The application of explainable deep-radiomics in lung cancer research: Method comparison and analysis
- VernacularTitle:可解释深度影像组学用于肺癌研究的方法比较与分析
- Author:
Yusen WANG
1
;
Chao GUO
1
;
Shanqing LI
1
Author Information
1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
lung neoplasms;
precision medicine;
radiomics;
deep learning;
non-fully supervised learning;
model interpretability
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(07):1034-1042
- CountryChina
- Language:Chinese
-
Abstract:
Nowadays, lung cancer is the most common and lethal invasive tumor type in Chinese population, challenging overall health level. However, personalized early-stage treatment is currently still not widely implemented, and the choice of treatment highly depends on experience of physician. Based on deep learning and radiomics principles, deep-radiomics is important for establishing objective and promotable precision medicine plans. Among all aspects, the explainability of a model is critical for its usage in clinical practice. This paper discusses the technical aspects of explainable deep-radiomics in lung cancer, and analyzes challenges we are facing. Non-fully supervised learning methods, as a current hotspot in deep learning technology, can construct more trustworthy and practically valuable deep learning models through the co-design method of performance-interpretability. Medical artificial intelligence faces three core challenges in transitioning from the laboratory to hospitals: high-level cognitive demands, data privacy and generalization capabilities, and regulatory compliance. However, with appropriate design, non-fully supervised learning holds the greatest potential to bridge the gap between design and application, enabling broader adoption.
