|
1
|
Lu XY, Cao JY, Chen S, Wang Y, Wei L, Gong W, Lou WH, Dong Y. Added Value of Dynamic Contrast-Enhanced Ultrasound Analysis for Differential Diagnosis of Small (≤20 mm) Solid Pancreatic Lesions. ULTRASOUND IN MEDICINE & BIOLOGY 2025; 51:535-542. [PMID: 39753466 DOI: 10.1016/j.ultrasmedbio.2024.11.015] [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: 04/10/2024] [Revised: 11/09/2024] [Accepted: 11/14/2024] [Indexed: 01/25/2025]
Abstract
OBJECTIVE To evaluate the added value of dynamic contrast-enhanced ultrasound (DCE-US) analysis in pre-operative differential diagnosis of small (≤20 mm) solid pancreatic lesions (SPLs). METHODS In this retrospective study, patients with biopsy or surgerical resection and histopathologically confirmed small (≤20 mm) SPLs were included. One wk before biopsy/surgery, pre-operative B-mode ultrasound and contrast-enhanced ultrasound were performed. An ultrasonic system (ACUSON Sequoia, Siemens Medical Solutions, PA, USA) equipped with a 5C1 MHz convex array transducer was utilized. A dose of 1.5 ml SonoVue (Bracco, Italy) was injected as the contrast agent. Time-intensity curves were generated using VueBox software (Bracco) and various DCE-US quantitative parameters were subsequently calculated after curve fitting. Univariate and multivariate logistic regression analysis were utilized. RESULTS From August 2020 to November 2023, a total of 76 patients (31 males and 45 females; mean age: 61.9 ± 10.5 y) with 76 small (≤20 mm) SPLs were included. Mean size of the lesions was 16.4 ± 0.4 mm (range: 7-20 mm). Final diagnosis included 37 benign and 39 malignant small SPLs. On B-mode ultrasound, the majority of malignant (37/39, 94.9%) and benign SPLs (30/37, 81.1%) were hypo-echoic lesions with ill-defined borders and irregular shapes (p > 0.05). During the arterial phase of contrast-enhanced ultrasound, most SPLs (59/76, 77.6%) exhibited iso-enhancement when compared with surrounding pancreatic parenchyma. Subsequently, 82.1% (32/39) of malignant SPLs and 35.1% (13/37) of benign SPLs demonstrated wash-out in the venous phase and showed hypo-enhancement in venous and late phases (p > 0.05). Compared with benign SPLs, the time-intensity curves of small malignant SPLs revealed earlier and lower enhancement in the arterial phase, and a faster decline during the venous phase with a decreased area under the curve. Among the quantitative parameters, a lower peak enhancement ratio and higher fall time ratio were more common in small malignant SPLs (p < 0.05). For DCE-US analysis, the combined areas under the curve of significant quantitative parameters was 0.919, with 87.2% sensitivity and 86.5% specificity when differentiating between small malignant and benign SPLs. This result was better than contrast-enhanced computed tomography, which has a sensitivity of 74.4% and a specificity of 75.7%. CONCLUSION DCE-US analysis provides added value for the pre-operative differential diagnosis of small malignant SPLs.
Collapse
Affiliation(s)
- Xiu-Yun Lu
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jia-Ying Cao
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Sheng Chen
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Li Wei
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei Gong
- Department of General Surgery, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wen-Hui Lou
- Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.
| |
Collapse
|
|
2
|
Iglesias-Garcia J, de la Iglesia D, Fusaroli P. Endoscopic Ultrasound armamentarium for precise and early diagnosis of biliopancreatic lesions. Best Pract Res Clin Gastroenterol 2025:101987. [DOI: 10.1016/j.bpg.2025.101987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/04/2025]
|
|
3
|
Jain A, Pabba M, Jain A, Singh S, Ali H, Vinayek R, Aswath G, Sharma N, Inamdar S, Facciorusso A. Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy. Cancers (Basel) 2025; 17:379. [PMID: 39941748 PMCID: PMC11815774 DOI: 10.3390/cancers17030379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 01/20/2025] [Accepted: 01/23/2025] [Indexed: 02/16/2025] Open
Abstract
Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.
Collapse
Affiliation(s)
- Aryan Jain
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Mayur Pabba
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Aditya Jain
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Sahib Singh
- Department of Internal Medicine, Sinai Hospital of Baltimore, Baltimore, MD 21215, USA
| | - Hassam Ali
- Department of Gastroenterology, ECU Health Medical Center/Brody School of Medicine, Greenville, NC 27834, USA;
| | - Rakesh Vinayek
- Department of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD 21215, USA;
| | - Ganesh Aswath
- Department of Gastroenterology, State University of New York Upstate Medical University, Syracuse, NY 13210, USA;
| | - Neil Sharma
- Department of Gastroenterology, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Sumant Inamdar
- Department of Gastroenterology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Experimental Medicine, University of Salento, 73100 Lecce, Italy;
- Clinical Effectiveness Research Group, Faculty of Medicine, Institute of Health and Society, University of Oslo, 0373 Oslo, Norway
| |
Collapse
|
|
4
|
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; 116:613-622. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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.
Collapse
Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
| | | | | | | | | |
Collapse
|
|
5
|
Jiang H, Ye LS, Yuan XL, Luo Q, Zhou NY, Hu B. Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions. J Dig Dis 2024; 25:564-572. [PMID: 39740251 DOI: 10.1111/1751-2980.13324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 11/13/2024] [Accepted: 12/03/2024] [Indexed: 01/02/2025]
Abstract
Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.
Collapse
Affiliation(s)
- Huan Jiang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lian Song Ye
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xiang Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Qi Luo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Nuo Ya Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Med-X Center for Materials, Sichuan University, Chengdu, Sichuan Province, China
| |
Collapse
|
|
6
|
Bou Jaoude J, Al Bacha R, Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol 2024; 5:91336. [DOI: 10.35712/aig.v5.i2.91336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/25/2024] [Accepted: 06/07/2024] [Indexed: 08/08/2024] Open
Abstract
Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.
Collapse
Affiliation(s)
- Joseph Bou Jaoude
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Rose Al Bacha
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Bassam Abboud
- Department of General Surgery, Geitaoui Hospital, Faculty of Medicine, Lebanese University, Lebanon, Beirut 166830, Lebanon
| |
Collapse
|
|
7
|
Metelli F, Manfredi G, Pagano N, Buscarini E, Crinò SF, Armellini E. The Role of Endoscopic Ultrasound and Ancillary Techniques in the Diagnosis of Autoimmune Pancreatitis: A Comprehensive Review. Diagnostics (Basel) 2024; 14:1233. [PMID: 38928649 PMCID: PMC11202526 DOI: 10.3390/diagnostics14121233] [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: 04/09/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Autoimmune pancreatitis (AIP) is a unique form of chronic pancreatitis with a multifactorial pathogenesis. Historically, it has been classified as type 1 and type 2, according to its clinical and histological features. The diagnosis of AIP is challenging and relies on a combination of clinical, histopathologic, serologic, and imaging characteristics. In the available guidelines, the imaging hallmarks of AIP are based on cross-sectional imaging and cholangiopancreatography retrograde endoscopic findings. Endoscopic ultrasound (EUS) is generally used for pancreatic tissue acquisition to rule out pancreatic cancer and diagnose AIP with limited accuracy. Several papers reported the reliability of EUS for providing informative morphologic features of AIP. Nowadays, the improvement in the resolution of EUS conventional images and the development of new ancillary technologies have further increased the diagnostic yield of EUS: contrast-enhanced EUS and EUS elastography are non-invasive and real-time techniques that strongly support the diagnosis and management of pancreatic diseases. In this review article, we will present the role of conventional EUS and ancillary diagnostic techniques in the diagnosis of AIP to support clinicians and endosonographers in managing this condition.
