Machine Learning Approaches in Multi-Cancer Early Detection

Cancer is a prominent global cause of mortality, primarily due to delayed detection leading to limited treatment options. Current screening methods are mostly invasive and involve complex lengthy processes with high costs. Moreover, each screening typically focuses on a single type of cancer. This imposes a growing need for innovative, precise, and minimally invasive methods for early cancer detection. With the current advances in assay technologies and data science, multi-cancer early detection (MCED) tests are gaining increased interest in the research community as they offer potential for earlier diagnosis and improved patient outcomes. Different approaches are followed for MCED, and multiple machine learning methods are considered. In this paper, we systematically explore various MCED studies and their applied machine learning (ML) models for different types of biomarker data. We discuss the strengths and limitations of different study designs and compare their performance. Future directions are proposed, emphasizing the importance of integrating multi-omics data, enhancing model transparency, and fostering collaborative efforts to develop robust, cost effective and clinically applicable MCED tools.