AI-driven precision oncology has the transformative potential to reshape
cancer treatment by leveraging the power of AI models to analyze the
interaction between complex patient characteristics and their corresponding
treatment outcomes. New technological platforms have facilitated the timely
acquisition of multimodal data on tumor biology at an unprecedented resolution,
such as single-cell multi-omics data, making this quality and quantity of data
available for data-driven improved clinical decision-making. In this work, we
propose a modular machine learning framework designed for personalized
counterfactual cancer treatment suggestions based on an ensemble of machine
learning experts trained on diverse multi-omics technologies. These specialized
counterfactual experts per technology are consistently aggregated into a more
powerful expert with superior performance and can provide both confidence and
an explanation of its decision. The framework is tailored to address critical
challenges inherent in data-driven cancer research, including the
high-dimensional nature of the data, and the presence of treatment assignment
bias in the retrospective observational data. The framework is showcased
through comprehensive demonstrations using data from in-vitro and in-vivo
treatment responses from a cohort of patients with ovarian cancer. Our method
aims to empower clinicians with a reality-centric decision-support tool
including probabilistic treatment suggestions with calibrated confidence and
personalized explanations for tailoring treatment strategies to multi-omics
characteristics of individual cancer patients.

AI 驱动的精准肿瘤治疗具有转变癌症治疗的潜力，通过利用 AI 模型分析复杂患者特征与相应治疗结果的相互作用，以及新技术平台提供的多模态肿瘤生物学数据，开展数据驱动的临床决策改进研究。该研究提出了一个模块化机器学习框架，旨在通过一组训练于多种多组学技术的机器学习专家，为个性化反事实癌症治疗建议提供支持，并提供决策的置信度和解释。该框架解决了数据驱动癌症研究中的关键挑战，并通过使用卵巢癌患者的体外和体内治疗反应数据，全面演示了其效用，旨在赋予临床医生一种现实中心的决策支持工具，以根据个体癌症患者的多组学特征来个性化治疗策略。