Towards precision oncology in pancreatic cancer: Diagnostic and prognostic biomarkers from conventional methods to liquid biopsy and artificial intelligence-based approaches.

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Abstract

Pancreatic cancer (PC) is known as one of the most aggressive and deadly tumors, with pancreatic ductal adenocarcinoma (PDAC) predicted to be the second leading cause of cancer death by 2030. Also, due to the lack of specific symptoms and limitations of biomarkers and screening methods for early diagnosis, most patients are diagnosed at advanced stages and with metastatic disease. Therefore, developing new, high-accuracy diagnostic methods for early diagnosis and monitoring of treatment response in patients is very important for providing appropriate treatment options and improving survival rates. In recent years, despite the progress in diagnostic methods such as imaging techniques, tissue biopsy, liquid biopsy (LB), and tumor marker testing, there are many uncertainties about the specificity and sensitivity of these technologies, as well as challenges in their practical use in the clinic. Also, advancements in artificial intelligence (AI) technology in the field of cancer detection have enabled the screening and early diagnosis of PC in high-risk populations by employing AI algorithms with high computational power in conjunction with biomarkers, medical imaging, and pathological tests. In this article, we comprehensively evaluate the potential and diagnostic effectiveness of established tools for PC, alongside emerging and promising approaches, including liquid biopsy and AI-based methods. We examine their applications in early diagnosis, screening, prognostication, and disease monitoring, while also discussing the key characteristics, advantages, and limitations of each approach, as well as the potential benefits of their integration. Finally, we provide clinical recommendations aimed at improving the early detection of this challenging disease.

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