Department of Radiation Oncology, University of Washington, Seattle, WA..
Department of Radiation Oncology, University of Rochester, Rochester, NY.
Semin Radiat Oncol. 2023 Oct;33(4):386-394. doi: 10.1016/j.semradonc.2023.06.004.
The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh decision-making based on their best understanding of information at hand. These complex, multifactorial decisions have a tremendous opportunity to benefit from data-driven machine learning (ML) methods to drive opportunities in artificial intelligence (AI). Within the past 5 years, we have seen AI move from simply a promising opportunity to being used in prospective trials. Here, we review recent efforts of AI in clinical trials that have moved the needle towards improved prediction of actionable outcomes, such as predicting acute care visits, short term mortality, and pathologic extranodal extension. We then pause and reflect on how these AI models ask a different question than traditional statistics models that readers may be more familiar with; how then should readers conceptualize and interpret AI models that they are not as familiar with. We end with what we believe are promising future opportunities for AI in oncology, with an eye towards allowing the data to inform us through unsupervised learning and generative models, rather than asking AI to perform specific functions.
肿瘤学实践需要分析和综合大量数据。从患者的检查以确定资格到接受的治疗以及治疗后的监测，从业者必须不断权衡、评估和权衡决策，基于他们对手头信息的最佳理解。这些复杂的、多因素的决策有很大的机会从数据驱动的机器学习 (ML) 方法中受益，从而为人工智能 (AI) 带来机遇。在过去的 5 年中，我们已经看到人工智能从一个有前途的机会发展到在前瞻性试验中使用。在这里，我们回顾了人工智能在临床试验中的最新进展，这些进展推动了对可操作结果的预测，例如预测急性护理就诊、短期死亡率和病理淋巴结外延伸。然后，我们停下来反思这些人工智能模型提出的问题与读者可能更熟悉的传统统计模型不同；那么，读者应该如何理解和解释他们不太熟悉的人工智能模型。最后，我们着眼于人工智能在肿瘤学中未来有希望的机会，希望通过无监督学习和生成模型让数据为我们提供信息，而不是要求人工智能执行特定功能。
