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Lin Y, Zhang X, Li F, Zhang R, Jiang H, Lai C, Yi L, Li Z, Wu W, Qiu L, Yang H, Guan Q, Wang Z, Deng L, Zhao Z, Lu W, Lun W, Dai J, He S, Bai Y. A deep neural network improves endoscopic detection of laterally spreading tumors. Surg Endosc 2025; 39:776-785. [PMID: 39578289 DOI: 10.1007/s00464-024-11409-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Accepted: 11/03/2024] [Indexed: 11/24/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally spreading tumors (LSTs) of the large intestine have unique morphological characteristics, special growth patterns and higher malignant potential. Therefore, LSTs are a precancerous lesion of CRC that could be easily missed. OBJECTIVE The purpose of this study was to establish an LSTs lesion detection algorithm based on the YOLOv7 model and to evaluate the detection performance of the algorithm on LSTs. METHOD A total of 7985 LSTs images and 93,197 non-LSTs images were included in this study, and the training set, validation set, and 80% of the data in the dataset is used for training, 10% for validation, and 10% for testing. In detail, a total of 6261 LSTs images and 74,798 non-LSTs images were used as the training set to train the LSTs lesion detection algorithm to identify LSTs. A total of 743 LSTs images and 9486 non-LSTs images were used as validation set to evaluate the learning ability of the LSTs lesion detection algorithm. A total of 981 LSTs images and 8913 non-LSTs images were used as test set to evaluate the generalization ability of the LSTs lesion detection algorithm. To evaluate the diagnostic ability of the LSTs lesion detection algorithm for LSTs, we selected 3636 images (562 LSTs, 3074 non-LSTs) images from the test set as the subtest set. Finally, we compared the performance of the AI algorithm with endoscopist in the diagnosis of LSTs. RESULT The accuracy of LSTs lesion detection algorithm in identifying LSTs is 99.34%, sensitivity is 96.88%, specificity is 99.8%, positive predictive value is 98.94%, and negative predictive value is 99.41%. CONCLUSION Our model based on the YOLOv7 achieved high diagnostic accuracy in LSTs lesion, significantly better than that of novice and senior doctors, and reaching the same level as expert endoscopists.
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Affiliation(s)
- Yu Lin
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xigang Zhang
- Department of Gastroenterology, Shenzhen Second People's Hospital, Shenzhen, China
| | - Feng Li
- Department of Gastroenterology, Shenzhen Hospital of Beijing University of Chinese Medicine (Longgang), Shenzhen, China
| | - Ruiya Zhang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Haiyang Jiang
- Department of Gastroenterology, Shayang Hospital of Traditional Chinese Medicine, Jingmen, China
| | - Chunxiao Lai
- Department of Gastroenterology, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lizhi Yi
- Department of Gastroenterology, The People's Hospital of Leshan, Leshan, China
| | - Zhijian Li
- Department of Gastroenterology, Shunde Hospital, Southern Medical University, Foshan, China
| | - Wen Wu
- Shanxi Academy of Traditional Chinese Medicine, Taiyuan, China
| | - Lin Qiu
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hui Yang
- Department of Gastroenterology, Rizhao Hospital of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Rizhao, China
| | - Quansheng Guan
- Department of Gastroenterology, Shayang Hospital of Traditional Chinese Medicine, Jingmen, China
| | - Zhenyu Wang
- Department of Digestive Endoscope, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Lv Deng
- Department of Gastroenterology, People's Hospital of Rong Jiang County, Rong Jiang, China
| | - Zhifang Zhao
- Department of Gastroenterology, People's Hospital of Rong Jiang County, Rong Jiang, China
| | - Weimin Lu
- Suzhou Wellomen Information Technology Co., Ltd., Suzhou, China
| | - Weijian Lun
- Department of Gastroenterology, People's Hospital of Nanhai District, Foshan, China.
| | - Jie Dai
- Suzhou Wellomen Information Technology Co., Ltd., Suzhou, China.
| | - Shunhui He
- Department of Gastroenterology, Shunde Hospital, Southern Medical University, Foshan, China.
| | - Yang Bai
- Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, Guangzhou, China.
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Savino A, Rondonotti E, Rocchetto S, Piagnani A, Bina N, Di Domenico P, Segatta F, Radaelli F. GI genius endoscopy module: a clinical profile. Expert Rev Med Devices 2024; 21:359-372. [PMID: 38618982 DOI: 10.1080/17434440.2024.2342508] [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: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. AREAS COVERED This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. EXPERT OPINION Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.