Collapse
Affiliation(s)
- Flavio Metelli
- Gastroenterology and Endoscopy Department, ASST Maggiore Hospital Crema, 26013 Crema, Italy; (F.M.); (G.M.); (E.B.)
| | - Guido Manfredi
- Gastroenterology and Endoscopy Department, ASST Maggiore Hospital Crema, 26013 Crema, Italy; (F.M.); (G.M.); (E.B.)
| | - Nico Pagano
- Gastroenterology Unit, Department of Oncological and Specialty Medicine, University Hospital Maggiore della Carità, 28100 Novara, Italy;
| | - Elisabetta Buscarini
- Gastroenterology and Endoscopy Department, ASST Maggiore Hospital Crema, 26013 Crema, Italy; (F.M.); (G.M.); (E.B.)
| | - Stefano Francesco Crinò
- Diagnostic and Interventional Endoscopy of Pancreas, Pancreas Institute, University of Verona, 37134 Verona, Italy;
| | - Elia Armellini
- Gastroenterology and Endoscopy Unit, ASST-Bergamoest, 24068 Seriate, Italy
| |
Collapse
|
|
8
|
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.
Collapse
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
| |
Collapse
|
|
9
|
Gheorghiu MI, Seicean A, Pojoga C, Hagiu C, Seicean R, Sparchez Z. Contrast-enhanced guided endoscopic ultrasound procedures. World J Gastroenterol 2024; 30:2311-2320. [PMID: 38813054 PMCID: PMC11130571 DOI: 10.3748/wjg.v30.i17.2311] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/10/2024] [Accepted: 04/15/2024] [Indexed: 04/30/2024] Open
Abstract
Contrast-enhanced endoscopic ultrasound (CH-EUS) can overcome the limitations of endoscopic ultrasound-guided acquisition by identifying microvessels inside inhomogeneous tumours and improving the characterization of these tumours. Despite the initial enthusiasm that oriented needle sampling under CH-EUS guidance could provide better diagnostic yield in pancreatic solid lesions, further studies did not confirm the supplementary values in cases of tissue acquisition guided by CH-EUS. This review details the knowledge based on the available data on contrast-guided procedures. The indications for CH-EUS tissue acquisition include isoechoic EUS lesions with poor visible delineation where CH-EUS can differentiate the lesion vascularisation from the surrounding parenchyma and also the mural nodules within biliopancreatic cystic lesions, which occur in select cases. Additionally, the roles of CH-EUS-guided therapy in patients whose pancreatic fluid collections or bile ducts that have an echogenic content have indications for drainage, and patients who have nonvisualized vessels that need to be highlighted via Doppler EUS are presented. Another indication is represented if there is a need for an immediate assessment of the post-radiofrequency ablation of pancreatic neuroendocrine tumours, in which case CH-EUS can be used to reveal the incomplete tumour destruction.
Collapse
Affiliation(s)
- Marcel Ioan Gheorghiu
- Department of Gastroenterology, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca 400162, Cluj, Romania
| | - Andrada Seicean
- Department of Gastroenterology, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca 400162, Cluj, Romania
| | - Cristina Pojoga
- Department of Gastroenterology, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca 400162, Cluj, Romania
- Department of Clinical Psychology and Psychotherapy, International Institute for Advanced Study of Psychotherapy and Applied Mental Health, Babeș-Bolyai University, Cluj-Napoca 400375, Cluj, Romania
| | - Claudia Hagiu
- Department of Gastroenterology, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca 400162, Cluj, Romania
| | - Radu Seicean
- The First Surgical Clinic, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj Napoca 400006, Cluj, Romania
| | - Zeno Sparchez
- Department of Gastroenterology, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca 400162, Cluj, Romania
| |
Collapse
|
|
10
|
Battistella A, Tacelli M, Mapelli P, Schiavo Lena M, Andreasi V, Genova L, Muffatti F, De Cobelli F, Partelli S, Falconi M. Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms. Expert Rev Gastroenterol Hepatol 2024; 18:155-169. [PMID: 38647016 DOI: 10.1080/17474124.2024.2342837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures. AREAS COVERED This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023. EXPERT OPINION A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.
Collapse
Affiliation(s)
- Anna Battistella
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Matteo Tacelli
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Valentina Andreasi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Luana Genova
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Muffatti
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Partelli
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Falconi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| |
Collapse
|
|
11
|
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: 6] [Impact Index Per Article: 6.0] [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.
Collapse
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
| | | |
Collapse
|
|
12
|
Yin M, Lin J, Wang Y, Liu Y, Zhang R, Duan W, Zhou Z, Zhu S, Gao J, Liu L, Liu X, Gu C, Huang Z, Xu X, Xu C, Zhu J. Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning. Int J Med Inform 2024; 184:105341. [PMID: 38290243 DOI: 10.1016/j.ijmedinf.2024.105341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 12/16/2023] [Accepted: 01/14/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVE Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model β: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION The proposed multimodal model outperformed any single-modality models and traditional scoring systems.
Collapse
Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jiaxi Lin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Yu Wang
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Department of General Surgery, Jintan Hospital Affiliated to Jiangsu University, Changzhou, Jiangsu 213299, China
| | - Yuanjun Liu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China
| | - Wenbin Duan
- Department of Hepatobiliary Surgery, the People's Hospital of Hunan Province, Changsha, Hunan 410002, China
| | - Zhirun Zhou
- Department of Obstetrics and Gynaecology, the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China
| | - Shiqi Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jingwen Gao
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Lu Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Xiaolin Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Chenqi Gu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhou Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China.
| | - Chunfang Xu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China; Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150000, China.
| |
Collapse
|
|
13
|
Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
Collapse
Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| |
Collapse
|
|
14
|
Robles-Medranda C, Baquerizo-Burgos J, Puga-Tejada M, Del Valle R, Mendez JC, Egas-Izquierdo M, Arevalo-Mora M, Cunto D, Alcívar-Vasquez J, Pitanga-Lukashok H, Tabacelia D. Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos). Gastrointest Endosc 2024; 99:271-279.e2. [PMID: 37827432 DOI: 10.1016/j.gie.2023.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND AND AIMS EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object detection. We developed 2 EUS-based CNN models for normal anatomic structure recognition during real-time linear- and radial-array EUS evaluations. METHODS The study was performed from February 2020 to June 2022. Consecutive patient videos of linear- and radial-array EUS videos were recorded. Expert endosonographers identified and labeled 20 normal anatomic structures within the videos for training and validation of the CNN models. Initial CNN models (CNNv1) were developed from 45 videos and the improved models (CNNv2) from an additional 102 videos. CNN model performance was compared with that of 2 expert endosonographers. RESULTS CNNv1 used 45,034 linear-array EUS frames and 21,063 radial-array EUS frames. CNNv2 used 148,980 linear-array EUS frames and 128,871 radial-array EUS frames. Linear-array CNNv1 and radial-array CNNv1 achieved a 75.65% and 71.36% mean average precision (mAP) with a total loss of .19 and .18, respectively. Linear-array CNNv2 obtained an 88.7% mAP with a .06 total loss, whereas radial-array CNNv2 achieved an 83.5% mAP with a .07 total loss. CNNv2 accurately detected all studied normal anatomic structures with a >98% observed agreement during clinical validation. CONCLUSIONS The proposed CNN models accurately recognize the normal anatomic structures in prerecorded videos and real-time EUS. Prospective trials are needed to evaluate the impact of these models on the learning curves of EUS trainees.
