AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.

Authors

Antony A,Mukherjee S,Bi Y,Collisson EA,Nagaraj M,Murlidhar M,Wallace MB,Goenka AH

Affiliations (4)

  • Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Department of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL, USA.
  • Department of Medical Oncology, Fred Hutchinson Cancer Center, Seattle, WA, USA.
  • Department of Radiology, Mayo Clinic, Rochester, MN, USA. [email protected].

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.

Topics

Pancreatic NeoplasmsArtificial IntelligenceCarcinoma, Pancreatic DuctalImage Interpretation, Computer-AssistedJournal ArticleReview

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