Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary Features Article Swipe
Mark Ramaekers
,
Christiaan G. A. Viviers
,
Terese Hellström
,
Lotte J S Ewals
,
Nick Tasios
,
Igor Jacobs
,
Joost Nederend
,
Fons van der Sommen
,
Misha Luyer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/cancers16132403
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/cancers16132403
The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.
Related Topics
Concepts
Pancreatic cancer
Medicine
Receiver operating characteristic
Pancreatic duct
Pancreatic head
Pancreatic ductal adenocarcinoma
Radiology
Adenocarcinoma
Cancer detection
Cancer
Pancreas
Nuclear medicine
Internal medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/cancers16132403
- https://www.mdpi.com/2072-6694/16/13/2403/pdf?version=1719652902
- OA Status
- gold
- Cited By
- 13
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400202608
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400202608Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/cancers16132403Digital Object Identifier
- Title
-
Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary FeaturesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-29Full publication date if available
- Authors
-
Mark Ramaekers, Christiaan G. A. Viviers, Terese Hellström, Lotte J S Ewals, Nick Tasios, Igor Jacobs, Joost Nederend, Fons van der Sommen, Misha LuyerList of authors in order
- Landing page
-
https://doi.org/10.3390/cancers16132403Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-6694/16/13/2403/pdf?version=1719652902Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-6694/16/13/2403/pdf?version=1719652902Direct OA link when available
- Concepts
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Pancreatic cancer, Medicine, Receiver operating characteristic, Pancreatic duct, Pancreatic head, Pancreatic ductal adenocarcinoma, Radiology, Adenocarcinoma, Cancer detection, Cancer, Pancreas, Nuclear medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 12, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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