CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction
Objective: To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN). Methods: A retrospective analysis was conducted on clinical data and radiological images from 392 patients with lun...
Saved in:
| Main Authors: | Rui Chen, Hu Zhang, Xingwen Huang, Haitao Han, Jinbo Jian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | European Journal of Radiology Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047725000474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules
by: Xianzhi Huang, et al.
Published: (2025-07-01) -
Changes in pulmonary function of CT-guided microwave ablation for patients with pulmonary ground-glass nodules after lobectomy: a retrospective, observation study
by: Ruoyu Deng, et al.
Published: (2025-05-01) -
Exploring the relationships between CT and pathological characteristics and gene mutations in neoplastic ground glass nodules
by: Zi-ya Zhao, et al.
Published: (2025-08-01) -
Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study
by: Jiabi Zhao, et al.
Published: (2025-05-01) -
Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas
by: Yu-Qiang Zuo, et al.
Published: (2025-08-01)