Year: 2026
Type: article
Abstract: Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely, effective treatment and ultimately reduce mortality. Liquid biopsy (LB) has emerged as a valuable detection tool, offering a non-invasive method to detect tumor-derived biomarkers in body fluids and demonstrating translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal to enabling large-scale, complex data integration. This contribution assesses the current state of integrative AI-based research in the LB field and provides methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting this approach, defining study objectives is crucial to guide subsequent methodological decisions, including study design, patient selection criteria, sample size, the nature of the LB features, and the metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch-effect correction, as well as for handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validation of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features.
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7223290464