ArticleWorld journal of gastroenterology2020
Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis.
Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Evaluating deep learning models for pancreatic cancer diagnosis.Clinical and experimental medicine · 2026Article
- Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.BMC cancer · 2025Article
- Pancreatic cancer in 2025: Have we found a solution?World journal of gastroenterology · 2025Review
- Deep learning-based automatic detection of pancreatic ductal adenocarcinoma ≤ 2 cm with high-resolution computed tomography: impact of the combination of tumor mass detection and indirect indicator evaluation.Japanese journal of radiology · 2025Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Early detection of pancreatic cancer on computed tomography: advancements with deep learning.Radiology advances · 2025Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Diagnostic methods for pancreatic cancer and their clinical applications (Review).Oncology letters · 2025Review
- Combining multimodal medical imaging and artificial intelligence for the early diagnosis of pancreatic cancer.Frontiers in medicine · 2025Article
- Automated CAD system for early detection and classification of pancreatic cancer using deep learning model.PloS one · 2025Article
- Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma.Cancers · 2024Article
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- Exploring the application and future outlook of Artificial intelligence in pancreatic cancer.Frontiers in oncology · 2024Review
- Weakly supervised large-scale pancreatic cancer detection using multi-instance learning.Frontiers in oncology · 2024Article
- Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells.Analytical and bioanalytical chemistry · 2023Article
- Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT.European radiology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEfforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.
aimTo identify pancreatic cancer in computed tomography (CT) images automatically by constructing a convolutional neural network (CNN) classifier.
methodsA CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018. We established three datasets from these images according to the image phases, evaluated the approach in terms of binary classification (
resultsThe overall diagnostic accuracy of the trained binary classifier was 95.47%, 95.76%, 95.15% on the plain scan, arterial phase, and venous phase, respectively. The sensitivity was 91.58%, 94.08%, 92.28% on three phases, with no significant differences (
conclusionWe proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images. It was suitable for screening purposes in pancreatic cancer detection.
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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.