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Affiliation(s)
- Alberto Savino
- Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
| | | | - Simone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Pasquale Di Domenico
- Gastrointestinal Unit, Department of Medicine, Surgery & Dentistry Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Francesco Segatta
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
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Yuan L, Zhou H, Xiao X, Zhang X, Chen F, Liu L, Liu J, Bao S, Tao K. Development and external validation of a transfer learning-based system for the pathological diagnosis of colorectal cancer: a large emulated prospective study. Front Oncol 2024; 14:1365364. [PMID: 38725622 PMCID: PMC11079287 DOI: 10.3389/fonc.2024.1365364] [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: 01/19/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Background The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. Method In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Results Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Conclusion Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients.
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Affiliation(s)
- Liuhong Yuan
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Henghua Zhou
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | | | - Xiuqin Zhang
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Feier Chen
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Lin Liu
- Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | | | - Shisan Bao
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
| | - Kun Tao
- Department of Pathology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai, China
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, Liu PX, Xiong F, Deng MM, Xia HF, Li JJ, Long XQ, Song Y, Li LP. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf) 2023; 11:goac081. [PMID: 36686571 PMCID: PMC9850273 DOI: 10.1093/gastro/goac081] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/21/2023] Open
Abstract
Background In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy. Methods A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4). Results A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001). Conclusions Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).
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Affiliation(s)
| | | | - Min Kang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Xue Peng
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Mei-Ling Shu
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Guan-Yu Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Pei-Xi Liu
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Ming-Ming Deng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Hong-Fen Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Jian-Jun Li
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Xiao-Qi Long
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Yan Song
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Liang-Ping Li
- Corresponding author. Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No.32 West Second Section, First Ring Road, Chengdu, Sichuan 610072, China. Tel: +86-28-8739 3927;
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Murakami T, Kurosawa T, Fukushima H, Shibuya T, Yao T, Nagahara A. Sessile serrated lesions: Clinicopathological characteristics, endoscopic diagnosis, and management. Dig Endosc 2022; 34:1096-1109. [PMID: 35352394 DOI: 10.1111/den.14273] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/30/2022] [Accepted: 02/13/2022] [Indexed: 02/08/2023]
Abstract
The 2019 World Health Organization (WHO) Classification of Tumours of the Digestive System (5th edition) introduced the term "sessile serrated lesion" (SSL) to replace the term "sessile serrated adenoma/polyp" (SSA/P). SSLs are early precursor lesions in the serrated neoplasia pathway that result in colorectal carcinomas with BRAF mutations, methylation for DNA repair genes, a CpG island methylator phenotype, and high levels of microsatellite instability. Some of these lesions can rapidly become dysplastic or invasive carcinomas that exhibit high lymphatic invasion and lymph node metastasis potential. The 2019 WHO classification noted that dysplasia arising in an SSL most likely is an advanced polyp, regardless of the morphologic grade of the dysplasia. Detecting SSLs with or without dysplasia is critical; however, detection of SSLs is challenging, and their identification by endoscopists and pathologists is inconsistent. Furthermore, indications for their endoscopic treatment have not been established. Moreover, SSLs are considered to contribute to the development of post-colonoscopy colorectal cancers. Herein, the clinicopathological and endoscopic characteristics of SSLs, including features determined using white light and image-enhanced endoscopy, therapeutic indications, therapeutic methods, and surveillance are reviewed based on the literature. This information may lead to more intensive research to improve detection, diagnosis, and rates of complete resection of these lesions and reduce post-colonoscopy colorectal cancer rates.
