用于早期肺癌检测发现的量子核估计
Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection
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中文总结 AI 辅助
本研究将量子-经典混合机器学习应用于 cfDNA 肺癌检测，通过量子核方法分析 DNA 片段组学与甲基化数据，其在部分任务中性能优于经典模型，为早期肺癌检测提供了新途径。
中文摘要 AI 辅助
低剂量胸部计算机断层扫描的肺癌筛查可降低死亡率，但受 uptake（参与率）、依从性及管理挑战限制。基于血液的无细胞 DNA（cfDNA）生物标志物提供了补充方法，不过因肺癌异质性及高维非线性分子信号，早期检测仍具难度。我们评估了量子-经典混合机器学习，利用 DNA 片段组学和 DNA 甲基化进行肺癌检测。经特征选择后，使用 20 和 40 个特征子集训练模型；采用角度和密集角度特征映射结合多种纠缠策略，将特征编码到量子希尔伯特空间；通过精确态矢量模拟计算基于保真度的量子核，与预计算核 SVM、核主成分分析（kernel-PCA）逻辑回归集成，并与基于原始特征训练的 SVM 模型对比。该框架可系统评估编码和纠缠设计对分类的影响。在重复的留存评估中，量子核模型在两个数据集上均取得了有竞争力的性能：对于片段组学，多个 20 特征配置的 AUC 优于经典 SVM 基线，提示可有效捕获非线性 cfDNA 片段化结构；对于甲基化，经典 SVM 取得最高 AUC，但选定的量子模型仍具竞争力，部分情况下提升了特异性。将特征从 20 增加到 40 未持续提升性能，且常增加变异性。总体而言，这些结果支持量子核方法作为基于 cfDNA 的肺癌检测的有前景方法。
英文摘要
Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges. Blood-based cell-free DNA (cfDNA) biomarkers offer a complementary approach, although early detection remains difficult because of lung cancer heterogeneity and high-dimensional, nonlinear molecular signals. We evaluated quantum-classical hybrid machine learning for lung cancer detection using DNA fragmentomics and DNA methylation. After feature selection, models were trained using 20- and 40-feature subsets. Features were encoded into quantum Hilbert space using angle and dense-angle feature maps with multiple entanglement strategies. Fidelity-based quantum kernels were computed with exact statevector simulation and integrated with precomputed-kernel SVM and kernel-PCA logistic regression and compared with an SVM model trained on the original features. This framework enabled systematic evaluation of how encoding and entanglement design affect classification. Across repeated held-out evaluations, quantum-kernel models achieved competitive performance on both datasets. For fragmentomics, several 20-feature configurations improved AUC relative to a classical SVM baseline, suggesting effective capture of nonlinear cfDNA fragmentation structure. For methylation, the classical SVM achieved the highest AUC, although selected quantum models remained competitive and improved specificity in some cases. Increasing features from 20 to 40 did not consistently improve performance and often increased variability. Overall, these results support quantum kernel methods as a promising approach for cfDNA-based lung cancer detection.
发表机构
- Cleveland Clinic Research（克利夫兰诊所研究院）
- IBM Quantum（IBM量子计算部门）
- IBM Thomas J Watson Research Center（IBM托马斯·J·沃森研究中心）
- Pulmonary Department, Cleveland Clinic（克利夫兰诊所肺科）
机构由 AI 辅助整理，请以论文原文为准。
