Artificial Intelligence in Pancreatic Cancer: From Predictive Performance to Clinical Translation
- 1Department of Hepatopancreatobiliary Surgery, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel and Europe Hospitals, 1090 Brussels, Belgium
Abstract
Background: Artificial intelligence (AI) is now applied across the pancreatic ductal adenocarcinoma (PDAC) pathway, yet the literature has grown faster than the evidence needed to act on it. The question is not whether an algorithm can predict an endpoint, but whether it yields information reliable, generalizable, and actionable enough to change a clinically meaningful decision. Methods: In this narrative, in a non-systematic review of PDAC-specific literature, we grade applications on an author-defined evidence-readiness framework (development, internal, external, prospective validation, clinical impact, and implementation) and on four dimensions—validation, comparator, actionability, and impact. Findings: Contemporary meta-analyses show high pooled accuracy for AI-based early detection and for distinguishing PDAC from mass-forming pancreatitis (sensitivity ~0.88–0.92, specificity ~0.90–0.93), but with extreme heterogeneity, low radiomics quality, scarce external validation, and—for electronic health-record risk models—positive predictive values often below 1% at population prevalence. Computed tomography (CT)-based detection and risk enrichment are among the more mature applications identified, yet none has been shown to improve stage distribution, resection rate, or survival. Treatment prediction is held to a strict standard, distinguishing prognostic association from a validated treatment-by-biomarker interaction. Conclusions: The rate-limiting steps are no longer algorithmic but translational—external and prospective validation, calibration, appropriate comparators, and workflow integration.
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BibTeX
@article{khan2026artificial,
title = {{Artificial Intelligence in Pancreatic Cancer: From Predictive Performance to Clinical Translation}},
author = {Ikra Khan and Azzadinne Belhaj and Alessandro Gemini and Kenza Azra Ibis and Nouman Darsif and Najoua Rouani and Aude Vanlander and Nouredin Messaoudi},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18203253},
url = {https://www.mdpi.com/2072-6694/18/20/3253},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Pancreatic Cancer: From Predictive Performance to Clinical Translation
AU - Ikra Khan
AU - Azzadinne Belhaj
AU - Alessandro Gemini
AU - Kenza Azra Ibis
AU - Nouman Darsif
AU - Najoua Rouani
AU - Aude Vanlander
AU - Nouredin Messaoudi
PY - 2026
DA - 2026/10/09
JO - Cancers
DO - 10.3390/cancers18203253
UR - https://www.mdpi.com/2072-6694/18/20/3253
AB - Background: Artificial intelligence (AI) is now applied across the pancreatic ductal adenocarcinoma (PDAC) pathway, yet the literature has grown faster than the evidence needed to act on it. The question is not whether an algorithm can predict an endpoint, but whether it yields information reliable, generalizable, and actionable enough to change a clinically meaningful decision. Methods: In this narrative, in a non-systematic review of PDAC-specific literature, we grade applications on an author-defined evidence-readiness framework (development, internal, external, prospective validation, clinical impact, and implementation) and on four dimensions—validation, comparator, actionability, and impact. Findings: Contemporary meta-analyses show high pooled accuracy for AI-based early detection and for distinguishing PDAC from mass-forming pancreatitis (sensitivity ~0.88–0.92, specificity ~0.90–0.93), but with extreme heterogeneity, low radiomics quality, scarce external validation, and—for electronic health-record risk models—positive predictive values often below 1% at population prevalence. Computed tomography (CT)-based detection and risk enrichment are among the more mature applications identified, yet none has been shown to improve stage distribution, resection rate, or survival. Treatment prediction is held to a strict standard, distinguishing prognostic association from a validated treatment-by-biomarker interaction. Conclusions: The rate-limiting steps are no longer algorithmic but translational—external and prospective validation, calibration, appropriate comparators, and workflow integration.
SN - 2072-6694
ER -