Related Experiment Video For Pulmonary ground-glass nodules (pGGNs) diagnosis
Updated: Sep 16, 2025
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
Published on: October 13, 2023
An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of
Xianzhi Huang1, Fangyi Xu2,3, Wenchao Zhu2,3
1Institute for Quantum Technology and Engineering Computing, School of JiaYang, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, Zhejiang, 310015, China.
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 develop a high-precision integrated strategy by combining radiomics-based feature extraction, Quantum Machine Learning (QML) models, and SHapley Additive exPlanations (SHAP) analysis to improve diagnostic accuracy and interpretability in pGGN classification.
Methods:
A total of 322 pGGNs from 275 patients were retrospectively analyzed. The CT images was randomly divided into training and testing cohorts (80:20), with radiomic features extracted from the training cohort. Three QML models-Quantum Support Vector Classifier (QSVC), Pegasos QSVC, and Quantum Neural Network (QNN)-were developed and compared with a classical Support Vector Machine (SVM). SHAP analysis was applied to interpret the contribution of radiomic features to the models' predictions.
Results:
All three QML models outperformed the classical SVM, with the QNN model achieving the highest improvements ([Formula: see text]) in classification metrics, including accuracy (89.23%, 95% CI: 81.54% - 95.38%), sensitivity (96.55%, 95% CI: 89.66% - 100.00%), specificity (83.33%, 95% CI: 69.44% - 94.44%), and area under the curve (AUC) (0.937, 95% CI: 0.871 - 0.983), respectively. SHAP analysis identified Low Gray Level Run Emphasis (LGLRE), Gray Level Non-uniformity (GLN), and Size Zone Non-uniformity (SZN) as the most critical features influencing classification.
Conclusion:
This study demonstrates that the proposed integrated strategy, combining radiomics, QML models, and SHAP analysis, significantly enhances the accuracy and interpretability of pGGN classification, particularly in small-sample datasets. It offers a promising tool for early, non-invasive lung cancer diagnosis and helps clinicians make more informed treatment decisions.
Clinical Trial Number:
Not applicable.
More Related Videos
Related Concept Videos
01:13Radiological Investigation III: Pulmonary Angiogram and PET Scan
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
01:30Radiological Investigation II: MRI and Ventilation Perfusion Scan
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...