ArticleCancers2024
Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary Features.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- From radiomics to transformers in pancreatic cancer detection and prognosis.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- 3D Modelling for Preoperative Planning, Intraoperative Navigation, and Training in Pancreatic Surgery: A Systematic Review.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Review
- Peptides as integrative modulators for clinical prognosis and targeted therapy in pancreatic cancer.Discover oncology · 2026Review
- Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.Scientific reports · 2026Article
- Potential of Silver Nanoparticles in Imaging Diagnostics and Image-Guided Applications: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Pancreatic multi structure segmentation on CT using transformer and deep learning approaches.Discover oncology · 2026Article
- Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.Abdominal radiology (New York) · 2026Article
- Optimized federated learning framework with RegNetZ and Swin-Transformer for multimodal pancreatic cancer detection1.Scientific reports · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.Scientific reports · 2025Article
- TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.