Akademik Veri Yönetim Sistemi
Microchemical Journal, cilt.215, 2025 (SCI-Expanded)
Pancreatic cancer remains one of the most lethal malignancies due to its asymptomatic progression, late-stage diagnosis, and limited treatment options. Current diagnostic modalities including imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS), as well as Carbohydrate Antigen 19–9 (CA 19–9) biomarker testing still fall short of the sensitivity and specificity required for reliable early-stage detection, which may contribute to the persistently elevated mortality rates. Liquid biopsy has emerged as a minimally invasive alternative, enabling the detection of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers. However, single-biomarker approaches remain inadequate in capturing the molecular complexity of pancreatic cancer. This review presents a novel, integrative framework that combines advances in multi-omics biosensor technologies and artificial intelligence (AI)-driven analytics to overcome the limitations of traditional diagnostic tools. Unlike existing reviews, this work offers a comprehensive examination of biosensors tailored for liquid biopsy applications covering electrochemical, optical, and nanomaterial-enhanced systems and evaluates their capacity to detect a broad range of circulating biomarkers, including ctDNA, CTCs, exosomes, and metabolomic signatures. Unlike conventional biosensors, multi-omics biosensors utilize advanced nanomaterials, electrochemical and optical detection systems, and AI-driven predictive modeling to enhance diagnostic precision. The integration of AI enables the real-time interpretation of multi-layered biological data, optimizing sensitivity and specificity while supporting early-stage screening and personalized treatment strategies. The application of AI in interpreting complex biological datasets is critically evaluated, particularly in the context of early diagnosis, personalized treatment planning, and risk stratification in pancreatic cancer. In addition to highlighting scientific advances, this review also discusses key translational challenges such as regulatory hurdles, standardization needs, and cost-effectiveness. By bridging the gap between molecular diagnostics, biosensor engineering, and AI-powered analytics, this review provides a foundational perspective for the next generation of precision tools in pancreatic cancer diagnostics. This review serves as a critical resource for researchers and clinicians, offering insights into the next generation of precision oncology tools that integrate biosensing, AI, and liquid biopsy methodologies.