Collapse
Affiliation(s)
- Carlos Robles-Medranda
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Jorge Baquerizo-Burgos
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Miguel Puga-Tejada
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Raquel Del Valle
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Juan C Mendez
- Research and Development Department, mdconsgroup, Guayaquil, Ecuador
| | - Maria Egas-Izquierdo
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Martha Arevalo-Mora
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Domenica Cunto
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Juan Alcívar-Vasquez
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Hannah Pitanga-Lukashok
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Daniela Tabacelia
- Gastroenterology and Hepatology, Elias Emergency University Hospital, Bucharest, Romania; Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| |
Collapse
|
|
15
|
Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
|
|
16
|
Li J, Zhang P, Wang T, Zhu L, Liu R, Yang X, Wang K, Shen D, Sheng B. DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:64-75. [PMID: 37368810 DOI: 10.1109/tmi.2023.3289859] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varying resolutions, effective regions, and interference signals, making the distribution of the data highly variable and negatively impacting the performance of deep learning models. Additionally, manual labeling of images is time-consuming and requires significant effort, leading to the desire to effectively utilize a large amount of unlabeled data for network training. To address these challenges, this study proposes the Dual Self-supervised Multi-Operator Transformation Network (DSMT-Net) for multi-source EUS diagnosis. The DSMT-Net includes a multi-operator transformation approach to standardize the extraction of regions of interest in EUS images and eliminate irrelevant pixels. Furthermore, a transformer-based dual self-supervised network is designed to integrate unlabeled EUS images for pre-training the representation model, which can be transferred to supervised tasks such as classification, detection, and segmentation. A large-scale EUS-based pancreas image dataset (LEPset) has been collected, including 3,500 pathologically proven labeled EUS images (from pancreatic and non-pancreatic cancers) and 8,000 unlabeled EUS images for model development. The self-supervised method has also been applied to breast cancer diagnosis and was compared to state-of-the-art deep learning models on both datasets. The results demonstrate that the DSMT-Net significantly improves the accuracy of pancreatic and breast cancer diagnosis.
Collapse
|
|
17
|
Chatterjee A, Shah J. Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma. Diagnostics (Basel) 2023; 14:78. [PMID: 38201387 PMCID: PMC10802852 DOI: 10.3390/diagnostics14010078] [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: 12/04/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common (90%) type of solid pancreatic neoplasm. Due to its late presentation and poor survival rate, early diagnosis and timely treatment is of utmost importance for better clinical outcomes. Endoscopic ultrasound provides high-resolution images of the pancreas and has excellent sensitivity in the diagnosis of even small (<2 cm) pancreatic lesions. Apart from imaging, it also has an advantage of tissue acquisition (EUS fine-needle aspiration, FNA; or fine-needle biopsy, FNB) for definitive diagnoses. EUS-guided tissue acquisition plays a crucial role in genomic and molecular studies, which in today's era of personalized medicine, are likely to become important components of PDAC management. With the use of better needle designs and technical advancements, EUS has now become an indispensable tool in the management of PDAC. Lastly, artificial intelligence for the detection of pancreatic lesions and newer automated needles for tissue acquisition will obviate observer dependency in the near future, resulting in the wider dissemination and adoption of this technology for improved outcomes in patients with PDAC.
Collapse
Affiliation(s)
| | - Jimil Shah
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India;
| |
Collapse
|
|
18
|
Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
Collapse
Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| |
Collapse
|
|
19
|
Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg 2023; 109:4298-4308. [PMID: 37800594 PMCID: PMC10720860 DOI: 10.1097/js9.0000000000000717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. METHODS Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. RESULTS A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). CONCLUSION AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
Collapse
Affiliation(s)
- Arkadeep Dhali
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vincent Kipkorir
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | | | | | - Ibsen Ongidi
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Talha Chaudhry
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Gisore Morara
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Khulud Nurani
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Doreen Cheruto
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | - Leonard R. Chieng
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Gopal Krishna Dhali
- School of Digestive and Liver Diseases, Institute of Postgraduate Medical Education and Research, Kolkata, India
| |
Collapse
|
|
20
|
Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel) 2023; 13:3054. [PMID: 37835797 PMCID: PMC10572518 DOI: 10.3390/diagnostics13193054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/06/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.
Collapse
Affiliation(s)
| | | | | | | | | | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China; (X.Q.); (T.R.); (Y.C.); (Y.Z.); (D.W.)
| |
Collapse
|
|
21
|
Koltai T. Earlier Diagnosis of Pancreatic Cancer: Is It Possible? Cancers (Basel) 2023; 15:4430. [PMID: 37760400 PMCID: PMC10526520 DOI: 10.3390/cancers15184430] [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: 06/07/2023] [Revised: 07/31/2023] [Accepted: 08/06/2023] [Indexed: 09/29/2023] Open
Abstract
Pancreatic ductal adenocarcinoma has a very high mortality rate which has been only minimally improved in the last 30 years. This high mortality is closely related to late diagnosis, which is usually made when the tumor is large and has extensively infiltrated neighboring tissues or distant metastases are already present. This is a paradoxical situation for a tumor that requires nearly 15 years to develop since the first founding mutation. Response to chemotherapy under such late circumstances is poor, resistance is frequent, and prolongation of survival is almost negligible. Early surgery has been, and still is, the only approach with a slightly better outcome. Unfortunately, the relapse percentage after surgery is still very high. In fact, early surgery clearly requires early diagnosis. Despite all the advances in diagnostic methods, the available tools for improving these results are scarce. Serum tumor markers permit a late diagnosis, but their contribution to an improved therapeutic result is very limited. On the other hand, effective screening methods for high-risk populations have not been fully developed as yet. This paper discusses the difficulties of early diagnosis, evaluates whether the available diagnostic tools are adequate, and proposes some simple and not-so-simple measures to improve it.
