An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules

An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules

Abstract Purpose Accurate classification of pulmonary pure ground-glass nodules (pGGNs) is essential for distinguishing invasive adenocarcinoma (IVA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), which significantly influences treatment decisions. This study aims to...

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Bibliographic Details
Main Authors: Xianzhi Huang, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Quantum machine learning (QML) algorithms
Radiomics-based feature extraction
SHAP analysis
Pulmonary ground-glass nodules (pGGNs) diagnosis
Online Access:https://doi.org/10.1186/s12880-025-01813-y
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