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Affiliation(s)
- Takashi Murakami
- Departments of 1Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
| | - Taro Kurosawa
- Departments of 1Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
- Human Pathology, Juntendo University School of Medicine, Tokyo, Japan
| | - Hirofumi Fukushima
- Departments of 1Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
| | - Tomoyoshi Shibuya
- Departments of 1Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takashi Yao
- Human Pathology, Juntendo University School of Medicine, Tokyo, Japan
| | - Akihito Nagahara
- Departments of 1Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
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Iwatate M, Hirata D, Francisco CPD, Co JT, Byeon J, Joshi N, Banerjee R, Quach DT, Aye TT, Chiu H, Lau LHS, Ng SC, Ang TL, Khomvilai S, Li X, Ho S, Sano W, Hattori S, Fujita M, Murakami Y, Shimatani M, Kodama Y, Sano Y, The CATCH project team. Efficacy of international web-based educational intervention in the detection of high-risk flat and depressed colorectal lesions higher (CATCH project) with a video: Randomized trial. Dig Endosc 2022; 34:1166-1175. [PMID: 35122323 PMCID: PMC9540870 DOI: 10.1111/den.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/14/2022] [Accepted: 01/23/2022] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Three subcategories of high-risk flat and depressed lesions (FDLs), laterally spreading tumors non-granular type (LST-NG), depressed lesions, and large sessile serrated lesions (SSLs), are highly attributable to post-colonoscopy colorectal cancer (CRC). Efficient and organized educational programs on detecting high-risk FDLs are lacking. We aimed to explore whether a web-based educational intervention with training on FIND clues (fold deformation, intensive stool/mucus attachment, no vessel visibility, and demarcated reddish area) may improve the ability to detect high-risk FDLs. METHODS This was an international web-based randomized control trial that enrolled non-expert endoscopists in 13 Asian countries. The participants were randomized into either education or non-education group. All participants took the pre-test and post-test to read 60 endoscopic images (40 high-risk FDLs, five polypoid, 15 no lesions) and answered whether there was a lesion. Only the education group received a self-education program (video and training questions and answers) between the tests. The primary outcome was a detection rate of high-risk FDLs. RESULTS In total, 284 participants were randomized. After excluding non-responders, the final data analyses were based on 139 participants in the education group and 130 in the non-education group. The detection rate of high-risk FDLs in the education group significantly improved by 14.7% (66.6-81.3%) compared with -0.8% (70.8-70.0%) in the non-education group. Similarly, the detection rate of LST-NG, depressed lesions, and large SSLs significantly increased only in the education group by 12.7%, 12.0%, and 21.6%, respectively. CONCLUSION Short self-education focusing on detecting high-risk FDLs was effective for Asian non-expert endoscopists. (UMIN000042348).
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Affiliation(s)
- Mineo Iwatate
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
| | - Daizen Hirata
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
- Department of Gastroenterology and HepatologyKindai UniversityOsakaJapan
| | | | - Jonard Tan Co
- Institute of Digestive and Liver DiseasesSt. Luke’s Medical CenterTaguig CityPhilippines
| | - Jeong‐Sik Byeon
- Department of GastroenterologyAsan Medical CenterUniversity of Ulsan College of MedicineSeoulKorea
| | - Neeraj Joshi
- Gastro Enterology UnitNepal Cancer Hospital and Research CentreLalitpurNepal
| | - Rupa Banerjee
- Medical GastroenterologyAsian Institute of GastroenterologyNew DelhiIndia
| | - Duc Trong Quach
- University of Medicine and Pharmacy at Ho Chi Minh CityHo Chi MinhVietnam
| | | | - Han‐Mo Chiu
- Department of Internal MedicineNational Taiwan University HospitalTaipeiTaiwan
| | - Louis H. S. Lau
- Department of Medicine and TherapeuticsFaculty of MedicineInstitute of Digestive DiseaseThe Chinese University of Hong KongHong KongChina
| | - Siew C. Ng
- Department of Medicine and TherapeuticsFaculty of MedicineInstitute of Digestive DiseaseThe Chinese University of Hong KongHong KongChina
| | - Tiing Leong Ang
- Department of Gastroenterology and HepatologyChangi General HospitalSingHealthSingapore
| | - Supakij Khomvilai
- Surgical EndoscopyColorectal DivisionDepartment of SurgeryFaculty of MedicineChulalongkorn UniversityBangkokThailand
| | - Xiao‐Bo Li
- Division of Gastroenterology and HepatologyKey Laboratory of Gastroenterology and HepatologyMinistry of Health, Renji HospitalSchool of MedicineShanghai Institute of Digestive DiseaseShanghai Jiao Tong UniversityShanghaiChina
| | - Shiaw‐Hooi Ho
- Department of MedicineFaculty of MedicineUniversity of MalayaKuala LumpurMalaysia
| | - Wataru Sano
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
| | - Santa Hattori
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
| | - Mikio Fujita
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
| | | | - Masaaki Shimatani
- The Third Department of Internal MedicineDivision of Gastroenterology and HepatologyKansai Medical University Medical CenterOsakaJapan
| | - Yuzo Kodama
- Division of GastroenterologyDepartment of Internal MedicineKobe University Graduate School of MedicineHyogoJapan
| | - Yasushi Sano
- Gastrointestinal Center and Institute of Minimally‐invasive Endoscopic Care (iMEC)Sano HospitalHyogoJapan
- Kansai Medical UniversityOsakaJapan
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Ahmad OF, González-Bueno Puyal J, Brandao P, Kader R, Abbasi F, Hussein M, Haidry RJ, Toth D, Mountney P, Seward E, Vega R, Stoyanov D, Lovat LB. Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia. Dig Endosc 2022; 34:862-869. [PMID: 34748665 DOI: 10.1111/den.14187] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 10/22/2021] [Accepted: 11/05/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. METHODS An AI algorithm was evaluated on four video test datasets containing 173 polyps (35,114 polyp-positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by eight endoscopists (four independent, four trainees, according to the Joint Advisory Group on gastrointestinal endoscopy [JAG] standards in the UK). RESULTS In the first two video datasets, the algorithm achieved per-polyp sensitivities of 100% and 98.9%. Per-frame sensitivities were 84.1% and 85.2%. In the subtle dataset, the algorithm detected a significantly higher number of polyps (P < 0.0001), compared to JAG-independent and trainee endoscopists, achieving per-polyp sensitivities of 79.5%, 37.2% and 11.5%, respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. CONCLUSIONS The AI based algorithm achieved high per-polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer.
