ArticleAbdominal radiology (New York)2022
CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.Journal of cancer research and clinical oncology · 2022Trial
- Evaluation of cystic neoplasms using EUS, MRI, and CT scan and treatment strategy.Annals of medicine and surgery (2012) · 2026Review
- Deep learning based on ultrasound for differential diagnosis of pancreatic serous cystic neoplasm and mucinous cystic neoplasm.BMC cancer · 2025Article
- 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
- Article
- Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study.Journal of imaging · 2025Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.Journal of applied clinical medical physics · 2024Article
- Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists.Abdominal radiology (New York) · 2022Article
- Review
- Pancreatic Cyst Management: A Multimodal and Interdisciplinary Approach.Journal of radiological scienceArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAt present, numerous challenges exist in the diagnosis of pancreatic SCNs and MCNs. After the emergence of artificial intelligence (AI), many radiomics research methods have been applied to the identification of pancreatic SCNs and MCNs. PURPOSE: A deep neural network (DNN) model termed Multi-channel-Multiclassifier-Random Forest-ResNet (MMRF-ResNet) was constructed to provide an objective CT imaging basis for differential diagnosis between pancreatic serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs). MATERIALS AND
methodsThis study is a retrospective analysis of pancreatic unenhanced and enhanced CT images in 63 patients with pancreatic SCNs and 47 patients with MCNs (3 of which were mucinous cystadenocarcinoma) confirmed by pathology from December 2010 to August 2016. Different image segmented methods (single-channel manual outline ROI image and multi-channel image), feature extraction methods (wavelet, LBP, HOG, GLCM, Gabor, ResNet, and AlexNet) and classifiers (KNN, Softmax, Bayes, random forest classifier, and Majority Voting rule method) are used to classify the nature of the lesion in each CT image (SCNs/MCNs). Then, the comparisons of classification results were made based on sensitivity, specificity, precision, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), with pathological results serving as the gold standard.
resultsMulti-channel-ResNet (AUC 0.98) was superior to Manual-ResNet (AUC 0.91).CT image characteristics of lesions extracted by ResNet are more representative than wavelet, LBP, HOG, GLCM, Gabor, and AlexNet. Compared to the use of three classifiers alone and Majority Voting rule method, the use of the MMRF-ResNet model exhibits a better evaluation effect (AUC 0.96) for the classification of the pancreatic SCNs and MCNs.
conclusionThe CT image classification model MMRF-ResNet is an effective method to distinguish between pancreatic SCNs and MCNs.
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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.