Quantitative Measures of Pure Ground-Glass Nodules from an Artificial Intelligence Software for Predicting Invasiveness of Pulmonary Adenocarcinoma on Low-Dose CT: A Multicenter Study
<b>Objectives</b>: Deep learning-based artificial intelligence (AI) tools have been gradually used to detect and segment pulmonary nodules in clinical practice. This study aimed to assess the diagnostic performance of quantitative measures derived from a commercially available AI softwar...
Saved in:
| Main Authors: | Yu Long, Yong Li, Yongji Zheng, Wei Lin, Haomiao Qing, Peng Zhou, Jieke Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/13/7/1600 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Differentiation of early-stage tumors from benign lesions manifesting as pure ground-glass nodule: a clinical prediction study based on AI-derived quantitative parameters
by: Shuxiang Chen, et al.
Published: (2025-05-01) -
Corrigendum: Differentiation of early-stage tumors from benign lesions manifesting as pure ground-glass nodule: a clinical prediction study based on AI-derived quantitative parameters
by: Shuxiang Chen, et al.
Published: (2025-06-01) -
CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction
by: Rui Chen, et al.
Published: (2025-12-01) -
Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules
by: Xiao Wang, et al.
Published: (2025-04-01) -
Exploration of CT-based discrimination and diagnosis of various pathological types of ground glass nodules in the lungs
by: Haihui Wu, et al.
Published: (2025-04-01)