Artificial intelligence-driven precision management of chest wall tumors: Prospects and challenges in clinical translation
- VernacularTitle:人工智能驱动胸壁肿瘤精准诊疗：临床转化前景与挑战
- Author:
Feng LIN
1
,
2
,
3
;
Hai GUO
1
,
2
,
3
;
Zhongyi ZENG
2
,
3
;
Haonan LIN
1
,
2
,
3
Author Information
1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
2. Department of Chest Wall Diseases Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
3. Department of Thoracic Surgery, West China Tianfu Hospital, Sichuan University, Chengdu, 610213, P. R. China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
chest wall tumors;
radiomics;
precision diagnosis;
efficacy prediction;
deep learning;
genomics
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(10):1573-1581
- CountryChina
- Language:Chinese
-
Abstract:
The precision diagnosis and treatment of chest wall tumors heavily rely on imaging assessment, which is limited by the subjectivity of conventional methods and their insensitivity to microscopic features. Artificial intelligence (AI) technologies, particularly radiomics and deep learning, offer new opportunities to overcome these bottlenecks. It is important to note, however, that direct AI research focused specifically on chest wall tumors remains scarce. Most current insights are derived from methodological borrowing and conceptual extrapolation of studies on other solid tumors, such as lung, breast, and esophageal cancers. This review systematically outlines the potential application framework of AI in the management of chest wall tumors, encompassing benign-malignant differentiation, non-invasive pathological subtyping, early treatment response prediction, and prognosis stratification based on multi-modal imaging. It discusses the core model-building strategies and validation processes centered on data fusion, and critically analyzes the unique challenges in this field, including data scarcity, model interpretability, and clinical translation. Moving forward, key directions for translating the potential of AI into clinical reality include fostering the development of dedicated chest wall tumor datasets, conducting prospective validation studies, and exploring "imaging-genomics" integration as well as dynamic decision-support systems.