Collapse
Affiliation(s)
- Tomas Koltai
- Hospital del Centro Gallego de Buenos Aires, Buenos Aires C1094, Argentina
| |
Collapse
|
|
22
|
Ramaekers M, Viviers CGA, Janssen BV, Hellström TAE, Ewals L, van der Wulp K, Nederend J, Jacobs I, Pluyter JR, Mavroeidis D, van der Sommen F, Besselink MG, Luyer MDP. Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions. J Clin Med 2023; 12:4209. [PMID: 37445243 DOI: 10.3390/jcm12134209] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open
Abstract
Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.
Collapse
Affiliation(s)
- Mark Ramaekers
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Christiaan G A Viviers
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Boris V Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Terese A E Hellström
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Lotte Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Philips Design, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Misha D P Luyer
- Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| |
Collapse
|
|
23
|
Khalaf K, Terrin M, Jovani M, Rizkala T, Spadaccini M, Pawlak KM, Colombo M, Andreozzi M, Fugazza A, Facciorusso A, Grizzi F, Hassan C, Repici A, Carrara S. A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound. J Clin Med 2023; 12:jcm12113757. [PMID: 37297953 DOI: 10.3390/jcm12113757] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/28/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. METHODS AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. RESULTS AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology. CONCLUSIONS The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.
Collapse
Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Maria Terrin
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Manol Jovani
- Division of Gastroenterology, Maimonides Medical Center, SUNY Downstate University, Brooklyn, NY 11219, USA
| | - Tommy Rizkala
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy
| | - Marco Spadaccini
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Katarzyna M Pawlak
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Matteo Colombo
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Marta Andreozzi
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Alessandro Fugazza
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| | - Cesare Hassan
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy
| | - Alessandro Repici
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy
| | - Silvia Carrara
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy
| |
Collapse
|
|
24
|
Ren Y, Zou D, Xu W, Zhao X, Lu W, He X. Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
|
25
|
Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Kuraishi Y, Fumihara D, Yanaidani T, Ishikawa S, Yasuda T, Yamada M, Onishi S, Yamada K, Tanaka T, Tajika M, Niwa Y, Yamaguchi R, Shimizu Y. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy 2023; 55:140-149. [PMID: 35688454 DOI: 10.1055/a-1873-7920] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]). METHODS : Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated. RESULTS : 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84-0.97), 0.94 (0.88-0.98), 0.82 (0.68-0.92), and 0.91 (0.85-0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90-0.99), PASC 1.00 (0.05-1.00), ACC 1.00 (0.22-1.00), MPT 0.33 (0.01-0.91), NEC 1.00 (0.22-1.00), NET 0.93 (0.66-1.00), SPN 1.00 (0.22-1.00), chronic pancreatitis 0.78 (0.52-0.94), and AIP 0.73 (0.39-0.94). CONCLUSIONS : Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.
Collapse
Affiliation(s)
- Takamichi Kuwahara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Kazuo Hara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Nobumasa Mizuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Shin Haba
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Nozomi Okuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yasuhiro Kuraishi
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Daiki Fumihara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Takafumi Yanaidani
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Sho Ishikawa
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Tsukasa Yasuda
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Masanori Yamada
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Sachiyo Onishi
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Keisaku Yamada
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Tsutomu Tanaka
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Masahiro Tajika
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yasumasa Niwa
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan
- Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yasuhiro Shimizu
- Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| |
Collapse
|
|
26
|
Tang A, Tian L, Gao K, Liu R, Hu S, Liu J, Xu J, Fu T, Zhang Z, Wang W, Zeng L, Qu W, Dai Y, Hou R, Tang S, Wang X. Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis. Cancer Med 2023; 12:7962-7973. [PMID: 36606571 PMCID: PMC10134340 DOI: 10.1002/cam4.5578] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 10/10/2022] [Accepted: 12/17/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND AIMS Distinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning-based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS-guided fine-needle aspiration (EUS-FNA) in real time. METHODS This is a prospective study. The CH-EUS MASTER system is composed of Model 1 (real-time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH-EUS examinations followed by EUS-FNA were recruited. All patients underwent CH-EUS and were diagnosed both by endoscopists and CH-EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS-FNA with or without CH-EUS MASTER guidance. RESULTS Compared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH-EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH-EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH-EUS MASTER guided EUS-FNA, and were not significantly different compared to the control group. CH-EUS MASTER-guided EUS-FNA significantly improved the first-pass diagnostic yield. CONCLUSION CH-EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time. TRIAL REGISTRATION NUMBER NCT04607720.
Collapse
Affiliation(s)
- Anliu Tang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Li Tian
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Kui Gao
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Rui Liu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Shan Hu
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Jinzhu Liu
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Jiahao Xu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Tian Fu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Zinan Zhang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Wujun Wang
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Long Zeng
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Weiming Qu
- Department of Gastroenterology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Yong Dai
- Department of Gastroenterology, The First Affiliated Hospital of University of South China, Hengyang, China
| | - Ruirui Hou
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Ningxia, China
| | - Shoujiang Tang
- Division of Digestive Diseases, Department of Medicine, University of Mississippi Medical Center, Jackson, United States
| | - Xiaoyan Wang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| |
Collapse
|
|
27
|
Yin H, Yang X, Sun L, Pan P, Peng L, Li K, Zhang D, Cui F, Xia C, Huang H, Li Z. The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review. Endosc Ultrasound 2023; 12:50-58. [PMID: 35313419 PMCID: PMC10134944 DOI: 10.4103/eus-d-21-00131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93-0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56-236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.
Collapse
Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan; Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai; Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan; Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai; Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Liqi Sun
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Peng Pan
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Lisi Peng
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Keliang Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Deyu Zhang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Fang Cui
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Chuanchao Xia
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| |
Collapse
|
|
28
|
Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med 2022; 11:jcm11247476. [PMID: 36556092 PMCID: PMC9786876 DOI: 10.3390/jcm11247476] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.
Collapse
Affiliation(s)
- Dushyant Singh Dahiya
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48601, USA
- Correspondence: ; Tel.: +1-(678)-602-1176
| | - Mohammad Al-Haddad
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE 68131, USA
| | - Manesh Kumar Gangwani
- Department of Internal Medicine, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Muhammad Aziz
- Department of Gastroenterology, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Babu P. Mohan
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Andrew Canakis
- Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA
| | - Neil Sharma
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Parkview Cancer Institute, Fort Wayne, IN 46845, USA
- Interventional Oncology & Surgical Endoscopy Programs (IOSE), Parkview Health, Fort Wayne, IN 46845, USA
| |
Collapse
|
|
29
|
Endoscopic ultrasonography: Enhancing diagnostic accuracy. Best Pract Res Clin Gastroenterol 2022; 60-61:101808. [PMID: 36577529 DOI: 10.1016/j.bpg.2022.101808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/10/2022] [Indexed: 11/21/2022]
Abstract
Endoscopic ultrasound (EUS) is an essential technique for the management of several diseases. Over the years, new technologies have been developed because to improve and overcome certain limitations related to EUS guided tissue acquisition. Among these new methods, EUS guided elastography and contrast enhanced EUS has arisen as the most widely recognized and available. We will review in this manuscript the different techniques of elastography and contrast enhancement. Nowadays, there are well establish indications for advance imaging, mainly for supporting the management of pancreatic diseases (diagnosis of chronic pancreatitis and differential diagnosis of solid and cystic pancreatic tumors) and characterization of lymph nodes. However, there are more data on new potential indications for the near future.
