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Review

Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging

by
Megan Schuurmans
1,*,†,
Natália Alves
1,*,†,
Pierpaolo Vendittelli
1,
Henkjan Huisman
1,† and
John Hermans
2,†
1
Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
2
Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2022, 14(14), 3498; https://doi.org/10.3390/cancers14143498
Submission received: 6 June 2022 / Revised: 7 July 2022 / Accepted: 15 July 2022 / Published: 19 July 2022
(This article belongs to the Special Issue Feature Review Papers on "Cancer Causes, Screening and Diagnosis")

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

Abstract

Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
Keywords: pancreatic cancer; artificial intelligence; imaging; radiology; pathology pancreatic cancer; artificial intelligence; imaging; radiology; pathology

Share and Cite

MDPI and ACS Style

Schuurmans, M.; Alves, N.; Vendittelli, P.; Huisman, H.; Hermans, J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers 2022, 14, 3498. https://doi.org/10.3390/cancers14143498

AMA Style

Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers. 2022; 14(14):3498. https://doi.org/10.3390/cancers14143498

Chicago/Turabian Style

Schuurmans, Megan, Natália Alves, Pierpaolo Vendittelli, Henkjan Huisman, and John Hermans. 2022. "Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging" Cancers 14, no. 14: 3498. https://doi.org/10.3390/cancers14143498

APA Style

Schuurmans, M., Alves, N., Vendittelli, P., Huisman, H., & Hermans, J. (2022). Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers, 14(14), 3498. https://doi.org/10.3390/cancers14143498

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