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Abstract

Pancreatic cancer remains one of the deadliest malignancies, with a five-year survival rate of less than 10%. Its asymptomatic nature in early stages, coupled with the aggressive progression, makes timely diagnosis challenging. This comprehensive overview delves into the current landscape of pancreatic cancer diagnosis, elucidating advancements in imaging techniques, biomarker discovery, and histopathological evaluation.

Initially, the paper examines traditional diagnostic modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS), highlighting their roles, benefits, and limitations. These imaging techniques are crucial for detecting pancreatic masses, assessing tumor resectability, and guiding biopsy procedures. The overview then transitions to the evolving landscape of molecular diagnostics. Emphasis is placed on the identification and clinical utility of circulating biomarkers, including CA 19-9, and emerging markets like microRNAs and exosomes, which offer promise for early detection and monitoring of treatment response. Histopathological evaluation, through fine-needle aspiration (FNA) and biopsy, remains the gold standard for definitive diagnosis. The review explores the techniques for obtaining high-quality tissue samples, the role of immunohistochemistry in differentiating pancreatic cancer from other pancreatic lesions, and the integration of next-generation sequencing (NGS) for comprehensive genetic profiling. The potential of liquid biopsy as a minimally invasive diagnostic tool is also discussed, with a focus on its current limitations and future prospects.

Furthermore, the overview addresses the challenges of diagnosing pancreatic cancer in the context of genetic predisposition and familial pancreatic cancer syndromes. The role of germline mutation testing and the implementation of risk assessment models for high-risk individuals are examined. The paper also considers the implications of artificial intelligence (AI) and machine learning (ML) in enhancing diagnostic accuracy and predicting patient outcomes. This in-depth overview provides a detailed examination of the multifaceted approaches to pancreatic cancer diagnosis, highlighting the critical need for early detection and the integration of innovative technologies to improve patient outcomes.