ArticleMachine learning in medical imaging. MLMI (Workshop)2023
Radiomics Boosts Deep Learning Model for IPMN Classification.
Article in Machine learning in medical imaging. MLMI (Workshop), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 13 citations in OpenAlex.
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- Multi-center evaluation of radiomics and deep learning to stratify malignancy risk of IPMNs.Abdominal radiology (New York) · 2026Article
- Radiomic MRI model for predicting the development of worrisome features in branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs).La Radiologia medica · 2026Article
- A Reproducible Multicentre MRI Radiomics Workflow for Pancreatic Cyst Risk Stratification Using Paired T1- and T2-Weighted Imaging.Tomography (Ann Arbor, Mich.) · 2026Article
- Uncertainty-Aware Explainable AI for Pancreatic Cysts: Identifying Deep Learning Vulnerabilities and Ensuring Safe Clinical Triage in IPMN Management.Research square · 2026Article
- Artificial Intelligence in IPMN diagnosis: bridging promise and clinical reality.Annals of medicine and surgery (2012) · 2025Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Pediatric pancreas segmentation from MRI scans with deep learning.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025Article
- Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.PLOS digital health · 2025Article
- Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study.Journal of imaging · 2025Article
- Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations.Cancers · 2024Review
- Genetic factors, risk prediction and AI application of thrombotic diseases.Experimental hematology & oncology · 2024Review
- Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Risk Assessment and Radiomics Analysis in Magnetic Resonance Imaging of Pancreatic Intraductal Papillary Mucinous Neoplasms (IPMN).Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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Authors and funding
17 authors at 11 institutions in 5 countries.
Funding
Abstract
Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.
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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.