Artificial Intelligence, ctDNA, and Biomarker-Driven Precision Oncology: Emerging Molecular Strategies for Cancer Diagnosis and Therapeutic Response Prediction

Abstract

Precision oncology has emerged as a transformative paradigm in cancer diagnosis and therapeutic decision-making through the integration of molecular diagnostics, genomic profiling, artificial intelligence (AI), and translational medicine. Conventional oncology approaches based primarily on histopathological classification frequently fail to address the extensive intertumoral and intratumoral heterogeneity that contributes to therapeutic resistance, metastatic progression, and variable clinical outcomes. Recent advances in next-generation sequencing (NGS), circulating tumor DNA (ctDNA)-based liquid biopsy technologies, transcriptomics, multi-omics integration, and AI-assisted computational oncology have significantly improved biomarker discovery, molecular surveillance, therapeutic response prediction, and personalized treatment strategies. Machine learning and deep learning algorithms are increasingly utilized for tumor classification, digital pathology, radiomics, survival prediction, biomarker-guided therapeutic optimization, and recurrence monitoring. ctDNA-based liquid biopsy platforms further provide minimally invasive approaches for longitudinal tumor profiling, minimal residual disease (MRD) detection, clonal evolution analysis, and real-time therapeutic response monitoring. Molecular biomarkers including EGFR mutations, HER2 amplification, KRAS mutations, microsatellite instability (MSI), tumor mutational burden (TMB), and angiogenic signaling markers are increasingly incorporated into precision therapeutic frameworks. Additionally, AI-assisted integration of genomics, transcriptomics, proteomics, epigenomics, and metabolomics has accelerated translational precision medicine. Despite substantial advances, major challenges remain, including biomarker standardization, AI interpretability limitations, sequencing costs, data heterogeneity, and clinical implementation barriers. This review discusses recent advances in AI-assisted precision oncology, molecular biomarker systems, ctDNA technologies, therapeutic resistance mechanisms, translational oncology, and emerging multi-omics frameworks. The review also highlights future directions involving AI-assisted molecular surveillance, computational oncology platforms, and personalized cancer therapeutics.