Prediction of benign and malignant ground glass pulmonary nodules based on multi-feature fusion of attention mechanism
Our experimental results demonstrated that (1) applying attention mechanism to fuse whole-lung CT images, radiomics features, clinical, and morphological features is feasible, (2) clinical, morphological, and radiomics features provide supplementary information for the classification of benign and malignant GGNs based on CT images, and (3) utilizing baseline whole-lung CT features to predict the benign and malignant of GGNs is an effective method. Therefore, optimizing the fusion of baseline whole-lung CT features can effectively improve the classification performance of GGNs.
Authors
- Shiyuan Liu (ORCID: https://orcid.org/0000-0003-3420-0310)
- Li Fan (ORCID: https://orcid.org/0000-0003-4722-3933)
- Xiuxiu Zhou (ORCID: https://orcid.org/0000-0003-2405-0566)
- Taohu Zhou (ORCID: https://orcid.org/0000-0001-6208-2249)
- Wenjun Huang (ORCID: https://orcid.org/0000-0002-2765-3381)
- Heng Deng (ORCID: https://orcid.org/0000-0002-2199-8960)
Institutions
- Shanghai University (CN)
- Deyang Stomatological Hospital (CN)
- Xian Yang Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-10-09
- DOI
- https://doi.org/10.3389/fonc.2024.1447132
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- National Health Commission of the People's Republic of China