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Purpose of Review <p>Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease often diagnosed too late for effective intervention. Early detection offers the best opportunity to improve survival, but no universal screening strategy exists. This review highlights recent advancements in risk stratification, diagnostic modalities, and artificial intelligence (AI)-based approaches for early detection, focusing on their clinical utility, current state, and future potential.</p> Recent Findings <p>Advances in risk stratification have improved identification of high-risk cohorts based on familial and germline risk, resulting in expert consensus supporting clinical screening. New-onset diabetes (NOD) risk modeling has emerged as a promising tool for identifying sporadic PDAC. AI-driven models using electronic medical records and multimodal datasets are in development, showing promise for individualized risk prediction. Imaging modalities (CT, MRI, EUS) are being refined for higher sensitivity, with AI applications enhancing detection of subtle, pre-diagnostic tumors. Biomarker research is advancing, with multi-omic panels and liquid biopsy assays improving early-stage detection. Novel molecular analyses of pancreatic juice and cyst fluid aid in identifying high-grade precursor lesions and early PDAC.</p> Summary <p>Innovations in imaging, biomarkers, and AI are reshaping PDAC early detection. A multimodal strategy targeting broader high-risk groups may enable earlier diagnosis and improved outcomes.</p>

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Early Detection of Pancreatic Cancer: Recent Advancements

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Tom Konikoff,
  • Shounak Majumder

摘要

Purpose of Review

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease often diagnosed too late for effective intervention. Early detection offers the best opportunity to improve survival, but no universal screening strategy exists. This review highlights recent advancements in risk stratification, diagnostic modalities, and artificial intelligence (AI)-based approaches for early detection, focusing on their clinical utility, current state, and future potential.

Recent Findings

Advances in risk stratification have improved identification of high-risk cohorts based on familial and germline risk, resulting in expert consensus supporting clinical screening. New-onset diabetes (NOD) risk modeling has emerged as a promising tool for identifying sporadic PDAC. AI-driven models using electronic medical records and multimodal datasets are in development, showing promise for individualized risk prediction. Imaging modalities (CT, MRI, EUS) are being refined for higher sensitivity, with AI applications enhancing detection of subtle, pre-diagnostic tumors. Biomarker research is advancing, with multi-omic panels and liquid biopsy assays improving early-stage detection. Novel molecular analyses of pancreatic juice and cyst fluid aid in identifying high-grade precursor lesions and early PDAC.

Summary

Innovations in imaging, biomarkers, and AI are reshaping PDAC early detection. A multimodal strategy targeting broader high-risk groups may enable earlier diagnosis and improved outcomes.