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Agudo Castillo B, Mascarenhas M, Martins M, Mendes F, de la Iglesia D, Costa AMMPD, Esteban Fernández-Zarza C, González-Haba Ruiz M. Advancements in biliopancreatic endoscopy: a comprehensive review of artificial intelligence in EUS and ERCP. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2024. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
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Rousta F, Esteki A, Shalbaf A, Sadeghi A, Moghadam PK, Voshagh A. Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108205. [PMID: 38703435 DOI: 10.1016/j.cmpb.2024.108205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/13/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024]
Abstract
The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.
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Affiliation(s)
- Fatemeh Rousta
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Esteki
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Sadeghi
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pardis Ketabi Moghadam
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ardalan Voshagh
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
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Dahiya DS, Shah YR, Ali H, Chandan S, Gangwani MK, Canakis A, Ramai D, Hayat U, Pinnam BSM, Iqbal A, Malik S, Singh S, Jaber F, Alsakarneh S, Mohamed I, Ali MA, Al-Haddad M, Inamdar S. Basic Principles and Role of Endoscopic Ultrasound in Diagnosis and Differentiation of Pancreatic Cancer from Other Pancreatic Lesions: A Comprehensive Review of Endoscopic Ultrasound for Pancreatic Cancer. J Clin Med 2024; 13:2599. [PMID: 38731128 PMCID: PMC11084399 DOI: 10.3390/jcm13092599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/17/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Pancreatic lesions consist of both neoplastic and non-neoplastic lesions and often pose a diagnostic and therapeutic challenge due to similar clinical and radiological features. In recent years, pancreatic lesions have been discovered more frequently as incidental findings due to the increased utilization and widespread availability of abdominal cross-sectional imaging. Therefore, it becomes imperative to establish an early and appropriate diagnosis with meticulous differentiation in an attempt to balance unnecessary treatment of benign pancreatic lesions and missing the opportunity for early intervention in malignant lesions. Endoscopic ultrasound (EUS) has become an important diagnostic modality for the identification and risk stratification of pancreatic lesions due to its ability to provide detailed imaging and acquisition of tissue samples for analysis with the help of fine-needle aspiration/biopsy. The recent development of EUS-based technology, including contrast-enhanced endoscopic ultrasound, real-time elastography-endoscopic ultrasound, miniature probe ultrasound, confocal laser endomicroscopy, and the application of artificial intelligence has significantly augmented the diagnostic accuracy of EUS as it enables better evaluation of the number, location, dimension, wall thickness, and contents of these lesions. This article provides a comprehensive overview of the role of the different types of EUS available for the diagnosis and differentiation of pancreatic cancer from other pancreatic lesions while discussing their key strengths and important limitations.
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Affiliation(s)
- Dushyant Singh Dahiya
- Division of Gastroenterology, Hepatology and Motility, The University of Kansas School of Medicine, Kansas City, KS 66160, USA
| | - Yash R. Shah
- Department of Internal Medicine, Trinity Health Oakland/Wayne State University, Pontiac, MI 48341, USA
| | - Hassam Ali
- Division of Gastroenterology, Hepatology & Nutrition, East Carolina University/Brody School of Medicine, Greenville, NC 27858, USA
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, Creighton University School of Medicine, Omaha, NE 68178, USA
| | - Manesh Kumar Gangwani
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Andrew Canakis
- Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, The University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Umar Hayat
- Department of Internal Medicine, Geisinger Wyoming Valley Medical Center, Wilkes Barre, PA 18711, USA
| | - Bhanu Siva Mohan Pinnam
- Department of Internal Medicine, John H. Stroger Hospital of Cook County, Chicago, IL 60612, USA
| | - Amna Iqbal
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Sheza Malik
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA
| | - Sahib Singh
- Department of Internal Medicine, Sinai Hospital, Baltimore, MD 21215, USA
| | - Fouad Jaber
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Saqr Alsakarneh
- Department of Internal Medicine, University of Missouri-Kansas City, Kansas City, MO 64110, USA
| | - Islam Mohamed
- Division of Hepatology, University of Missouri School of Medicine, Columbia, MO 64108, USA
| | - Meer Akbar Ali
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Mohammad Al-Haddad
- Division of Gastroenterology and Hepatology, University of Jordan, 11942 Amman, Jordan
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sumant Inamdar
- Department of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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Huang J, Fan X, Liu W. Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases. Diagnostics (Basel) 2023; 13:2815. [PMID: 37685350 PMCID: PMC10487217 DOI: 10.3390/diagnostics13172815] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
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Affiliation(s)
| | | | - Wentian Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; (J.H.); (X.F.)
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Jiang J, Chao WL, Culp S, Krishna SG. Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma. Cancers (Basel) 2023; 15:2410. [PMID: 37173876 PMCID: PMC10177524 DOI: 10.3390/cancers15092410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.
