Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging
The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.
Authors
- Mingyan Zhou (ORCID: https://orcid.org/0000-0002-9210-4151)
- Dongbo Wu (ORCID: https://orcid.org/0000-0003-4500-047X)
- Yingci Li
- Jiankun Li (ORCID: https://orcid.org/0000-0001-6443-3479)
- Feifei Gong
Institutions
- Harbin Medical University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Shenyang 242 Hospital (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1186/s12885-024-13018-7
- Citations
- 3
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.26
Funders
- Harbin Medical University