Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection
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Computer Science > Machine Learning
Title:Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detection
Abstract:Cell-free DNA (cfDNA) is a promising avenue for non-invasive multicancer early detection (MCED), in that, it can enable multiple cancer detection simultaneously from a single blood draw, with particular sensitivity to cancers that currently lack established screening programs. Here we review the computational methods developed between 2022 and 2025 for cfDNA-based MCED. We focus on how fragmentomics and epigenetic features are extracted and analyzed to detect cancer at early stages. We first briefly outline the biological basis of cfDNA signals, then review classical statistical and machine learning approaches alongside deep learning frameworks including autoencoder-based models. For each method we discuss biological interpretability, validation strategy, and readiness for clinical integration. Furthermore, we categorize the current challenges into technical, computational, and methodological while outlining open problems in the field. This review shows that multimodal ensemble approaches have the strongest promise for clinical integration and the highest readiness. However, for better assessment of future work and side-by-side comparison, standardization of evaluation protocols and reporting results will be crucial.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.20174 [cs.LG] |
| (or arXiv:2606.20174v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.20174
arXiv-issued DOI via DataCite (pending registration)
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