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Affiliation(s)
- Joanna Jiang
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Ohio State University Wexner Medical Ceter, Columbus, OH 43210, USA
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Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms. Clin Imaging 2023; 96:15-22. [PMID: 36736182 DOI: 10.1016/j.clinimag.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/20/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). METHODS This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. RESULTS The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. CONCLUSION The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.
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Schulz D, Heilmaier M, Phillip V, Treiber M, Mayr U, Lahmer T, Mueller J, Demir IE, Friess H, Reichert M, Schmid RM, Abdelhafez M. Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning. Endoscopy 2023; 55:415-422. [PMID: 36323331 DOI: 10.1055/a-1971-1274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
BACKGROUND Risk stratification and recommendation for surgery for intraductal papillary mucinous neoplasm (IPMN) are currently based on consensus guidelines. Risk stratification from presurgery histology is only potentially decisive owing to the low sensitivity of fine-needle aspiration. In this study, we developed and validated a deep learning-based method to distinguish between IPMN with low grade dysplasia and IPMN with high grade dysplasia/invasive carcinoma using endoscopic ultrasound (EUS) images. METHODS For model training, we acquired a total of 3355 EUS images from 43 patients who underwent pancreatectomy from March 2015 to August 2021. All patients had histologically proven IPMN. We used transfer learning to fine-tune a convolutional neural network and to classify "low grade IPMN" from "high grade IPMN/invasive carcinoma." Our test set consisted of 1823 images from 27 patients, recruiting 11 patients retrospectively, 7 patients prospectively, and 9 patients externally. We compared our results with the prediction based on international consensus guidelines. RESULTS Our approach could classify low grade from high grade/invasive carcinoma in the test set with an accuracy of 99.6 % (95 %CI 99.5 %-99.9 %). Our deep learning model achieved superior accuracy in prediction of the histological outcome compared with any individual guideline, which have accuracies between 51.8 % (95 %CI 31.9 %-71.3 %) and 70.4 % (95 %CI 49.8-86.2). CONCLUSION This pilot study demonstrated that deep learning in IPMN-EUS images can predict the histological outcome with high accuracy.
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Affiliation(s)
- Dominik Schulz
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Markus Heilmaier
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Veit Phillip
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Treiber
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ulrich Mayr
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tobias Lahmer
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Julius Mueller
- Klinik für Innere Medizin II, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Ihsan Ekin Demir
- Klinik und Poliklinik für Chirurgie, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Helmut Friess
- Klinik und Poliklinik für Chirurgie, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maximilian Reichert
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Roland M Schmid
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Mohamed Abdelhafez
- Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Li J, Yin W, Wang Y. CT classification model of pancreatic serous cystic neoplasm and mucinous cystic neoplasm based on deep transfer learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:167-180. [PMID: 36404568 DOI: 10.3233/xst-221281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.
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Affiliation(s)
- Jin Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wei Yin
- Department of Radiology, Changhai Hospital, The Naval Military Medical University, Shanghai, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Vilas-Boas F, Ribeiro T, Afonso J, Cardoso H, Lopes S, Moutinho-Ribeiro P, Ferreira J, Mascarenhas-Saraiva M, Macedo G. Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study. Diagnostics (Basel) 2022; 12:diagnostics12092041. [PMID: 36140443 PMCID: PMC9498252 DOI: 10.3390/diagnostics12092041] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 12/12/2022] Open
Abstract
Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.
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Affiliation(s)
- Filipe Vilas-Boas
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Susana Lopes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Moutinho-Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Miguel Mascarenhas-Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Correspondence:
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel) 2022; 7:biomimetics7020079. [PMID: 35735595 PMCID: PMC9221027 DOI: 10.3390/biomimetics7020079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 12/10/2022] Open
Abstract
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34–68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25–64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.
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Affiliation(s)
- Shiva Rangwani
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA; (S.R.); (D.R.A.)
| | - Devarshi R. Ardeshna
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA; (S.R.); (D.R.A.)
| | - Brandon Rodgers
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Jared Melnychuk
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Ronald Turner
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA;
| | - Somashekar G. Krishna
- Department of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
- Correspondence: ; Tel.: +614-293-6255
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Simsek C, Lee LS. Machine learning in endoscopic ultrasonography and the pancreas: The new frontier? Artif Intell Gastroenterol 2022; 3:54-65. [DOI: 10.35712/aig.v3.i2.54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.
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Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, United States
| | - Linda S Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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Oh S, Kim YJ, Park YT, Kim KG. Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010245. [PMID: 35009788 PMCID: PMC8749636 DOI: 10.3390/s22010245] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/22/2021] [Accepted: 12/27/2021] [Indexed: 05/05/2023]
Abstract
The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images.
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Affiliation(s)
- Seok Oh
- Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea; (S.O.); (Y.-J.K.)
| | - Young-Jae Kim
- Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea; (S.O.); (Y.-J.K.)
| | - Young-Taek Park
- HIRA Research Institute, Health Insurance Review & Assessment Service (HIRA), Wonju-si 26465, Korea;
| | - Kwang-Gi Kim
- Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea; (S.O.); (Y.-J.K.)
- Correspondence:
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