机器学习在癌症患者冠心病诊疗中的典型应用

Typical application of machine learning in the diagnosis and treatment of coronary heart disease in cancer patients

摘要:

随着癌症生存率的显著提高,冠心病已成为癌症长期幸存者的主要非癌症死因。癌症患者相较于普通患者,面临着特定的风险挑战,包括抗肿瘤治疗引起的心血管毒性、临床表现的非典型性,以及传统风险评估工具在适用性上的局限性。这些因素共同导致癌症患者的冠心病诊疗过程变得尤为复杂,具有挑战性。近年来,机器学习(ML)在医学领域迅速发展,为优化癌症患者冠心病的诊疗过程,研究者们开发了大量整合临床特征、影像学信息和实验室检查等多模态信息的ML模型。ML的应用不仅提升了癌症患者冠心病的风险预测能力和早期筛查的敏感度,避免了传统诊断模式中的主观误差,还能指导患者进行个性化治疗,改善患者预后。该文通过总结ML在冠心病的风险预测、诊断优化和治疗决策中的典型应用,探讨ML在冠心病诊疗中的研究现状和所面临的挑战,并展望其未来的临床应用趋势。

Abstract:

With the significant improvement of cancer survival rate, coronary heart disease has become the main non-cancer cause of death in long-term cancer survivors. Cancer patients have specific risk challenges compared with ordinary patients, including cardiovascular toxicity caused by anti-tumor therapy, atypical clinical manifestations, and limitations in the applicability of traditional risk assessment tools. This makes the diagnosis and treatment of coronary heart disease in cancer patients challenging. In recent years, machine learning (ML) has developed rapidly in the medical field.In order to optimize the diagnosis and treatment process of coronary heart disease in cancer patients, researchers have developed a large number of ML models that integrate multimodal information such as clinical features, imaging information and laboratory examination. The application of ML improves the predictive ability of coronary heart disease risk and the sensitivity of early screening in cancer patients, and avoids subjective errors in traditional diagnostic models. It can also guide doctors to implement personalized treatment for patients and improve their prognosis. This review summarizes the typical application of ML in the diagnosis and treatment of coronary heart disease, discusses the research status and challenges of ML in the diagnosis and treatment of coronary heart disease, and looks forward to its future clinical application trend.