Journal of advanced research · 2026
Artificial intelligence-driven early screening and diagnosis of pancreatic cancer: technical innovations, clinical applications, and precision medicine strategies.
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Li Yan-Rong, Li Dan, Zhou Yi-Wei, Wang Wen-Er, Ma Yu-Shui, Liu Xin-Yun, Yang Qin-Xin, Lu Cui-Ni, Cai Yue-Feng, Yang Chun, Chu Kai-Jian, Dong Hui, Yu Hong, Fu Da, Wu Wen-Guang, Zhang Yang, Xue Pei
Аннотация
BACKGROUND: Pancreatic cancer is characterized by prolonged subclinical progression, molecular heterogeneity, and late clinical presentation, resulting in diagnosis predominantly at advanced stages. Current screening approaches lack sufficient sensitivity and scalability, underscoring the need for risk-adapted early detection strategies. Artificial intelligence (AI) offers a shift from reactive diagnosis toward proactive, precision-oriented screening.
AIM OF REVIEW: This review synthesizes recent advances in AI for the early screening and diagnosis of pancreatic cancer. We focus on how AI enables population-level and high-risk prediction, augments diagnostic assessment in patients with suspicious clinical, imaging, or molecular findings, and supports precision stratification through multimodal integration of radiologic imaging, circulating biomarkers, and longitudinal electronic health records (EHRs).
KEY SCIENTIFIC CONCEPTS OF REVIEW: Advances span three domains. In imaging, deep learning models-including convolutional neural networks, transformer architectures, and self-configuring segmentation frameworks-improve pancreas segmentation, lesion detection, and classification, with several systems demonstrating radiologist-level performance in retrospective multicenter studies. In biomarker discovery, machine learning approaches such as LASSO, random forest, and XGBoost facilitate high-dimensional feature selection from transcriptomic, metabolomic, and exosomal data, enabling composite diagnostic signatures beyond CA19-9. In longitudinal EHR analysis, temporal deep learning models identify latent disease trajectories and predict pancreatic cancer risk months to years before clinical diagnosis. Despite these advances, most models remain retrospectively validated and face limitations related to data heterogeneity, interpretability, and cross-population generalizability.
CONCLUSION: AI strengthens early detection through multimodal integration, risk-adapted stratification, and data-driven clinical support aligned with precision medicine. Its near-term value lies in augmenting detection among high-risk populations rather than enabling universal screening or autonomous diagnosis. Prospective multicenter validation and improved model transparency are critical for translation into routine practice.
AIM OF REVIEW: This review synthesizes recent advances in AI for the early screening and diagnosis of pancreatic cancer. We focus on how AI enables population-level and high-risk prediction, augments diagnostic assessment in patients with suspicious clinical, imaging, or molecular findings, and supports precision stratification through multimodal integration of radiologic imaging, circulating biomarkers, and longitudinal electronic health records (EHRs).
KEY SCIENTIFIC CONCEPTS OF REVIEW: Advances span three domains. In imaging, deep learning models-including convolutional neural networks, transformer architectures, and self-configuring segmentation frameworks-improve pancreas segmentation, lesion detection, and classification, with several systems demonstrating radiologist-level performance in retrospective multicenter studies. In biomarker discovery, machine learning approaches such as LASSO, random forest, and XGBoost facilitate high-dimensional feature selection from transcriptomic, metabolomic, and exosomal data, enabling composite diagnostic signatures beyond CA19-9. In longitudinal EHR analysis, temporal deep learning models identify latent disease trajectories and predict pancreatic cancer risk months to years before clinical diagnosis. Despite these advances, most models remain retrospectively validated and face limitations related to data heterogeneity, interpretability, and cross-population generalizability.
CONCLUSION: AI strengthens early detection through multimodal integration, risk-adapted stratification, and data-driven clinical support aligned with precision medicine. Its near-term value lies in augmenting detection among high-risk populations rather than enabling universal screening or autonomous diagnosis. Prospective multicenter validation and improved model transparency are critical for translation into routine practice.