Keyword: Liquid Biopsy

ABSTRACT: Cancer behaves as a dynamic evolutionary system, whereas conventional tissue biopsy and imaging provide incomplete temporal information about how a tumour is changing. This review critically examines whether longitudinal liquid-biopsy measurement, combined with artificial intelligence (AI), can move oncology from tumour monitoring toward genuine prediction of cancer evolution and treatment resistance. We synthesise mechanistic, clinical, and computational evidence on circulating tumour DNA, circulating tumour cells, extracellular vesicles, and multimodal liquid-biopsy platforms, together with AI approaches applied to longitudinal cancer data, drawing on landmark and recent studies across colorectal, lung, breast, and ovarian cancer. Current evidence distinguishes three increasingly demanding tasks: early detection, longitudinal monitoring, and genuine forecasting. Longitudinal circulating tumour DNA analysis can detect molecular progression and emerging resistance mutations months before radiographic progression, and machine-learning models can stratify survival using serial biomarker metrics; however, most published "AI prediction" studies demonstrate earlier detection or prognostic association rather than prospectively validated forecasting of a future, patient-specific event. Biological variability in tumour shedding, analytical noise, small and irregularly sampled longitudinal datasets, overfitting, data leakage, weak external validation, and limited interpretability constrain the transition from detection to actionable prediction. AI-enabled liquid biopsy has genuine potential to forecast tumour trajectories, but current evidence is considerably stronger for molecular monitoring and early detection than for prospectively validated, clinically actionable prediction. Realising this potential will require standardised assays, prospective longitudinal cohorts with predefined prediction horizons, rigorous external and temporal validation, calibrated and interpretable models, and interventional trials linking predictions to treatment decisions before AI-enabled liquid biopsy can be considered ready to guide clinical care.

ABSTRACT: Liquid biopsies have developed as a revolutionary technique in cancer diagnosis, treatment evaluation, and the detection of therapeutic resistance. Unlike traditional tissue biopsies, which are invasive and limited to a single temporal analysis, liquid biopsies offer a non-invasive, real-time evaluation of tumour dynamics through the analysis of biomarkers such as circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), exosomes, and microRNAs. This approach enables continuous monitoring of tumour advancement, allowing for the early detection of cancer, the tracking of minimal residual disease, and the identification of emerging resistance mutations. As cancers advance and acquire resistance to therapies, liquid biopsy provides critical information that enables clinicians to customise treatment strategies and improve outcomes. Despite challenges such as sensitivity limitations in early-stage cancers and the necessity for standardised testing protocols, technological advancements, including next-generation sequencing (NGS), CRISPR, and AI-driven analytics, are enhancing the precision and accessibility of liquid biopsies. Through ongoing validation and cost-reduction efforts, liquid biopsies are set to become essential to precision oncology, offering a transformative approach to cancer therapy that could improve patient outcomes and foster equitable healthcare globally.