An Integrated Strategy Based on Radiomics and Quantum Machine Learning: Diagnosis and Clinical Interpretation of Pulmonary Ground-Glass Nodules

Abstract

Background and Purpose: Accurate classification of pulmonary pure ground-glass nodules (pGGNs) is essentialfor 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-precisionintegrated 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 pGGNclassification.Materials and methods: A total of 322 pGGNs from 275 patients were retrospectively analyzed. The CT images wasrandomly 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 appliedto 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 (p < 0.05) 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 thecurve (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 featuresinfluencing classification.Conclusion: This study demonstrates that the proposed integrated strategy, combining radiomics, QML models, andSHAP analysis, significantly enhances the accuracy and interpretability of pGGN classification, particularly in small-sample datasets.

Note:
Funding declaration: This study was supported by Zhejiang Shuren University Basic Scientific Research Special Funds (2023XZ018) and Fundamental Research Funds for the Central Universities under grant 226-2024-00185

Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that couldhave appeared to influence the work reported in this paper
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Keywords: Quantum Machine Learning (QML) algorithms, Radiomics-based feature extraction, SHAP analysis, Pulmonary ground-glass nodules (pGGNs) diagnosis