Background: Neoplasms remains a leading cause of mortality worldwide, with timely diagnosis being crucial for improving patient outcomes. Current diagnostic methods are often invasive, costly, and inaccessible to many populations. Electrocardiogram (ECG) data, widely available and non-invasive, has the potential to serve as a tool for neoplasms diagnosis by using physiological changes in cardiovascular function associated with neoplastic prescences. Methods: This study explores the application of machine learning models to analyze ECG features for the diagnosis of neoplasms. We developed a pipeline integrating tree-based models with Shapley values for explainability. The model was trained and internally validated and externally validated on a second large-scale independent external cohort to ensure robustness and generalizability. Findings: The results demonstrate that ECG data can effectively capture neoplasms-associated cardiovascular changes, achieving high performance in both internal testing and external validation cohorts. Shapley values identified key ECG features influencing model predictions, revealing established and novel cardiovascular markers linked to neoplastic conditions. This non-invasive approach provides a cost-effective and scalable alternative for the diagnosis of neoplasms, particularly in resource-limited settings. Similarly, useful for the management of secondary cardiovascular effects given neoplasms therapies. Interpretation: This study highlights the feasibility of leveraging ECG signals and machine learning to enhance neoplasms diagnostics. By offering interpretable insights into cardio-neoplasms interactions, this approach bridges existing gaps in non-invasive diagnostics and has implications for integrating ECG-based tools into broader neoplasms diagnostic frameworks, as well as neoplasms therapy management.

翻译：背景：肿瘤仍是全球主要致死原因，及时诊断对改善患者预后至关重要。当前诊断方法通常具有侵入性、成本高昂且许多人群难以获得。心电图数据作为广泛可用且非侵入性的工具，通过捕捉与肿瘤存在相关的心血管功能生理变化，具备用于肿瘤诊断的潜力。方法：本研究探索应用机器学习模型分析心电图特征以诊断肿瘤。我们开发了一个集成树模型与沙普利值的可解释性分析流程。该模型经过训练与内部验证，并在第二个大规模独立外部队列中进行外部验证，以确保稳健性与泛化能力。结果：研究表明心电图数据能有效捕捉肿瘤相关心血管变化，在内部测试与外部验证队列中均表现出优异性能。沙普利值识别出影响模型预测的关键心电图特征，揭示了与肿瘤状态相关的已知及新型心血管标志物。这种非侵入性方法为肿瘤诊断提供了经济高效且可扩展的替代方案，特别适用于资源有限环境。该方法同样适用于肿瘤治疗继发心血管效应的管理。结论：本研究证实了利用心电图信号与机器学习增强肿瘤诊断的可行性。通过提供对心脏-肿瘤相互作用可解释的洞察，该方法弥补了非侵入性诊断领域的现有空白，对将基于心电图的工具整合至更广泛的肿瘤诊断框架及肿瘤治疗管理具有重要价值。