Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning
The intratumoral heterogeneity score effectively assessed the invasiveness of lung adenocarcinoma. The stacking classifier outperformed other methods for this ternary classification task. Intratumoral heterogeneity score, nodule size, nodule type, and age predict invasiveness.
Authors
- Yujie Feng (ORCID: https://orcid.org/0000-0003-1596-8965)
- Xiaohong Fan (ORCID: https://orcid.org/0000-0003-0265-7279)
- Ying Zeng (ORCID: https://orcid.org/0000-0003-2795-4616)
- Jinqiu Deng
- Shanyue Lin
- Zhichao Zuo
- Wanyin Qi (ORCID: https://orcid.org/0009-0004-5691-4669)
Institutions
- Zhejiang Normal University (CN)
- Hunan University (CN)
- Guilin Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Xiangyang Central Hospital (CN)
- Xiangtan University (CN)
- Hunan Agricultural University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s13244-025-02097-0
- Citations
- 10
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 15.18