Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.

Abstract

IntroductionMachine learning (ML)-based analysis of cell-free DNA (cfDNA) has emerged as a promising strategy for multi-cancer early detection (MCED). However, reported diagnostic performance varies widely across studies, and many estimates are derived from training or enriched cohorts, limiting their relevance to independent validation and real-world settings.MethodsWe conducted a systematic review and diagnostic accuracy meta-analysis of ML-based cfDNA assays for MCED. Four databases (PubMed, Embase, Web of Science, and the Cochrane Library) were searched from inception to February 2, 2025. Only independent validation or testing datasets were included; all training datasets were excluded. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves were estimated using a bivariate random-effects model. Subgroup analyses and meta-regression were performed to explore sources of heterogeneity.ResultsThirteen studies comprising 23 independent datasets and 14,892 participants were included. The pooled sensitivity was 0.78 (95% CI: 0.66-0.87), and the pooled specificity was 0.96 (95% CI: 0.90-0.98). The summary area under the curve (AUC) was 0.94, with a DOR of 76.6. Substantial between-study heterogeneity was observed (I² > 90%), with geographic region, sample size, and cfDNA biomarker type identified as major contributing factors.ConclusionML-based cfDNA assays demonstrate consistently high specificity and moderate-to-high sensitivity across independent validation datasets, supporting their potential role in multi-cancer early detection. However, diagnostic performance is highly context dependent and strongly influenced by study design, population characteristics, and analytical choices. These findings highlight the need for large-scale, prospective, population-based validation before widespread clinical implementation.