Construction and validation of a risk stratification model based on Lung-RADS® v2022 and CT features for predicting the invasive pure ground-glass pulmonary nodules in China
Lung-RADS<sup>®</sup> v2022 delayed lung cancer diagnosis for nodules appearing as pGGNs. Lung-RADS<sup>®</sup> v2022 showed lower accuracy and AUC than Lung-RADS 1.0. cLung-RADS<sup>®</sup> v2022 model effectively predicts the invasiveness of pulmonary pGGNs.
Authors
- Hong Ge (ORCID: https://orcid.org/0000-0003-4270-2556)
- Xuejun Chen (ORCID: https://orcid.org/0000-0002-4467-3464)
- Renzhi Zhang (ORCID: https://orcid.org/0000-0001-7723-0865)
- Wentao Liu (ORCID: https://orcid.org/0000-0001-7599-4650)
- Min Fang (ORCID: https://orcid.org/0000-0001-6688-437X)
- Qingcheng Meng (ORCID: https://orcid.org/0000-0003-4749-7467)
- Pengrui Gao (ORCID: https://orcid.org/0000-0003-0844-2287)
- Tong Liu (ORCID: https://orcid.org/0000-0003-4268-1505)
- Hui Peng
- W. C. Chen
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Zhengzhou University (CN)
- National Cancer Center (US)
- Henan Cancer Hospital (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-03-23
- DOI
- https://doi.org/10.1186/s13244-025-01937-3
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52