Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study
A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.
Authors
- Weijie Fan (ORCID: https://orcid.org/0000-0002-1828-0982)
- Yan Yang (ORCID: https://orcid.org/0000-0001-8852-2791)
- Fei Wang (ORCID: https://orcid.org/0000-0002-5670-1944)
- Zheng‐Ming Wang (ORCID: https://orcid.org/0000-0001-5908-8076)
- Dong Zhang (ORCID: https://orcid.org/0000-0003-0009-3318)
- Li Wen (ORCID: https://orcid.org/0000-0001-8638-9552)
Institutions
- Army Medical University (CN)
- First Affiliated Hospital of Sichuan Medical University (CN)
- Xinqiao Hospital (CN)
Publication Details
- Journal
- BMC Pulmonary Medicine
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1186/s12890-024-03360-8
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Army Medical University
- Chongqing Xinqiao Hospital, Second Affiliated Hospital of Army Medical University