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

• URL: http://arxiv.org/abs/2402.12190v2
• Date: Wed, 20 Mar 2024 21:21:48 GMT
• Title: Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data
• Authors: Manuel Schürch, Laura Boos, Viola Heinzelmann-Schwarz, Gabriele Gut, Michael Krauthammer, Andreas Wicki, Tumor Profiler Consortium,
• Abstract summary: We propose a modular machine learning framework designed for personalized counterfactual cancer treatment suggestions. The framework is tailored to address critical challenges inherent in data-driven cancer research. Our method aims to empower clinicians with a reality-centric decision-support tool.
• Score: 0.05025737475817938
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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