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Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis rem...

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Autores principales: Udriștoiu, Anca Loredana, Cazacu, Irina Mihaela, Gruionu, Lucian Gheorghe, Gruionu, Gabriel, Iacob, Andreea Valentina, Burtea, Daniela Elena, Ungureanu, Bogdan Silviu, Costache, Mădălin Ionuț, Constantin, Alina, Popescu, Carmen Florina, Udriștoiu, Ștefan, Săftoiu, Adrian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238220/
https://www.ncbi.nlm.nih.gov/pubmed/34181680
http://dx.doi.org/10.1371/journal.pone.0251701
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author Udriștoiu, Anca Loredana
Cazacu, Irina Mihaela
Gruionu, Lucian Gheorghe
Gruionu, Gabriel
Iacob, Andreea Valentina
Burtea, Daniela Elena
Ungureanu, Bogdan Silviu
Costache, Mădălin Ionuț
Constantin, Alina
Popescu, Carmen Florina
Udriștoiu, Ștefan
Săftoiu, Adrian
author_facet Udriștoiu, Anca Loredana
Cazacu, Irina Mihaela
Gruionu, Lucian Gheorghe
Gruionu, Gabriel
Iacob, Andreea Valentina
Burtea, Daniela Elena
Ungureanu, Bogdan Silviu
Costache, Mădălin Ionuț
Constantin, Alina
Popescu, Carmen Florina
Udriștoiu, Ștefan
Săftoiu, Adrian
author_sort Udriștoiu, Anca Loredana
collection PubMed
description Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time.
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spelling pubmed-82382202021-07-09 Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model Udriștoiu, Anca Loredana Cazacu, Irina Mihaela Gruionu, Lucian Gheorghe Gruionu, Gabriel Iacob, Andreea Valentina Burtea, Daniela Elena Ungureanu, Bogdan Silviu Costache, Mădălin Ionuț Constantin, Alina Popescu, Carmen Florina Udriștoiu, Ștefan Săftoiu, Adrian PLoS One Research Article Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time. Public Library of Science 2021-06-28 /pmc/articles/PMC8238220/ /pubmed/34181680 http://dx.doi.org/10.1371/journal.pone.0251701 Text en © 2021 Udriștoiu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Udriștoiu, Anca Loredana
Cazacu, Irina Mihaela
Gruionu, Lucian Gheorghe
Gruionu, Gabriel
Iacob, Andreea Valentina
Burtea, Daniela Elena
Ungureanu, Bogdan Silviu
Costache, Mădălin Ionuț
Constantin, Alina
Popescu, Carmen Florina
Udriștoiu, Ștefan
Săftoiu, Adrian
Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title_full Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title_fullStr Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title_full_unstemmed Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title_short Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
title_sort real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238220/
https://www.ncbi.nlm.nih.gov/pubmed/34181680
http://dx.doi.org/10.1371/journal.pone.0251701
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