Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study
Ground-glass nodules invasiveness directly influences surgical strategies and prognosis. Multiple-instance learning framework integrates radiomics and deep learning features. Integrated model achieves superior accuracy and consistency in invasiveness prediction.
Authors
- Xiulin Wang (ORCID: https://orcid.org/0000-0002-8884-0973)
- Lili Qin
- Yijiang Hu (ORCID: https://orcid.org/0000-0003-0036-7980)
- Jianlin Wu
- Feng Chen (ORCID: https://orcid.org/0000-0002-4508-5963)
- Jing Shen (ORCID: https://orcid.org/0000-0002-4566-5925)
- Kaifeng Wang (ORCID: https://orcid.org/0000-0002-5076-8304)
- Yue Xiao (ORCID: https://orcid.org/0000-0002-2836-2906)
- Du Hai (ORCID: https://orcid.org/0009-0002-6543-3854)
Institutions
- Fujian Medical University (CN)
- Dalian Medical University (CN)
- Soochow University (CN)
- Inner Mongolia Electric Power (China) (CN)
- Zhangjiagang First People's Hospital (CN)
- Zhejiang Lab (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- Affiliated Zhongshan Hospital of Dalian University (CN)
- Seventh People's Hospital of Dalian (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1186/s13244-025-02156-6
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Inner Mongolia
- Qingdao University
- Dalian Medical University