ArticleDiagnostics (Basel, Switzerland)2021
Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From radiomics to transformers in pancreatic cancer detection and prognosis.Frontiers in medicine · 2025Pooled it
- Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI.Journal of imaging informatics in medicine · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- 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
- Application of Artificial Intelligence in Pancreatic Cyst Management: A Systematic Review.Cancers · 2025Review
- Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.PLOS digital health · 2025Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- MC3DU-Net: a multisequence cascaded pipeline for the detection and segmentation of pancreatic cysts in MRI.International journal of computer assisted radiology and surgery · 2024Article
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics.European heart journal. Cardiovascular Imaging · 2023Article
- Deep Learning to Detect Pancreatic Cystic Lesions on Abdominal Computed Tomography Scans: Development and Validation Study.JMIR AI · 2023Article
- Advances in the Diagnosis of Pancreatic Cystic Lesions.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets.Journal of digital imaging · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a two-step nnU-Net for detection with the performance assessed depending on lesions' volume and location in comparison to three human readers of varying experience. Mean sensitivity was 78.8 ± 0.1%. The sensitivity was highest for large lesions with 87.8% for cysts ≥220 mm
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Registered trials
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.