1
|
Popa SL, Stancu B, Ismaiel A, Turtoi DC, Brata VD, Duse TA, Bolchis R, Padureanu AM, Dita MO, Bashimov A, Incze V, Pinna E, Grad S, Pop AV, Dumitrascu DI, Munteanu MA, Surdea-Blaga T, Mihaileanu FV. Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications. Biomedicines 2023; 11:2991. [PMID: 38001991 PMCID: PMC10669430 DOI: 10.3390/biomedicines11112991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 10/26/2023] [Accepted: 11/05/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases. MATERIALS AND METHODS We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases. RESULTS Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics. CONCLUSIONS The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.
Collapse
Affiliation(s)
- Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Bogdan Stancu
- 2nd Surgical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania;
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Daria Claudia Turtoi
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Traian Adrian Duse
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Roxana Bolchis
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Alexandru Marius Padureanu
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Miruna Oana Dita
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Atamyrat Bashimov
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Victor Incze
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Edoardo Pinna
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Simona Grad
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Andrei-Vasile Pop
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania;
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania;
| | - Teodora Surdea-Blaga
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Florin Vasile Mihaileanu
- 2nd Surgical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania;
| |
Collapse
|
2
|
Wang X, Hu X, Xu Y, Yong J, Li X, Zhang K, Gan T, Yang J, Rao N. A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence. Therap Adv Gastroenterol 2023; 16:17562848231206991. [PMID: 37900007 PMCID: PMC10612444 DOI: 10.1177/17562848231206991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023] Open
Abstract
Background Magnetically controlled capsule endoscopy (MCCE) is a non-invasive, painless, comfortable, and safe equipment to diagnose gastrointestinal diseases (GID), partially overcoming the shortcomings of conventional endoscopy and wireless capsule endoscopy (WCE). With advancements in technology, the main technical parameters of MCCE have continuously been improved, and MCCE has become more intelligent. Objectives The aim of this systematic review was to summarize the research progress of MCCE and artificial intelligence (AI) in the diagnosis and treatment of GID. Data Sources and Methods We conducted a systematic search of PubMed and EMBASE for published studies on GID detection of MCCE, physical factors related to MCCE imaging quality, the application of AI in aiding MCCE, and its additional functions. We synergistically reviewed the included studies, extracted relevant data, and made comparisons. Results MCCE was confirmed to have the same performance as conventional gastroscopy and WCE in detecting common GID, while it lacks research in detecting early gastric cancer (EGC). The body position and cleanliness of the gastrointestinal tract are the main factors affecting imaging quality. The applications of AI in screening intestinal diseases have been comprehensive, while in the detection of common gastric diseases such as ulcers, it has been developed. MCCE can perform some additional functions, such as observations of drug behavior in the stomach and drug damage to the gastric mucosa. Furthermore, it can be improved to perform a biopsy. Conclusion This comprehensive review showed that the MCCE technology has made great progress, but studies on GID detection and treatment by MCCE are in the primary stage. Further studies are required to confirm the performance of MCCE.
Collapse
Affiliation(s)
- Xiaotong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Hu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxue Xu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiahao Yong
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Digestive Endoscopic Center of West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Chengdu, Sichuan Province 610017, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section Two, Jianshe North Road, Chengdu 610054, China
| |
Collapse
|
3
|
Mohan A, Asghar Z, Abid R, Subedi R, Kumari K, Kumar S, Majumder K, Bhurgri AI, Tejwaney U, Kumar S. Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review. Ann Med Surg (Lond) 2023; 85:4920-4927. [PMID: 37811030 PMCID: PMC10553069 DOI: 10.1097/ms9.0000000000001175] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/05/2023] [Indexed: 10/10/2023] Open
Abstract
Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning and machine learning, have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by correlating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature, which provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease's poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of the application of AI in the diagnosis, treatment, and prognosis of esophageal cancer and how computer-aided diagnosis and computer-aided detection can act as vital tools for clinicians in the long run.
