Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules
Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.
Authors
- Hanbo Pan (ORCID: https://orcid.org/0000-0001-5880-2573)
- Qingquan Luo (ORCID: https://orcid.org/0000-0003-4098-9477)
- Long Jiang (ORCID: https://orcid.org/0000-0002-6860-755X)
- Miao Wang (ORCID: https://orcid.org/0000-0002-1455-3365)
- Hongda Zhu (ORCID: https://orcid.org/0009-0005-3285-5454)
- Ningyuan Zou
- Yang Zhou (ORCID: https://orcid.org/0000-0003-3365-6631)
- Weiqiu Jin
- Yu Tian (ORCID: https://orcid.org/0009-0004-3013-6504)
- Jia Huang
Institutions
- Shanghai Jiao Tong University (CN)
- Shanghai Chest Hospital (CN)
- Zhengzhou People's Hospital (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1080/07853890.2024.2405075
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Natural Science Foundation of Shanghai
- Shanghai Municipal Health Commission
- National Natural Science Foundation of China
- Shanghai Chest Hospital
- Fundamental Research Funds for the Central Universities