AI-DRIVEN LIQUID BIOPSY SYSTEMS FOR EARLY CANCER DETECTION AND PERSONALIZED ONCOLOGY

Abstract

Early cancer detection remains one of the most promising strategies for improving survival outcomes, yet current screening methods are limited by invasiveness, cost, and inadequate sensitivity for early-stage disease. This research develops a comprehensive AI-driven liquid biopsy framework that integrates circulating tumor DNA analysis, circulating tumor cells characterization, exosome profiling, protein biomarkers, and metabolomic signatures from peripheral blood samples to enable multi-cancer early detection and longitudinal monitoring for personalized oncology. We employed deep learning architectures combining convolutional neural networks for sequencing data pattern recognition, graph neural networks for mutation signature analysis, and transformer models for temporal biomarker trajectory modeling across 5,843 patients including 3,267 cancer cases across twelve tumor types and 2,576 healthy controls. The foundation model achieved 91.4% sensitivity at 95.3% specificity for pan-cancer detection, with tissue-of-origin localization accuracy of 87.6% among detected cancers. Stage I cancer detection sensitivity reached 73.8%, substantially exceeding conventional screening approaches. Longitudinal monitoring demonstrated detection of molecular relapse an average of 8.7 months before radiological progression, enabling early therapeutic intervention. Integration of multi-analyte liquid biopsy features through cross-modal attention mechanisms revealed synergistic signatures where combined ctDNA variant allele frequency, CTC enumeration, and exosomal miRNA panels achieved superior performance compared to individual biomarkers. The interpretable framework identified novel early detection signatures including tissue-specific methylation patterns, clonal hematopoiesis discrimination algorithms, and tumor evolution trajectories predicting therapeutic resistance. This AI-augmented liquid biopsy system establishes a scalable, minimally invasive approach for population-level cancer screening, minimal residual disease monitoring, and real-time treatment response assessment.