ArticleBMC cancer2022
Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 41 citations in OpenAlex.
- Mucinous vs. non-mucinous pancreatic cysts: diagnostic keys on CT and MRI.Abdominal radiology (New York) · 2026Review
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- Interpretable prediction of occult lymph node metastasis in pancreatic ductal adenocarcinoma using a model fusing habitat radiomics and deep learning.Abdominal radiology (New York) · 2026Article
- Common cystic pancreatic neoplasms: A comprehensive magnetic resonance imaging-based review of typical and atypical imaging features.World journal of radiology · 2026Review
- A Reproducible Multicentre MRI Radiomics Workflow for Pancreatic Cyst Risk Stratification Using Paired T1- and T2-Weighted Imaging.Tomography (Ann Arbor, Mich.) · 2026Article
- Pancreatic Cystic Lesions: From Basic Knowledge to Recent Guidelines.Journal of clinical medicine · 2026Review
- Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.BMC cancer · 2025Article
- Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact.NPJ digital medicine · 2025Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Gastroenterology in the age of artificial intelligence: Bridging technology and clinical practice.World journal of gastroenterology · 2025Review
- Thoracic staging of lung cancers byPhysical and engineering sciences in medicine · 2025Article
- Evolving techniques in the endoscopic evaluation and management of pancreas cystic lesions.Current opinion in gastroenterology · 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
- Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.NPJ precision oncology · 2025Article
- Article
- Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Misdiagnosis of pancreatic intraductal papillary mucinous neoplasms and the challenge of mimicking lesions: imaging diagnosis and differentiation strategies.Abdominal radiology (New York) · 2025Review
- Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study.Journal of imaging · 2025Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas.
methodsClinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression.
resultsFor the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score.
conclusionsThe radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.
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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.