Huang Xianzhi, Xu Fangyi, Zhu Wenchao, Yao Lin, He Jiahuan, Su Junhao, Zhao Wending, Hu Hongjie
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.
BMC Med Imaging. 2025 Jul 11;25(1):279. doi: 10.1186/s12880-025-01813-y.
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.
准确分类肺纯磨玻璃结节（pGGN）对于区分浸润性腺癌（IVA）与原位腺癌（AIS）和微浸润腺癌（MIA）至关重要，这会显著影响治疗决策。本研究旨在通过结合基于放射组学的特征提取、量子机器学习（QML）模型和SHapley加性解释（SHAP）分析来开发一种高精度的综合策略，以提高pGGN分类的诊断准确性和可解释性。
回顾性分析了275例患者的322个pGGN。CT图像被随机分为训练组和测试组（80:20），从训练组中提取放射组学特征。开发了三种QML模型——量子支持向量分类器（QSVC）、Pegasos QSVC和量子神经网络（QNN），并与经典支持向量机（SVM）进行比较。应用SHAP分析来解释放射组学特征对模型预测的贡献。
所有三种QML模型均优于经典SVM，其中QNN模型在分类指标上实现了最高的提升（[公式：见原文]），包括准确率（89.23%，95%CI：81.54% - 95.38%）、灵敏度（96.55%，95%CI：89.66% - 100.00%）、特异性（83.33%，95%CI：69.44% - 94.44%）和曲线下面积（AUC）（0.937，95%CI：0.871 - 0.983）。SHAP分析确定低灰度游程强调（LGLRE）、灰度不均匀性（GLN）和大小区域不均匀性（SZN）为影响分类的最关键特征。
本研究表明，所提出的结合放射组学、QML模型和SHAP分析的综合策略显著提高了pGGN分类的准确性和可解释性，尤其是在小样本数据集中。它为早期非侵入性肺癌诊断提供了一个有前景的工具，并有助于临床医生做出更明智的治疗决策。
不适用。
