用AI和机器学习预测癌症治疗后复发，提升精准医疗水平
Utilizing AI and Machine Learning for Predictive Analysis of Post-Treatment Cancer Recurrence
- 结合遗传、临床与治疗数据，用监督与无监督学习识别复发模式
- 相比传统方法显著提高复发预测准确率，支持早期干预决策
- 适合肿瘤医生与精准医疗研究者参考，推动个性化治疗
在肿瘤学中，治疗后的复发是影响患者生存率和生活质量的重大挑战。传统复发预测依赖临床观察与统计模型，难以解释肿瘤复发的复杂多因素特性。本研究探讨人工智能（AI）与机器学习（ML）模型如何提升癌症复发预测的准确性和可靠性。通过分析基因组、临床表现和治疗数据，AI与ML为个性化医疗和主动患者管理提供了新机遇。论文介绍了多种基于监督与无监督学习的AI/ML技术，用于识别癌症患者的复发模式并预测治疗结局。研究还讨论了其临床意义，强调早期干预的可能性及治疗方案设计的优化。
原文摘要 · Abstract (English)
In oncology, recurrence after treatment is one of the major challenges, related to patients' survival and quality of life. Conventionally, prediction of cancer relapse has always relied on clinical observation with statistical model support, which almost fails to explain the complex, multifactorial nature of tumor recurrence. This research explores how AI and ML models may increase the accuracy and reliability of recurrence prediction in cancer. Therefore, AI and ML create new opportunities not only for personalized medicine but also for proactive management of patients through analyzing large volumes of data on genetics, clinical manifestations, and treatment. The paper describes the various AI and ML techniques for pattern identification and outcome prediction in cancer patients using supervised and unsupervised learning. Clinical implications provide an opportunity to review how early interventions could happen and the design of treatment planning.
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