ArticleScientific reports2021
A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025Pooled it
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Artificial Intelligence in Pancreatic Endoscopic Ultrasonography: From Image-Based Diagnosis to Cytopathology.Journal of clinical medicine · 2026Review
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- Evaluation of the Usefulness of Machine Learning and Artificial Intelligence on Hyperspectral Images in the Diagnosis of Myelodysplastic Syndrome.Journal of biophotonics · 2026Article
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- Data privacy-aware machine learning approach in pancreatic cancer diagnosis.BMC medical informatics and decision making · 2024Article
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Cutting edge of endoscopic ultrasound-guided fine-needle aspiration for solid pancreatic lesions.Journal of medical ultrasonics (2001) · 2024Review
- Advances and efficacy in specimen handling for endoscopic ultrasound-guided fine needle aspiration and biopsy: A comprehensive review.DEN open · 2024Review
- Review
- Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.Journal of community hospital internal medicine perspectives · 2024Review
- Pancreatic Juice-Derived microRNA-4516 and microRNA-4674 as Novel Biomarkers for Pancreatic Ductal Adenocarcinoma.Gastro hep advances · 2024Article
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.Cancer medicine · 2023Article
- Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions.Biomedicines · 2023Review
- Tissue contamination challenges the credibility of machine learning models in real world digital pathology.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
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Authors and funding
28 authors at 16 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has become the mainstay of preoperative pathological diagnosis. However, on EUS-FNB specimens, accurate histopathological evaluation is difficult due to low specimen volume with isolated cancer cells and high contamination of blood, inflammatory and digestive tract cells. In this study, we performed annotations for training sets by expert pancreatic pathologists and trained a deep learning model to assess PDAC on EUS-FNB of the pancreas in histopathological whole-slide images. We obtained a high receiver operator curve area under the curve of 0.984, accuracy of 0.9417, sensitivity of 0.9302 and specificity of 0.9706. Our model was able to accurately detect difficult cases of isolated and low volume cancer cells. If adopted as a supportive system in routine diagnosis of pancreatic EUS-FNB specimens, our model has the potential to aid pathologists diagnose difficult cases.
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Registered trials
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