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
Autor: | Adrian Saftoiu, Gabriel Gruionu, Irina M. Cazacu, Carmen Popescu, Lucian Gheorghe Gruionu, Daniela E. Burtea, Bogdan Silviu Ungureanu, Ștefan Udriștoiu, Mădălin Ionuț Costache, Alina Constantin, Andreea Valentina Iacob, Anca Loredana Udriștoiu |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Endoscopic ultrasound
Computer science Pilot Projects Endosonography Diagnostic Radiology Machine Learning 0302 clinical medicine Ultrasound Imaging Medicine and Health Sciences Diagnosis Computer-Assisted Multidisciplinary Artificial neural network medicine.diagnostic_test Radiology and Imaging Fine-needle aspiration Oncology 030220 oncology & carcinogenesis Medicine 030211 gastroenterology & hepatology Radiology Elastography Research Article medicine.medical_specialty Computer and Information Sciences Imaging Techniques Science Surgical and Invasive Medical Procedures Adenocarcinoma Research and Analysis Methods Sensitivity and Specificity Doppler Imaging Diagnosis Differential 03 medical and health sciences Pancreatic Cancer Deep Learning Diagnostic Medicine Artificial Intelligence Pancreatic cancer Gastrointestinal Tumors parasitic diseases medicine Cancer Detection and Diagnosis Humans Endoscopic Ultrasound-Guided Fine Needle Aspiration Pancreas business.industry Deep learning Carcinoma Cancers and Neoplasms Endoscopy medicine.disease Pancreatic Neoplasms Computer-aided diagnosis Artificial intelligence Neural Networks Computer Differential diagnosis business |
Zdroj: | PLoS ONE, Vol 16, Iss 6, p e0251701 (2021) PLoS ONE |
ISSN: | 1932-6203 |
Popis: | 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. |
Databáze: | OpenAIRE |
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