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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-025-01813-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849341911726292992 |
|---|---|
| author | Xianzhi Huang Fangyi Xu Wenchao Zhu Lin Yao Jiahuan He Junhao Su Wending Zhao Hongjie Hu |
| author_facet | Xianzhi Huang Fangyi Xu Wenchao Zhu Lin Yao Jiahuan He Junhao Su Wending Zhao Hongjie Hu |
| author_sort | Xianzhi Huang |
| collection | DOAJ |
| description | 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 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 ( $$p < 0.05$$ ) 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 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. |
| format | Article |
| id | doaj-art-917a7c35637f46eeaa6169d05a4da731 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-917a7c35637f46eeaa6169d05a4da7312025-08-20T03:43:31ZengBMCBMC Medical Imaging1471-23422025-07-0125111210.1186/s12880-025-01813-yAn integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodulesXianzhi Huang0Fangyi Xu1Wenchao Zhu2Lin Yao3Jiahuan He4Junhao Su5Wending Zhao6Hongjie Hu7Institute for Quantum Technology and Engineering Computing, School of JiaYang, Zhejiang Shuren UniversityDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineHuayi Boao (Beijing) Quantum Technology Co., Ltd.Shulan International Medical College, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityHuayi Boao (Beijing) Quantum Technology Co., Ltd.Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineAbstract 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 ( $$p < 0.05$$ ) 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 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.https://doi.org/10.1186/s12880-025-01813-yQuantum machine learning (QML) algorithmsRadiomics-based feature extractionSHAP analysisPulmonary ground-glass nodules (pGGNs) diagnosis |
| spellingShingle | Xianzhi Huang Fangyi Xu Wenchao Zhu Lin Yao Jiahuan He Junhao Su Wending Zhao Hongjie Hu An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules BMC Medical Imaging Quantum machine learning (QML) algorithms Radiomics-based feature extraction SHAP analysis Pulmonary ground-glass nodules (pGGNs) diagnosis |
| title | An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules |
| title_full | An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules |
| title_fullStr | An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules |
| title_full_unstemmed | An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules |
| title_short | An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules |
| title_sort | integrated strategy based on radiomics and quantum machine learning diagnosis and clinical interpretation of pulmonary ground glass nodules |
| topic | Quantum machine learning (QML) algorithms Radiomics-based feature extraction SHAP analysis Pulmonary ground-glass nodules (pGGNs) diagnosis |
| url | https://doi.org/10.1186/s12880-025-01813-y |
| work_keys_str_mv | AT xianzhihuang anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT fangyixu anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT wenchaozhu anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT linyao anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT jiahuanhe anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT junhaosu anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT wendingzhao anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT hongjiehu anintegratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT xianzhihuang integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT fangyixu integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT wenchaozhu integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT linyao integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT jiahuanhe integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT junhaosu integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT wendingzhao integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules AT hongjiehu integratedstrategybasedonradiomicsandquantummachinelearningdiagnosisandclinicalinterpretationofpulmonarygroundglassnodules |