Preoperative CT-based artificial intelligence-derived quantitative parameters and imaging features for predicting the invasiveness of histologically confirmed subcentimeter adenocarcinomatous nodules: a two-center study
In this cohort, models combining quantitative CT AI parameters with CT radiologic features demonstrated high performance in predicting the invasiveness of subcentimeter adenocarcinomatous nodules.
Authors
- Hongbiao Sun (ORCID: https://orcid.org/0000-0002-0060-431X)
- Jing Cao (ORCID: https://orcid.org/0000-0002-9485-4204)
- Shiyuan Liu (ORCID: https://orcid.org/0000-0002-0756-1439)
- Xiang Wang (ORCID: https://orcid.org/0000-0001-5538-4310)
- Quanxin Zhu (ORCID: https://orcid.org/0000-0003-3130-4923)
- Jie Song (ORCID: https://orcid.org/0000-0003-4111-2570)
- Qi Chen (ORCID: https://orcid.org/0000-0001-5971-7872)
- Qinling Jiang (ORCID: https://orcid.org/0009-0007-2930-6598)
- Qingchu Li (ORCID: https://orcid.org/0000-0003-4351-5824)
- Yi Xiao (ORCID: https://orcid.org/0000-0001-6758-3763)
Institutions
- Second Military Medical University (CN)
- Shanghai Changzheng Hospital (CN)
- Yichang Central People's Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-11-25
- DOI
- https://doi.org/10.21037/qims-2025-650
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00