AI Insight
This review article examines the current state of artificial intelligence-based research integrating liquid biopsy features in oncology, finding that such integrative studies remain sparse in the published literature. The authors provide a methodological framework covering key steps including study design, patient selection, data preprocessing, normalization, batch correction, and machine learning or deep learning approaches for feature selection. They also emphasize the importance of rigorous internal and external model validation, as well as clinical interpretability, which they identify as frequently neglected aspects in this research domain.
Why it matters
Liquid biopsy offers a minimally invasive means of detecting and monitoring cancer through biomarkers in body fluids, and standardized AI-based analytical pipelines could accelerate its translation into clinical practice. This guidance may help researchers design more robust and reproducible studies, potentially improving early cancer detection and treatment monitoring.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide 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 the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as 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 validations 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 an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features.
Source: Learning from Drops: AI-Guided Integration of Liquid Biopsy Features in Cancer Studies