[论文解读] Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data

Manuel Schürch, Laura Boos+5 more|arXiv (Cornell University)|Feb 19, 2024

Radiomics and Machine Learning in Medical Imaging被引用 5

一句话总结

本论文提出一个针对多组学数据训练的专门对照事实专家的模块化集合框架，用以提出个性化、置信度校准的对照事实治疗建议，应用于癌症，使用卵巢癌数据进行示例。

ABSTRACT

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.

研究动机与目标

• 通过从回顾性多组学数据中学习个性化的对照事实治疗结果，推动AI驱动的精准肿瘤治疗。
• 开发一个健壮的、模块化且可解释的ML框架，跨技术聚合专门专家。
• 提供置信估计和解释，以支持在现实世界高维数据中的临床决策。
• 应对观测数据中的高维性、缺失模态和治疗指派偏差等挑战。

提出的方法

• 在Neyman-Rubin框架内为多组学患者数据定义个性化对照事实结局模型。
• 为每种技术和每种治疗创建专门的对照事实专家，并通过一致性融合（基于协方差交集）将它们聚合为一个元专家。
• 为每个专家估计 mu_a(x) 与 nu_a(x)，并将置信度 lambda_a(x) 计算为不确定性的逆。
• 使用SHAP值聚合解释 chi_a(x)，并通过带权并集在跨技术之间融合它们。
• 通过最大化基于 mu_a(x) 的效用并考虑置信度，选择前K个治疗。

实验结果

研究问题

• RQ1 是否可以从回顾性多组学观测数据中学习个性化肿瘤治疗的对照事实结局？
• RQ2 相较于单一组学基线，面向技术特定专家的模块化集合在预测体外和体内结局方面表现如何？
• RQ3 不确定性感知的专家聚合是否在现实世界数据中提升个性化治疗建议和解释？
• RQ4 跨组学模态中，哪些解释和生物标志物成为治疗建议的关键影响因素？

主要发现

• 专门专家集合在预测体外和体内结局方面通常优于单一专家和基线。
• 聚合后的专家预测在体内数据上的AUROC和准确率范围为0.70至0.90，平均聚合专家约为0.75。
• 只有一部分技术专家的预测优于随机，聚合方法抑制了表现不佳的专家。
• 该框架提供个性化解释（基于SHAP）和治疗建议，因患者而异，反映出异质的治疗效应。
• 该方法在包含61名多组学数据的卵巢癌队列上展示了可行性，使用留一法和嵌套交叉验证来估计性能和置信度。