Collapse
|
|
30
|
Early Assessment of Chemoradiotherapy Response for Locally Advanced Pancreatic Ductal Adenocarcinoma by Dynamic Contrast-Enhanced Ultrasound. Diagnostics (Basel) 2022; 12:diagnostics12112662. [PMID: 36359506 PMCID: PMC9689529 DOI: 10.3390/diagnostics12112662] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Objective: To evaluate the value of dynamic contrast-enhanced ultrasound (DCE-US) and quantitative parameters in early prediction of tumor response to chemoradiotherapy (CRT) in patients with locally advanced pancreatic ductal adenocarcinoma (LAPC). Patients and Methods: In this prospective study, patients with biopsy-proved and histopathologically proved LAPC who underwent regular CRT were recruited. DCE-US evaluations were performed before and four months after CRT. SonoVue-enhanced contrast-enhanced ultrasound (CEUS) was performed by an ultrasound system (ACUSON Sequoia; Siemens Medical Solutions, USA) equipped with a 5C1 MHz convex array transducer. Time−intensity curves were created by VueBox software (Bracco, Italy), and various DCE-US quantitative parameters were obtained. Taking Response Evaluation Criteria in Solid Tumors (RECIST) based on computed tomography (CT) or magnetic resonance imaging (MRI) as the gold standard, DCE-US parameters were compared between the treatment responder group (RG) and non-responder group (NRG). The correlation between the DCE-US parameters and the serum carbohydrate antigen 19-9 (CA 19-9) level was also analyzed. Results: Finally, 21 LAPC patients (mean age 59.3 ± 7.2 years) were included. In comparing the RG (n = 18) and NRG (n = 3), no significant change could be found among the mean size of the lesions (31.2 ± 8.1 mm vs. 27.2 ± 8.3 mm, p = 0.135). In comparing the TICs between the two groups, the LAPC lesions in the RG took a longer time to reach peak enhancement and to wash out. Among all the DCE-US parameters, RT (rise time), WiAUC (wash-in area under the curve), WoAUC (wash-out area under the curve) and WiWoAUC (wash-in and wash-out area under the curve) decreased significantly after CRT in the RG (p < 0.05). The RT ratio, WiAUC ratio, WoAUC ratio and WiWoAUC ratio were closely correlated with the change in serum CA 19-9 level in the RG (p < 0.05). Conclusion: DCE-US might be a potential imaging method for non-invasive follow-up for early response in LAPC patients treated by CRT.
Collapse
|
|
31
|
Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers (Basel) 2022; 14:5382. [PMID: 36358800 PMCID: PMC9657087 DOI: 10.3390/cancers14215382] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 08/01/2023] Open
Abstract
Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.
Collapse
Affiliation(s)
- Bahrudeen Shahul Hameed
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
| | - Uma Maheswari Krishnan
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Arts, Sciences, Humanities & Education (SASHE), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
| |
Collapse
|
|
32
|
Liu JQ, Ren JY, Xu XL, Xiong LY, Peng YX, Pan XF, Dietrich CF, Cui XW. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2022; 28:5530-5546. [PMID: 36304086 PMCID: PMC9594013 DOI: 10.3748/wjg.v28.i38.5530] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/12/2022] [Accepted: 09/22/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.
Collapse
Affiliation(s)
- Ji-Qiao Liu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xiao-Lan Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Yan Xiong
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian 116000, Liaoning Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3003, Switzerland
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| |
Collapse
|
|
33
|
Monino L, Barthet M. Futures perspectives and therapeutic applications. Best Pract Res Clin Gastroenterol 2022; 60-61:101816. [PMID: 36577535 DOI: 10.1016/j.bpg.2022.101816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Laurent Monino
- Department of Hepatogastroenterology, Assistance Publique des Hôpitaux de Marseille, Aix-Marseille Université, Hôpital Nord, Marseille, France; Department of Hepatogastroenterology, Université catholique de Louvain, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Marc Barthet
- Department of Hepatogastroenterology, Assistance Publique des Hôpitaux de Marseille, Aix-Marseille Université, Hôpital Nord, Marseille, France.
| |
Collapse
|
|
34
|
Spadaccini M, Koleth G, Emmanuel J, Khalaf K, Facciorusso A, Grizzi F, Hassan C, Colombo M, Mangiavillano B, Fugazza A, Anderloni A, Carrara S, Repici A. Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence. World J Gastroenterol 2022; 28:3814-3824. [PMID: 36157539 PMCID: PMC9367228 DOI: 10.3748/wjg.v28.i29.3814] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/07/2022] [Accepted: 07/06/2022] [Indexed: 02/06/2023] Open
Abstract
Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset. Often metastatic or locally invasive when symptomatic, most patients are deemed inoperable. In those who are symptomatic, multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma. In asymptomatic patients, detected pancreatic lesions can be either solid or cystic. The clinical implications of identifying small asymptomatic solid pancreatic lesions (SPLs) of < 2 cm are tantamount to a better outcome. The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early, driving the development of existing imaging tools while promoting more comprehensive screening programs. An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound (EUS). It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy (FNA/FNB). Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography, both having improved the specificity of FNA and FNB. This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging, artificial intelligence.
Collapse
Affiliation(s)
- Marco Spadaccini
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Glenn Koleth
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - James Emmanuel
- Department of Gastroenterology and Hepatology, Queen Elizabeth, Kota Kinabalu 88200, Sabah, Malaysia
| | - Kareem Khalaf
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical Sciences, University of Foggia, Foggia 71122, Italy
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Rozzano 20089, Italy
| | - Cesare Hassan
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Matteo Colombo
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Benedetto Mangiavillano
- Digestive Endoscopy Unit, Division of Gasteroenterology, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Alessandro Fugazza
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Andrea Anderloni
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Silvia Carrara
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| |
Collapse
|
|
35
|
Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022; 12:973999. [PMID: 35982967 PMCID: PMC9380440 DOI: 10.3389/fonc.2022.973999] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Purpose We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. Methods Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. Results A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field. Conclusion The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.
Collapse
Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Feixiong Zhang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiangkun Meng
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Yu Miao
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | | | - Li Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
| | - Zhaoshen Li
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
- Clinical Medical College, Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
| |
Collapse
|
|
36
|
Ruano J, Jaramillo M, Gómez M, Romero E. Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1602-1614. [PMID: 35613973 DOI: 10.1016/j.ultrasmedbio.2022.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/06/2022] [Accepted: 04/10/2022] [Indexed: 06/15/2023]
Abstract
Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.
Collapse
Affiliation(s)
- Josué Ruano
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - María Jaramillo
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Martín Gómez
- Medicina Interna, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia.
| |
Collapse
|
|
37
|
Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
Collapse
Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
| |
Collapse
|
|
38
|
Wu H, Ou S, Zhang H, Huang R, Yu S, Zhao M, Tai S. Advances in biomarkers and techniques for pancreatic cancer diagnosis. Cancer Cell Int 2022; 22:220. [PMID: 35761336 PMCID: PMC9237966 DOI: 10.1186/s12935-022-02640-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/18/2022] [Indexed: 12/31/2022] Open
Abstract
Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.