Collapse
Affiliation(s)
| | | | - Rabia Abid
- Liaquat College of Medicine and Dentistry
| | - Rasish Subedi
- Universal College of Medical Sciences, Siddharthanagar, Nepal
| | | | | | | | - Aqsa I. Bhurgri
- Shaheed Muhtarma Benazir Bhutto Medical University, Larkana, Pakistan
| | | | - Sarwan Kumar
- Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh
- Wayne State University, Michigan, USA
| |
Collapse
|
4
|
Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
Collapse
Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
| |
Collapse
|
5
|
Du W, Rao N, Yong J, Adjei PE, Hu X, Wang X, Gan T, Zhu L, Zeng B, Liu M, Xu Y. Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107397. [PMID: 36753915 DOI: 10.1016/j.cmpb.2023.107397] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 12/15/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances between EGC lesions and healthy background mucosa. Hence, this study proposed an automatic segmentation framework: co-spatial attention and channel attention based triple-branch ResUnet (CSA-CA-TB-ResUnet) to achieve accurate segmentation of EGC lesions for aiding clinical diagnosis and treatment. METHODS The input gastroscopic image sequences of the triple-branch segmentation network CSA-CA-TB-ResUnet is firstly generated by the designed multi-branch input preprocessing (MBIP) module in order to fully utilize massive correlation information among multiple gastroscopic images of the same a lesion. Then, the proposed CSA-CA-TB-ResUnet performs the segmentation of EGC lesion, in which the co-spatial attention (CSA) mechanism is designed to activate the spatial location of EGC lesions by leveraging on the correlations among multiple gastroscopic images of the same EGC lesion, and the channel attention (CA) mechanism is introduced to extract subtle discriminative features of EGC lesions by capturing the interdependencies between channel features. Finally, two gastroscopic images datasets from different digestive endoscopic centers in the southwest and northeast regions of China respectively were collected to validate the performances of proposed segmentation method. RESULTS The correlation information among gastroscopic images was confirmed to be able to improve the accuracy of EGC segmentation. On another unseen dataset, our EGC segmentation method achieves Jaccard similarity index (JSI) of 84.54% (95% confidence interval (CI), 83.49%-85.56%), threshold Jaccard index (TJI) of 81.73% (95% CI, 79.70%-83.61%), Dice similarity coefficient (DSC) of 91.08% (95% CI, 90.40%-91.76%) and pixel-wise accuracy (PA) of 91.18% (95% CI, 90.43%-91.87%), which is superior to other state-of-the-art methods. Even on the challenging small lesions, the segmentation results of our CSA-CA-TB-ResUnet-based method are consistently and significantly better than other state-of-the-art methods. We also compared the segmentation result of our model with the diagnostic accuracy with junior/senior expert. The comparison results indicated that our model performed better than the junior expert. CONCLUSIONS This study proposed a novel CSA-CA-TB-ResUnet-based EGC segmentation method and it has a potential for real-time application in improving EGC clinical diagnosis and minimally invasive surgery.
Collapse
Affiliation(s)
- Wenju Du
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Nini Rao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Jiahao Yong
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Prince Ebenezer Adjei
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiaoming Hu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiaotong Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China.
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China
| | - Bing Zeng
- School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu 610054, China
| | - Mengyuan Liu
- The First Hospital of China Medical University, China Medical University, Shenyang 110001, China
| | - Yongxue Xu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| |
Collapse
|
6
|
Zhang G, Song J, Feng Z, Zhao W, Huang P, Liu L, Zhang Y, Su X, Wu Y, Cao Y, Li Z, Jie Z. Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace. Front Oncol 2023; 12:1075974. [PMID: 36686778 PMCID: PMC9846739 DOI: 10.3389/fonc.2022.1075974] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023] Open
Abstract
Objective This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. Methods The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. Results A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. Conclusions Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.
Collapse
Affiliation(s)
- Guoyang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jingjing Song
- Jiangxi Med College of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wentao Zhao
- The Third Clinical Department of China Medical University, Shenyang, China
| | - Pan Huang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Liu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xufeng Su
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yukang Wu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Cao
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
| | - Zhigang Jie
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
| |
Collapse
|
7
|
Wang JG. Application and future perspectives of gastric cancer technology based on artificial intelligence. Tzu Chi Med J 2023. [DOI: 10.4103/tcmj.tcmj_305_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
|
8
|
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: 4.5] [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
|
9
|
Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
Collapse
Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| |
Collapse
|
10
|
Chen PC, Lu YR, Kang YN, Chang CC. The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study. J Med Internet Res 2022; 24:e27694. [PMID: 35576561 PMCID: PMC9152716 DOI: 10.2196/27694] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 10/23/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. OBJECTIVE The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. METHODS We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. RESULTS We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. CONCLUSIONS For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. TRIAL REGISTRATION PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.
Collapse
Affiliation(s)
- Pei-Chin Chen
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yun-Ru Lu
- Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yi-No Kang
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan.,Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.,Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| |
Collapse
|
11
|
Niikura R, Aoki T, Shichijo S, Yamada A, Kawahara T, Kato Y, Hirata Y, Hayakawa Y, Suzuki N, Ochi M, Hirasawa T, Tada T, Kawai T, Koike K. Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy 2022; 54:780-784. [PMID: 34607377 PMCID: PMC9329064 DOI: 10.1055/a-1660-6500] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
AIMS To compare endoscopy gastric cancer images diagnosis rate between artificial intelligence (AI) and expert endoscopists. PATIENTS AND METHODS We used the retrospective data of 500 patients, including 100 with gastric cancer, matched 1:1 to diagnosis by AI or expert endoscopists. We retrospectively evaluated the noninferiority (prespecified margin 5 %) of the per-patient rate of gastric cancer diagnosis by AI and compared the per-image rate of gastric cancer diagnosis. RESULTS Gastric cancer was diagnosed in 49 of 49 patients (100 %) in the AI group and 48 of 51 patients (94.12 %) in the expert endoscopist group (difference 5.88, 95 % confidence interval: -0.58 to 12.3). The per-image rate of gastric cancer diagnosis was higher in the AI group (99.87 %, 747 /748 images) than in the expert endoscopist group (88.17 %, 693 /786 images) (difference 11.7 %). CONCLUSIONS Noninferiority of the rate of gastric cancer diagnosis by AI was demonstrated but superiority was not demonstrated.
