# Liquid Biopsy in Early Screening of Cancers: Emerging Technologies and New Prospects

## TL;DR

Liquid biopsy is evolving to use multiple biological signals for early cancer detection, improving accuracy and guiding treatment decisions.

## Contribution

The paper proposes a pathway-aware workflow integrating blood-based risk scoring with imaging and secondary testing for early cancer detection.

## Key findings

- Fragmentomic and methylation features combined with radiomics enhance high-specificity cancer risk stratification.
- Multimodal liquid biopsy systems improve early prediction of treatment response and relapse.
- Standardized workflows and error suppression are critical for clinical implementation of liquid biopsy.

## Abstract

Liquid biopsy is moving beyond mutation-centric assays to multimodal frameworks that integrate cell-free DNA (cfDNA) signals with additional analytes such as circulating tumor cells (CTCs) and extracellular vesicles (EVs). In this review, we summarize emerging technologies across analytes for early cancer detection, emphasizing sequencing and error-suppression strategies and the growing evidence for multi-cancer early detection (MCED), tissue-of-origin (TOO) inference, diagnostic triage, and longitudinal surveillance. At low tumor fractions, fragmentomic and methylation features preserve tissue and chromatin context; when combined with radiomics using deep learning, they support blood-first, high-specificity risk stratification, increase positive predictive value (PPV), reduce unnecessary procedures, and enhance early prediction of treatment response and relapse. Building on these findings, we propose a pathway-aware workflow: initial blood-based risk scoring, followed by organ-directed imaging, and targeted secondary testing when indicated. We further recommend that model reports include not only discrimination metrics but also calibration, decision-curve analysis, PPV/negative predictive value (NPV) at fixed specificity, and TOO accuracy, alongside multi-site external validation and blinded dataset splits to improve generalizability. Overall, liquid biopsy is transitioning from signal discovery to deployable multimodal decision systems; standardized pre-analytical and analytical workflows, robust error suppression, and prospective real-world evaluations will be pivotal for clinical implementation.