Collapse
|
|
39
|
Rao B H, Trieu JA, Nair P, Gressel G, Venu M, Venu RP. Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions. Artif Intell Gastrointest Endosc 2022; 3:16-30. [DOI: 10.37126/aige.v3.i3.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/07/2022] [Accepted: 05/07/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal (GI) endoscopy. Despite vast improvements in endoscopic techniques and equipment, diagnostic endoscopy remains heavily operator-dependent, in particular, colonoscopy and endoscopic ultrasound (EUS). Recent reports have shown that as much as 25% of colonic adenomas may be missed at colonoscopy. This can result in an increased incidence of interval colon cancer. Similarly, EUS has been shown to have high inter-observer variability, overlap in diagnoses with a relatively low specificity for pancreatic lesions. Our understanding of Machine-learning (ML) techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers. ML is an aspect of AI that is based on neural networks, and is widely used for image classification, object detection, and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems. In this review, current status and limitations of ML, specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions, will be summarized from existing literature. This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy.
Collapse
Affiliation(s)
- Harshavardhan Rao B
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| | - Judy A Trieu
- Internal Medicine - Gastroenterology, Loyola University Medical Center, Maywood, IL 60153, United States
| | - Priya Nair
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| | - Gilad Gressel
- Center for Cyber Security Systems and Networks, Amrita Vishwavidyapeetham, Kollam 690546, Kerala, India
| | - Mukund Venu
- Internal Medicine - Gastroenterology, Loyola University Medical Center, Maywood, IL 60153, United States
| | - Rama P Venu
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| |
Collapse
|
|
40
|
Dietrich CF, Shi L, Koch J, Löwe A, Dong Y, Cui X, Worni M, Jenssen C. Early detection of pancreatic tumors by advanced EUS imaging. Minerva Gastroenterol (Torino) 2022; 68:133-143. [PMID: 33337117 DOI: 10.23736/s2724-5985.20.02789-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The early detection of pancreatic ductal adenocarcinoma (PDAC) dramatically improves outcome. All available state-of-the-art imaging methods allow early detection with EUS being the best technique for exclusion of PDAC and detection of very early PDAC. Etiological differentiation of small SPL is important to guide individually tailored patients' management including radical surgery in resectable PDAC, medical (neoadjuvant or palliative intended) treatment in patients with non-resectable malignancy, pancreatic parenchyma saving strategies in some non-PDAC, and follow-up in particular in low-grade PanNEN or other small benign lesions. Multimodality EUS imaging including B-Mode assessment, elastography, contrast-enhancement and EUS-guided sampling is the most appropriate technique for diagnosis and risk assessment of small SPL. We present a review discussing modern (endoscopic) ultrasound imaging techniques including contrast enhanced ultrasound and elastography for the early detection and characterization of solid pancreatic lesions.
Collapse
Affiliation(s)
- Christoph F Dietrich
- Department Allgemeine Innere Medizin, Beau Site Clinic, Salem-Spital, Kliniken Hirslanden, Bern, Switzerland -
| | - Long Shi
- Department of Ultrasound, Jingmen N.2 People's Hospital, Jingmen, China
| | - Jonas Koch
- Department Allgemeine Innere Medizin, Beau Site Clinic, Salem-Spital, Kliniken Hirslanden, Bern, Switzerland
| | - Axel Löwe
- Department Allgemeine Innere Medizin, Beau Site Clinic, Salem-Spital, Kliniken Hirslanden, Bern, Switzerland
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Mathias Worni
- Department of Visceral Surgery, Clarunis, St. Clara Hospital and University Hospital, University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland
- Campus SLB, Swiss Institute for Translational and Entrepreneurial Medicine, Stiftung Lindenhof, Bern, Switzerland
- Department of Surgery, Beau Site Clinic, Bern, Switzerland
| | - Christian Jenssen
- Department of Internal Medicine, Krankenhaus Märkisch Oderland, Strausberg, Germany
- Brandenburg Institute for Clinical Ultrasound, Medical University Brandenburg, Neuruppin, Germany
| |
Collapse
|
|
41
|
Low DJ, Hong Z, Lee JH. Artificial intelligence implementation in pancreaticobiliary endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:493-498. [PMID: 35639864 DOI: 10.1080/17474124.2022.2083604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Artificial intelligence has been rapidly deployed in gastroenterology and endoscopy. The acceleration of deep convolutional neural networks along with hardware development has allowed implementation of artificial intelligence algorithms into real-time endoscopy, particularly colonoscopy. However, artificial intelligence implementation in pancreaticobiliary endoscopy is nascent. AREAS COVERED Initial studies have been conducted in endoscopic retrograde pancreatography (ERCP), endoscopic ultrasound (EUS), and digital single operator cholangioscopy (DSOC). Machine learning has been implemented in identifying significant landmarks, including the ampulla on ERCP, and the bile duct, pancreas, and portal confluence on EUS. Moreover, artificial intelligence algorithms have been deployed in differentiating pathology including pancreas cancer, autoimmune pancreatitis, pancreatic cystic lesions, and biliary strictures. EXPERT OPINION There have been relatively few studies with limited sample sizes in developing these machine learning algorithms. Despite the early successful demonstration of artificial intelligence in pancreaticobiliary endoscopy, additional research needs to be conducted with larger data sets to improve generalizability and assessed in real-time endoscopy before clinical implementation. However, pancreaticobiliary endoscopy remains a promising avenue of artificial intelligence application with the potential to improve clinical practice and outcomes.
Collapse
Affiliation(s)
- Daniel J Low
- Department of Gastroenterology Hepatology and Nutrition, Division of Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA.,Division of Gastroenterology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Zhuoqiao Hong
- System Design & Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeffrey H Lee
- Department of Gastroenterology Hepatology and Nutrition, Division of Internal Medicine, MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
|
42
|
Minchenberg SB, Walradt T, Glissen Brown JR. Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy. World J Gastrointest Oncol 2022; 14:989-1001. [PMID: 35646286 PMCID: PMC9124983 DOI: 10.4251/wjgo.v14.i5.989] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.
Collapse
Affiliation(s)
- Scott B Minchenberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Trent Walradt
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Jeremy R Glissen Brown
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| |
Collapse
|
|
43
|
Fujimori N, Minoda Y, Ogawa Y. What is the best modality for diagnosing pancreatic cancer? Dig Endosc 2022; 34:744-746. [PMID: 35318739 DOI: 10.1111/den.14283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
This Editorial refers to the article by S. Omoto et al., p 198‐206 of this issue DEN 34:1.
Collapse
Affiliation(s)
- Nao Fujimori
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yosuke Minoda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Endoscopic Diagnostics and Therapeutics, Kyushu University Hospital, Fukuoka, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| |
Collapse
|
|
44
|
Cazacu IM, Saftoiu A, Bhutani MS. Advanced EUS Imaging Techniques. Dig Dis Sci 2022; 67:1588-1598. [PMID: 35451709 DOI: 10.1007/s10620-022-07486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/21/2022] [Indexed: 12/09/2022]
Affiliation(s)
- Irina M Cazacu
- Department of Oncology, Fundeni Clinical Institute, Bucharest, Romania.,Faculty of Medicine, Titu Maiorescu University, Bucharest, Romania
| | - Adrian Saftoiu
- Department of Gastroenterology, Elias Emergency University Hospital, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Manoop S Bhutani
- Department of Gastroenterology, Hepatology and Nutrition-Unit 1466, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX, 77030-4009, USA.
| |
Collapse
|
|
45
|
Goyal H, Sherazi SAA, Gupta S, Perisetti A, Achebe I, Ali A, Tharian B, Thosani N, Sharma NR. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. Therap Adv Gastroenterol 2022; 15:17562848221093873. [PMID: 35509425 PMCID: PMC9058356 DOI: 10.1177/17562848221093873] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 03/01/2022] [Indexed: 02/04/2023] Open
Abstract
Background Pancreatic cancer (PC) is a highly fatal malignancy with a global overall 5-year survival of under 10%. Screening of PC is not recommended outside of clinical trials. Endoscopic ultrasonography (EUS) is a very sensitive test to identify PC but lacks specificity and is operator-dependent, especially in the presence of chronic pancreatitis (CP). Artificial Intelligence (AI) is a growing field with a wide range of applications to augment the currently available modalities. This study was undertaken to study the effectiveness of AI with EUS in the diagnosis of PC. Methods Studies from MEDLINE and EMBASE databases reporting the AI performance applied to EUS imaging for recognizing PC. Data were analyzed using descriptive statistics. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the quality of the included studies. Results A total of 11 articles reported the role of EUS in the diagnosis of PC. The overall accuracy, sensitivity, and specificity of AI in recognizing PC were 80-97.5%, 83-100%, and 50-99%, respectively, with corresponding positive predictive value (PPV) and negative predictive value (NPV) of 75-99% and 57-100%, respectively. Types of AI studied were artificial neural networks (ANNs), convolutional neural networks (CNN), and support vector machine (SVM). Seven studies using other than basic ANN reported a sensitivity and specificity of 88-96% and 83-94% to differentiate PC from CP. Two studies using SVM reported a 94-96% sensitivity, 93%-99% specificity, and 94-98% accuracy to diagnose PC from CP. The reported sensitivity and specificity of detection of malignant from benign Intraductal Papillary Mucinous Neoplasms (IPMNs) was 96% and 92%, respectively. Conclusion AI reported a high sensitivity with high specificity and accuracy to diagnose PC, differentiate PC from CP, and differentiate benign from malignant IPMN when used with EUS.
Collapse
Affiliation(s)
- Hemant Goyal
- The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18503, USA
| | - Syed Ali Amir Sherazi
- Department of Medicine, John H. Stroger J.r Hospital of Cook County, Chicago, IL, USA
| | - Shweta Gupta
- Division of Hematology-Oncology, John H. Stroger Jr. Hospital of Cook County, Chicago, IL, USA
| | - Abhilash Perisetti
- Advanced Interventional Oncology and Surgical Endoscopy, Parkview Cancer Institute, Parkview Health, Fort Wayne, IN, USA
| | - Ikechukwu Achebe
- Department of Medicine, John H. Stroger J.r Hospital of Cook County, Chicago, IL, USA
| | - Aman Ali
- Commonwealth Medical College, Scranton, PA, USA; Wilkes Barre General Hospital, Wilkes-Barre, PA, USA; Digestive Care Associates, Kingston, PA, USA
| | - Benjamin Tharian
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Nirav Thosani
- Center for Interventional Gastroenterology at UTHealth (iGUT), Houston, TX, USADivision of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, Houston, TX, USA
| | - Neil R. Sharma
- Division of interventional Oncology & Surgical Endoscopy (IOSE), Parkview cancer Institute, Fort Wayne, IN, USAIndiana University School of Medicine, Fort Wayne, IN, USA
| |
Collapse
|
|
46
|
Lin KW, Ang TL, Li JW. Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma. Artif Intell Med Imaging 2022; 3:21-32. [DOI: 10.35711/aimi.v3.i2.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.
Collapse
Affiliation(s)
- Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| |
Collapse
|
|
47
|
Mohan BP, Facciorusso A, Khan SR, Madhu D, Kassab LL, Ponnada S, Chandan S, Crino SF, Kochhar GS, Adler DG, Wallace MB. Pooled diagnostic parameters of artificial intelligence in EUS image analysis of the pancreas: A descriptive quantitative review. Endosc Ultrasound 2022; 11:156-169. [PMID: 35313417 PMCID: PMC9258019 DOI: 10.4103/eus-d-21-00063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
EUS is an important diagnostic tool in pancreatic lesions. Performance of single-center and/or single study artificial intelligence (AI) in the analysis of EUS-images of pancreatic lesions has been reported. The aim of this study was to quantitatively study the pooled rates of diagnostic performance of AI in EUS image analysis of pancreas using rigorous systematic review and meta-analysis methodology. Multiple databases were searched (from inception to December 2020) and studies that reported on the performance of AI in EUS analysis of pancreatic adenocarcinoma were selected. The random-effects model was used to calculate the pooled rates. In cases where multiple 2 × 2 contingency tables were provided for different thresholds, we assumed the data tables as independent from each other. Heterogeneity was assessed by I2% and 95% prediction intervals. Eleven studies were analyzed. The pooled overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 86% (95% confidence interval [82.8–88.6]), 90.4% (88.1–92.3), 84% (79.3–87.8), 90.2% (87.4–92.3) and 89.8% (86–92.7), respectively. On subgroup analysis, the corresponding pooled parameters in studies that used neural networks were 85.5% (80–89.8), 91.8% (87.8–94.6), 84.6% (73–91.7), 87.4% (82–91.3), and 91.4% (83.7–95.6)], respectively. Based on our meta-analysis, AI seems to perform well in the EUS-image analysis of pancreatic lesions.