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Affiliation(s)
- Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Juana González-Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Odin Vision Ltd, London, UK
| | - Patrick Brandao
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Odin Vision Ltd, London, UK
| | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Division of Surgery and Interventional Sciences, University College London, London, UK
| | - Faisal Abbasi
- Division of Surgery and Interventional Sciences, University College London, London, UK
| | - Mohamed Hussein
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Division of Surgery and Interventional Sciences, University College London, London, UK
| | - Rehan J Haidry
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | | | | | - Ed Seward
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Roser Vega
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
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9
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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10
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Yoon D, Kong HJ, Kim BS, Cho WS, Lee JC, Cho M, Lim MH, Yang SY, Lim SH, Lee J, Song JH, Chung GE, Choi JM, Kang HY, Bae JH, Kim S. Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network. Sci Rep 2022; 12:261. [PMID: 34997124 PMCID: PMC8741803 DOI: 10.1038/s41598-021-04247-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/20/2021] [Indexed: 12/28/2022] Open
Abstract
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.
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Affiliation(s)
- Dan Yoon
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Hyoun-Joong Kong
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea
| | - Byeong Soo Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Woo Sang Cho
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Jung Chan Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, South Korea.,Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea
| | - Minwoo Cho
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Min Hyuk Lim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Sun Young Yang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Seon Hee Lim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Jooyoung Lee
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Ji Hyun Song
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Ji Min Choi
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Hae Yeon Kang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Jung Ho Bae
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea. .,Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea. .,Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea.
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11
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Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. THE VISUAL COMPUTER 2021; 39:2207-2222. [PMID: 34744231 PMCID: PMC8557108 DOI: 10.1007/s00371-021-02322-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 05/07/2023]
Abstract
With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Jixiang Zhang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
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12
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Zhu XW, Yan J, He YL, Liu G, Li X. Application of deep learning based artificial intelligence technology in identification of colorectal polyps. Shijie Huaren Xiaohua Zazhi 2021; 29:1201-1206. [DOI: 10.11569/wcjd.v29.i20.1201] [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: 02/06/2023] Open
Abstract
Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.
