An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.
Authors
Affiliations (9)
Affiliations (9)
- Institute for Quantum Technology and Engineering Computing, School of JiaYang, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, Zhejiang, 310015, China.
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China.
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China.
- Huayi Boao (Beijing) Quantum Technology Co., Ltd., 1 Kegu Street, Beijing, 102600, China.
- Shulan International Medical College, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, Zhejiang, 310015, China.
- College of Information Science and Technology, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, Zhejiang, 310015, China.
- Huayi Boao (Beijing) Quantum Technology Co., Ltd., 1 Kegu Street, Beijing, 102600, China. [email protected].
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China. [email protected].
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, 3 Qingchun East Road, Hangzhou, Zhejiang, 310016, China. [email protected].
Abstract
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. 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. 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. 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. Not applicable.