Collapse
Affiliation(s)
- Babu P Mohan
- Department of Gastroenterology and Hepatology, University of Utah, Salt Lake City, Utah, USA
| | | | - Shahab R Khan
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Deepak Madhu
- Department of Gastroenterology, Aster MIMS, Calicut, Kerala, India
| | - Lena L Kassab
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Suresh Ponnada
- Department of Internal Medicine, Roanoke Medical Center, Roanoke, Virginia, USA
| | - Saurabh Chandan
- Department of Gastroenterology, CHI Creighton University Medical Center, Omaha, NE, USA
| | - Stefano F Crino
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas Institute, G. B. Rossi University Hospital, Verona, Italy
| | - Gursimran S Kochhar
- Department of Gastroenterology and Hepatology, Allegheny Health Network, Pittsburgh, PA, USA
| | - Douglas G Adler
- Department of Gastroenterology and Hepatology, University of Utah, Salt Lake City, Utah, USA
| | - Michael B Wallace
- Department of Gastroenterology and Hepatology, Sheikh Shahkbout Medical City, Abu Dhabi, UAE
| |
Collapse
|
|
48
|
Iwasa Y, Iwashita T, Ichikawa H, Mita N, Uemura S, Yoshida K, Iwata K, Mukai T, Yasuda I, Shimizu M. Efficacy of Contrast-Enhanced Harmonic Endoscopic Ultrasound for Pancreatic Solid Tumors with a Combination of Qualitative and Quantitative Analyses: A Prospective Pilot Study. Dig Dis Sci 2022; 67:1054-1064. [PMID: 33730346 DOI: 10.1007/s10620-021-06931-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/26/2021] [Indexed: 12/09/2022]
Abstract
INTRODUCTION Image evaluation of contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS) and additional time-intensity curve (TIC) analysis enable qualitative and quantitative analyses of pancreatic tumor based on real-time perfusion imaging. AIMS To evaluate the efficacy of CEH-EUS with a combination of qualitative and quantitative analyses of pancreatic solid tumors. METHODS Patients were scheduled to undergo EUS-guided fine needle aspiration (FNA) for pancreatic solid tumors were prospectively enrolled between 11/2016 and 12/2018 and underwent CEH-EUS. The vascular and enhancement patterns were qualitatively evaluated and heterogeneous enhancement was defined to be indicative of malignancy. The echo intensity change during 60 s in the tumor was quantitatively evaluated by time intensity curve analysis. RESULTS In total, 100 patients were enrolled in this study. The final diagnoses were malignant lesions in 87 patients and benign legions in 13 patients. There were four categories of enhancement and patterns: hypovascular with heterogeneous, hypovascular with homogeneous, hypervascular heterogeneous, and hypervascular homogeneous enhancement. The diagnostic capability of qualitative analysis was the sensitivity, specificity, and accuracy of 89%, 62%, and 85%, respectively. With respect to time intensity curve analysis, the time to peak of malignant lesions was significantly shorter than those of benign lesions (P = 0.0009) with an optimal cutoff value of 12.81 s on the receiver operating characteristic curve analysis. With the combination of qualitative and quantitative analyses, the sensitivity, specificity, and accuracy were improved to 100%, 54%, and 94%, respectively. CONCLUSIONS CEH-EUS with combined qualitative and quantitative analyses for pancreatic tumors might be useful as a complement for EUS-FNA. The UMIN Clinical Trials Registry (UMIN000025192).
Collapse
Affiliation(s)
- Yuhei Iwasa
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan
| | - Takuji Iwashita
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan.
| | - Hironao Ichikawa
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan
| | - Naoki Mita
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan
| | - Shinya Uemura
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan
| | - Kensaku Yoshida
- Department of Gastroenterology, Gifu Prefectural General Medical Center, Gifu, Japan
| | - Keisuke Iwata
- Department of Gastroenterology, Gifu Prefectural General Medical Center, Gifu, Japan
| | - Tsuyoshi Mukai
- Department of Gastroenterology, Gifu Municipal Hospital, Gifu, Japan
| | - Ichiro Yasuda
- Third Department of Internal Medicine, University of Toyama Hospital, Toyama, Japan
| | - Masahito Shimizu
- First Department of Internal Medicine, Gifu University Hospital, Gifu, Japan
| |
Collapse
|
|
49
|
Saraiva MM, Ribeiro T, Ferreira JPS, Boas FV, Afonso J, Santos AL, Parente MPL, Jorge RN, Pereira P, Macedo G. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study. Gastrointest Endosc 2022; 95:339-348. [PMID: 34508767 DOI: 10.1016/j.gie.2021.08.027] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 08/25/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. METHODS We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. CONCLUSIONS The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.
Collapse
Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal; Department of Gastroenterology, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P S Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal
| | - Filipe Vilas Boas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal; Department of Gastroenterology, Faculty of Medicine, University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Ana Luísa Santos
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal; Department of Gastroenterology, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Marco P L Parente
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal
| | - Renato N Jorge
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal
| | - Pedro Pereira
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal; Department of Gastroenterology, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal; WGO Gastroenterology and Hepatology Training Center, Porto, Portugal; Department of Gastroenterology, Faculty of Medicine, University of Porto, Porto, Portugal
| |
Collapse
|
|
50
|
Dumitrescu EA, Ungureanu BS, Cazacu IM, Florescu LM, Streba L, Croitoru VM, Sur D, Croitoru A, Turcu-Stiolica A, Lungulescu CV. Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:309. [PMID: 35204400 PMCID: PMC8870917 DOI: 10.3390/diagnostics12020309] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 12/12/2022] Open
Abstract
We performed a meta-analysis of published data to investigate the diagnostic value of artificial intelligence for pancreatic cancer. Systematic research was conducted in the following databases: PubMed, Embase, and Web of Science to identify relevant studies up to October 2021. We extracted or calculated the number of true positives, false positives true negatives, and false negatives from the selected publications. In total, 10 studies, featuring 1871 patients, met our inclusion criteria. The risk of bias in the included studies was assessed using the QUADAS-2 tool. R and RevMan 5.4.1 software were used for calculations and statistical analysis. The studies included in the meta-analysis did not show an overall heterogeneity (I2 = 0%), and no significant differences were found from the subgroup analysis. The pooled diagnostic sensitivity and specificity were 0.92 (95% CI, 0.89-0.95) and 0.9 (95% CI, 0.83-0.94), respectively. The area under the summary receiver operating characteristics curve was 0.95, and the diagnostic odds ratio was 128.9 (95% CI, 71.2-233.8), indicating very good diagnostic accuracy for the detection of pancreatic cancer. Based on these promising preliminary results and further testing on a larger dataset, artificial intelligence-assisted endoscopic ultrasound could become an important tool for the computer-aided diagnosis of pancreatic cancer.
Collapse
Affiliation(s)
- Elena Adriana Dumitrescu
- Institute of Oncology, Prof. Dr. Alexandru Trestioreanu, Șoseaua Fundeni, 022328 Bucharest, Romania;
- Doctoral School, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Bogdan Silviu Ungureanu
- Department of Gastroenterology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, Romania;
| | - Irina M. Cazacu
- Department of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, Romania; (I.M.C.); (A.C.)
| | - Lucian Mihai Florescu
- Department of Radiology & Medical Imaging, University of Medicine and Pharmacy Craiova, 2-4 Petru Rares St, 200349 Craiova, Romania;
| | - Liliana Streba
- Department of Oncology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, Romania; (L.S.); (C.V.L.)
| | - Vlad M. Croitoru
- Department of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, Romania; (I.M.C.); (A.C.)
| | - Daniel Sur
- 11th Department of Medical Oncology, University of Medicine and Pharmacy Iuliu Hatieganu, 400012 Cluj-Napoca, Romania
| | - Adina Croitoru
- Department of Oncology, Fundeni Clinical Institute, 258 Fundeni St, 022238 Bucharest, Romania; (I.M.C.); (A.C.)
| | - Adina Turcu-Stiolica
- Department of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, 2 Petru Rares Str, 200349 Craiova, Romania;
| | - Cristian Virgil Lungulescu
- Department of Oncology, University of Medicine and Pharmacy Craiova, 2 Petru Rares Str, 200349 Craiova, Romania; (L.S.); (C.V.L.)
| |
Collapse
|