Predicting High-risk Lung Adenocarcinoma in Solid and Part-solid Nodules on Low-dose CT: A Multicenter Study
Our LDCT-based radiomic model enabled non-invasive identification of high-risk LUADs in solid and part-solid nodules with good diagnostic performance and might assist in case-specific decision-making in lung cancer screening.
Authors
- Jieke Liu
- Haomiao Qing (ORCID: https://orcid.org/0009-0001-0929-3618)
- Yong Li (ORCID: https://orcid.org/0000-0002-8910-9787)
- Junqiang Liang
- Peng Zhou (ORCID: https://orcid.org/0000-0002-1281-6088)
- Ling Guo (ORCID: https://orcid.org/0000-0001-7133-3951)
- Yanling Zheng
- Yu Long (ORCID: https://orcid.org/0009-0003-9672-6593)
- Wei Lin (ORCID: https://orcid.org/0000-0001-9711-2022)
Institutions
- University of Electronic Science and Technology of China (CN)
- Deyang Stomatological Hospital (CN)
- Sichuan Cancer Hospital (CN)
Publication Details
- Journal
- Academic Radiology
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1016/j.acra.2024.11.059
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities