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PreprintCited by 23AI in cancer detectionRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

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Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence

Open access
Anghel, Cristian, Mugur, Grasu Cristian, Anghel, Denisa Andreea, Rusu-Munteanu, Gina Ionela, Dumitru, Radu Lucian, Lupescu, Ioana Gabriela·2024·Diagnostics
DOI 10.20944/preprints202401.0904.v1PMC PMC10887967

Preprint — published as 10.3390/diagnostics14040438

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Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignant tumor of the pancreas, carrying a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last 5 years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in detection of PDAC, segmentation of the lesion and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

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Crossref · 10.20944/preprints202401.0904.v1

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Figures

Figure 1 Portal venous phase CT: pancreatic adenocarcinoma involving the corporeal region (red circle) with less than 180° contact with superior mesentery vein (yellow arrow). Multiple liver metastases noted (blue arrow).
Figure 1 Portal venous phase CT: pancreatic adenocarcinoma involving the corporeal region (red circle) with less than 180° contact with superior mesentery vein (yellow arrow). Multiple liver metastases noted (blue arrow).CC BY · PubMed Central
Figure 2 Late-arterial phase 3D T1WI fat-suppressed GRE: pancreatic adenocarcinoma involving the head region (red circle) with less than 180° contact with celiac trunk (yellow arrow) and more than 180° contact with common hepatic artery (blue arrow).
Figure 2 Late-arterial phase 3D T1WI fat-suppressed GRE: pancreatic adenocarcinoma involving the head region (red circle) with less than 180° contact with celiac trunk (yellow arrow) and more than 180° contact with common hepatic artery (blue arrow).CC BY · PubMed Central
Figure 3 PubMed search for pancreatic adenocarcinoma and artificial intelligence.
Figure 3 PubMed search for pancreatic adenocarcinoma and artificial intelligence.CC BY · PubMed Central
Figure 4 CECT portal venous-phase showing semi-automated corporeal pancreatic tumor segmentation (depicted in green) using “growing-seeds” method.
Figure 4 CECT portal venous-phase showing semi-automated corporeal pancreatic tumor segmentation (depicted in green) using “growing-seeds” method.CC BY · PubMed Central

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References 117 · Cited by 23

Metadata

Authors
Anghel, C., Mugur, G. C., Anghel, D. A., Rusu-Munteanu, G. I., Dumitru, R. L., & Lupescu, I. G.
Affiliations
  • Carol Davila University of Medicine and Pharmacy (RO)
  • Institutul Clinic Fundeni (RO)
ORCID
Venue
Diagnostics
Publisher
MDPI AG
Published
2024-01-11
Volume
14
Issue
4
Pages
438-438
ISSN
2075-4418 (ISSN-L)
DOI
10.20944/preprints202401.0904.v1
PMC
PMC10887967
OpenAlex
W4391883983
Cited by
23
References
117
Citations by year
2026: 6 · 2025: 10 · 2024: 7
FWCI
0.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
pancreatic ductal adenocarcinoma, artificial intelligence, computed tomography, magnetic resonance imaging, tumor detection, lesion segmentation, tumor classification, differential diagnosis, outcome prediction, genomic prediction

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CoChat. (2026). Citation check: Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence. https://app.cochat.ai/papers/10.20944/preprints202401.0904.v1
The paper
Anghel, C., Mugur, G. C., Anghel, D. A., Rusu-Munteanu, G. I., Dumitru, R. L., & Lupescu, I. G. (2024). Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence. Diagnostics. https://doi.org/10.20944/preprints202401.0904.v1