.1097/CM9.0000000000003790
- Author:
Yuhao WANG
1
;
Yunjie GU
1
;
Xueyuan ZHANG
2
;
Baizhi WANG
1
;
Rundong WANG
1
;
Xiaolong LI
1
;
Yudong LIU
3
;
Fengmei QU
4
;
Fei REN
3
;
Rui YAN
1
;
S Kevin ZHOU
1
Author Information
1. School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, Hefei 230026, China.
2. Chongqing Zhijian Life Technology Co., Ltd., Chongqing 400039, China.
3. State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing 100190, China.
4. Jinfeng Laboratory, Chongqing 401329, China.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Computational pathology;
Deep learning;
Foundation models;
Precision oncology
- MeSH:
Humans;
Precision Medicine/methods*;
Medical Oncology/methods*;
Artificial Intelligence;
Neoplasms/pathology*;
Computational Biology/methods*
- From:
Chinese Medical Journal
2025;138(22):2868-2878
- CountryChina
- Language:English
-
Abstract:
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.
