ArticleJournal of clinical medicine2020
Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP).
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 48 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
- Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Image-based artificial intelligence for preoperative differentiation of pancreatic cancer from pancreatitis: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Development and validation of a clinical-radiomics nomogram for the differential diagnosis of focal pancreatic solid lesions: a retrospective cohort study.Translational cancer research · 2026Article
- Artificial intelligence in chronic and autoimmune pancreatitis: diagnosis, prognosis, and personalized management.Frontiers in medicine · 2026Review
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.International ophthalmology · 2025Review
- Diagnostic methods for pancreatic cancer and their clinical applications (Review).Oncology letters · 2025Review
- A PET-CT radiomics model for immunotherapy response and prognosis prediction in patients with metastatic colorectal cancer.Frontiers in oncology · 2025Article
- CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.Japanese journal of radiology · 2024Review
- Deep Learning Applications in Pancreatic Cancer.Cancers · 2024Review
- Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.BJR artificial intelligence · 2024Article
- Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.Journal of clinical medicine · 2023Review
- Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma.Nature communications · 2023Article
- Article
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Recent advances in the management of autoimmune pancreatitis in the era of artificial intelligence.World journal of gastroenterology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (
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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.