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Affiliation(s)
- Xing-Wang Zhu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying-Li He
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Gang Liu
- Lanzhou University School of Information Science & Engineering, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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13
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Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2:185-197. [DOI: 10.37126/aige.v2.i4.185] [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: 06/11/2021] [Revised: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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14
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Li DH, Liu XY, Huang C, Deng CN, Zhang JL, Xu XW, Xu LB. Pathological Analysis and Endoscopic Characteristics of Colorectal Laterally Spreading Tumors. Cancer Manag Res 2021; 13:1137-1144. [PMID: 33603459 PMCID: PMC7881785 DOI: 10.2147/cmar.s286039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/13/2021] [Indexed: 12/15/2022] Open
Abstract
Objective This study aims to analyze the endoscopic and pathological characteristics of colorectal laterally spreading tumors (LSTs) to assist malignant risk stratification to inform selection of the appropriate treatment strategy. Methods Patients with colorectal LST were selected as retrospective study objects. Characteristics, including endoscopic findings and the most common site of LSTs of different diameters and histological types, were analyzed. The risk factors for malignancy in colorectal LST were explored by multivariate logistic regression analysis. Results LSTs with diameters of ≥20 mm were found mainly in the rectum and mainly with granular-mixed (G-M) morphology (36% and 44.6%, respectively; p < 0.05), while LSTs with diameters of <20 mm were found mainly in the ascending colon and mainly with granular-homogenous (G-H) morphology (40.9% and 46.2%, respectively; p < 0.05). Adenoma was the main histological type in patients with tumors of all diameters. However, the cancerization rate of LSTs was 31% in patients with tumor diameter ≥20 mm, while there was no invasive cancer in patients with tumor diameter < 20 mm. In the low-grade dysphasia (adenoma) group, most of the lesions were located in the ascending colon and most had the morphology LST-G-H (35.8% and 39.2%, respectively; p < 0.05). In the cancerization group, most of the lesions were located in the rectum, with the morphology LST-G-M (51.6% and 67.2%, respectively; p < 0.05), and the diameter was larger than that of the adenoma group (33.84 ± 17.99 mm vs 21.68 ± 8.99 mm). Conclusion The rectum was the most common site for an LST with a diameter ≥20 mm and cancerization, of which the morphology was mainly LST-G-M (endoscopic submucosal dissection is the preferred treatment for this type of LST). LST malignancy was found to be correlated with lesion diameter, location, and morphological appearance.
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Affiliation(s)
- Da-Huan Li
- Department of the Digestive Endoscopy, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Xue-Ying Liu
- Department of the Digestive Endoscopy, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Chao Huang
- Department of the Emergency, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Chao-Nan Deng
- Department of the Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Jia-Lu Zhang
- Department of the Digestive Endoscopy, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Xiao-Wen Xu
- Department of the Digestive Endoscopy, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
| | - Liang-Bi Xu
- Department of the Digestive Endoscopy, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People's Republic of China
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15
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Shaukat A, Colucci D, Erisson L, Phillips S, Ng J, Iglesias JE, Saltzman JR, Somers S, Brugge W. Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device. Endosc Int Open 2021; 9:E263-E270. [PMID: 33553591 PMCID: PMC7857961 DOI: 10.1055/a-1321-1317] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022] Open
Abstract
Background and study aims Detecting colorectal neoplasia is the goal of high-quality screening and surveillance colonoscopy, as reflected by high adenoma detection rate (ADR) and adenomas per colonoscopy (APC). The aim of our study was to evaluate the performance of a novel artificial intelligence (AI)-aided polyp detection device, Skout, with the primary endpoints of ADR and APC in routine colonoscopy. Patients and methods We compared ADR and APC in a cohort of outpatients undergoing routine high-resolution colonoscopy with and without the use of a real-time, AI-aided polyp detection device. Patients undergoing colonoscopy with Skout were enrolled in a single-arm, unblinded, prospective trial and the results were compared with a historical cohort. All resected polyps were examined histologically. Results Eighty-three patients undergoing screening and surveillance colonoscopy at an outpatient endoscopy center were enrolled and outcomes compared with 283 historical control patients. Overall, ADR with and without Skout was 54.2 % and 40.6 % respectively ( P = 0.028) and 53.6 % and 30.8 %, respectively, in screening exams ( P = 0.024). Overall, APC rate with and without Skout was 1.46 and 1.01, respectively, ( P = 0.104) and 1.18 and 0.50, respectively, in screening exams ( P = 0.002). Overall, true histology rate (THR) with and without Skout was 73.8 % and 78.4 %, respectively, ( P = 0.463) and 75.0 % and 71.0 %, respectively, in screening exams ( P = 0.731). Conclusion We have demonstrated that our novel AI-aided polyp detection device increased the ADR in a cohort of patients undergoing screening and surveillance colonoscopy without a significant concomitant increase in hyperplastic polyp resection. AI-aided colonoscopy has the potential for improving the outcomes of patients undergoing colonoscopy.
