Learning from drops: AI-guided integration of liquid biopsy features in cancer studies.
6 affiliations
- 1Genetic and Molecular Epidemiology Group (GMEG), Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
- 2CIBERONC, Madrid, Spain.
- 3Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
- 4European Liquid Biopsy Society, Hamburg, Germany.
- 5Laboratory of Rare Human Circulating Cells (LCCRH) and Liquid Biopsy, University Medical Centre of Montpellier (CHUM), Montpellier, France.
- 6CREEC (CREES), Unité Mixte de Recherches, IRD 224-CNRS 5290-Université de Montpellier, Montpellier, France.
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.
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BibTeX
@article{andueza2026learning,
title = {{Learning from drops: AI-guided integration of liquid biopsy features in cancer studies.}},
author = {Mireia Andueza and Pablo Villoslada-Blanco and Brune De Dreuille and Lola Alonso and Sergio Sabroso-Lasa and Klaus Pantel and Catherine Alix-Panabières and Evangelina López De Maturana and Núria Malats},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1937336},
url = {https://europepmc.org/article/MED/42996708},
note = {CC BY},
}
RIS
TY - JOUR
TI - Learning from drops: AI-guided integration of liquid biopsy features in cancer studies.
AU - Mireia Andueza
AU - Pablo Villoslada-Blanco
AU - Brune De Dreuille
AU - Lola Alonso
AU - Sergio Sabroso-Lasa
AU - Klaus Pantel
AU - Catherine Alix-Panabières
AU - Evangelina López De Maturana
AU - Núria Malats
PY - 2026
DA - 2026/10/23
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1937336
UR - https://europepmc.org/article/MED/42996708
AB - 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.
SN - 2624-8212
ER -