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Affiliation(s)
- Aasma Shaukat
- University of Minnesota – GI, Minneapolis, Minnesota, United States
| | - Daniel Colucci
- Iterative Scopes, Cambridge, Massachusetts, United States
| | - Lavi Erisson
- Iterative Scopes, Cambridge, Massachusetts, United States
| | | | - Jonathan Ng
- Iterative Scopes, Cambridge, Massachusetts, United States
| | - Juan Eugenio Iglesias
- Iterative Scopes, Cambridge, Massachusetts, United States,University College London – European Research Council, London, United Kingdom,Massachusetts General Hospital – Martinos Center for Biological Imaging, Boston, Massachusetts, United States,Massachusetts Institute of Technology – MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, United States
| | - John R. Saltzman
- Brigham and Women’s Hospital – Gastroenterology, Boston, Massachusetts, United States
| | - Samuel Somers
- Concord Hospital – Gastroenterology, Concord, New Hampshire, United States
| | - William Brugge
- Mount Auburn Hospital – Gastroenterology, Cambridge, Massachusetts, United States
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16
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Hassan C, Bhandari P, Antonelli G, Repici A. Artificial intelligence for non-polypoid colorectal neoplasms. Dig Endosc 2021; 33:285-289. [PMID: 32767704 DOI: 10.1111/den.13807] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 07/31/2020] [Accepted: 08/04/2020] [Indexed: 12/15/2022]
Abstract
The miss rate of flat advanced colorectal neoplasia is still unacceptably high, especially in the Western setting, notwithstanding the widespread implementation of quality improvement programs and training. It is well known that flat morphology is associated with miss rate of colorectal neoplasia, and that this subset of lesions often shows a more aggressive biological behaviour. Artificial intelligence (AI) applied to the detection of colorectal neoplasia has been shown to increase adenoma detection rate, consistently across all lesion sizes and locations in the colon. However, there is still uncertainty whether AI can reduce the miss rate of flat advanced neoplasia, mainly because all published trials report a low number of flat colorectal lesions in their training sets, and this could reduce AI accuracy for this subset of lesions. In addition, flat lesions have different morphologies with variable prevalence and potentially different accuracy in their detection. For example, the subtle appearance and rarer frequency of a non-granular laterally spreading tumor (LST) could be much harder to identify than a granular mixed LST. In this review, we present a summary of the evidence on the role of AI in the identification of colorectal flat neoplasia, with a focus on data regarding presence of LSTs in the training/validation sets of the AI systems currently available on the market.
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17
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Liu P, Wang P, Glissen Brown JR, Berzin TM, Zhou G, Liu W, Xiao X, Chen Z, Zhang Z, Zhou C, Lei L, Xiong F, Li L, Liu X. The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. Therap Adv Gastroenterol 2020; 13:1756284820979165. [PMID: 33403003 PMCID: PMC7745558 DOI: 10.1177/1756284820979165] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/16/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Computer-aided detection (CADe) of colon polyps has been demonstrated to improve colon polyp and adenoma detection during colonoscopy by indicating the location of a given polyp on a parallel monitor. The aim of this study was to investigate whether embedding the CADe system into the primary colonoscopy monitor may serve to increase polyp and adenoma detection, without increasing physician fatigue level. METHODS Consecutive patients presenting for colonoscopies were prospectively randomized to undergo routine colonoscopy with or without the assistance of a real-time polyp detection CADe system. Fatigue level was evaluated from score 0 to 10 by the performing endoscopists after each colonoscopy procedure. The main outcome was adenoma detection rate (ADR). RESULTS Out of 790 patients analyzed, 397 were randomized to routine colonoscopy (control group), and 393 to a colonoscopy with computer-aided diagnosis (CADe group). The ADRs were 20.91% and 29.01%, respectively (OR = 1.546, 95% CI 1.116-2.141, p = 0.009). The average number of adenomas per colonoscopy (APC) was 0.29 and 0.48, respectively (Change Folds = 1.64, 95% CI 1.299-2.063, p < 0.001). The improvement in polyp detection was mainly due to increased detection of non-advanced diminutive adenomas, serrated adenoma and hyperplastic polyps. The fatigue score for each procedure was 3.28 versus 3.40 for routine and CADe group, p = 0.357. CONCLUSIONS A real-time CADe system employed on the primary endoscopy monitor may lead to improvements in ADR and polyp detection rate without increasing fatigue level during colonoscopy. The integration of a low-latency and high-performance CADe systems may serve as an effective quality assurance tool during colonoscopy. www.chictr.org.cn number, ChiCTR1800018058.
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Affiliation(s)
- Peixi Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, No.32 West Second Section First Ring Road, Chengdu, Sichuan, China
| | - Jeremy R. Glissen Brown
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Guanyu Zhou
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Weihui Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xun Xiao
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Ziyang Chen
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Zhihong Zhang
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Chao Zhou
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Lei Lei
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Fei Xiong
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Liangping Li
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xiaogang Liu
- Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
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