Collapse
Affiliation(s)
- Ryota Niikura
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan,Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | - Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Satoki Shichijo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Takuya Kawahara
- Clinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, Japan
| | | | - Yoshihiro Hirata
- Division of Advanced Genome Medicine, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoku Hayakawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Nobumi Suzuki
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Masanori Ochi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital Ariake, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan,Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Takashi Kawai
- Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Japan
| |
Collapse
|
12
|
Tang S, Yu X, Cheang CF, Hu Z, Fang T, Choi IC, Yu HH. Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22041492. [PMID: 35214396 PMCID: PMC8876234 DOI: 10.3390/s22041492] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/26/2022] [Accepted: 02/08/2022] [Indexed: 05/03/2023]
Abstract
It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.
Collapse
Affiliation(s)
- Suigu Tang
- Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; (S.T.); (X.Y.); (Z.H.); (T.F.)
| | - Xiaoyuan Yu
- Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; (S.T.); (X.Y.); (Z.H.); (T.F.)
| | - Chak-Fong Cheang
- Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; (S.T.); (X.Y.); (Z.H.); (T.F.)
- Correspondence:
| | - Zeming Hu
- Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; (S.T.); (X.Y.); (Z.H.); (T.F.)
| | - Tong Fang
- Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China; (S.T.); (X.Y.); (Z.H.); (T.F.)
| | - I-Cheong Choi
- Kiang Wu Hospital, Macau 999078, China; (I.-C.C.); (H.-H.Y.)
| | - Hon-Ho Yu
- Kiang Wu Hospital, Macau 999078, China; (I.-C.C.); (H.-H.Y.)
| |
Collapse
|
13
|
Chen L, Ge C, Feng X, Fu H, Wang S, Zhu J, Linghu E, Zheng X. Identification of Combinations of Plasma lncRNAs and mRNAs as Potential Biomarkers for Precursor Lesions and Early Gastric Cancer. JOURNAL OF ONCOLOGY 2022; 2022:1458320. [PMID: 35186077 PMCID: PMC8856804 DOI: 10.1155/2022/1458320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/18/2022]
Abstract
Patients with gastric cancer (GC) are usually first diagnosed at an advanced stage due to the absence of obvious symptoms at an early GC (EGC) stage. Therefore, it is necessary to identify an effective screening method to detect precursor lesions of GC (PLGC) and EGC to increase the 5-year survival rate of patients. Cell-free RNA, as a biomarker, has shown potential in early diagnosis, personalised treatment, and prognosis of cancer. In this study, six RNAs (CEBPA-AS1, INHBA-AS1, AK001058, UCA1, PPBP, and RGS18) were analysed via real-time quantitative polymerase chain reaction (RT-qPCR) using the plasma of patients with EGC and PLGC to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic accuracy. Among the six RNAs, four lncRNAs (CEBPA-AS1, INHBA-AS1, AK001058, and UCA1) were upregulated and two mRNAs (PPBP and RGS18) were downregulated in the plasma of patients with PLGC and EGC. According to the findings of the ROC analysis, the four-RNA combination of INHBA-AS1, AK001058, UCA1, and RGS18 had the highest area under the curve (AUC) value for determining risk of GC in patients with PLGC and the six-RNA combination including CEBPA-AS1, INHBA-AS1, AK001058, UCA1, PPBP, and RGS18 had the highest AUC value for determining the risk of GC in patients with EGC. The results suggest the potential usefulness of noninvasive biomarkers for the molecular diagnosis of GC at earlier stages.
Collapse
Affiliation(s)
- Lu Chen
- 1Department of Experimental Hematology and Biochemistry, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Changhui Ge
- 1Department of Experimental Hematology and Biochemistry, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiuxue Feng
- 2Division of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 1000853, China
| | - Hanjiang Fu
- 1Department of Experimental Hematology and Biochemistry, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Shasha Wang
- 2Division of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 1000853, China
| | - Jie Zhu
- 1Department of Experimental Hematology and Biochemistry, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Enqiang Linghu
- 2Division of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing 1000853, China
| | - Xiaofei Zheng
- 1Department of Experimental Hematology and Biochemistry, Beijing Key Laboratory for Radiobiology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| |
Collapse
|
14
|
Yu X, Tang S, Cheang CF, Yu HH, Choi IC. Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention. SENSORS 2021; 22:s22010283. [PMID: 35009825 PMCID: PMC8749873 DOI: 10.3390/s22010283] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 12/12/2022]
Abstract
The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.
Collapse
Affiliation(s)
- Xiaoyuan Yu
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau; (X.Y.); (S.T.)
| | - Suigu Tang
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau; (X.Y.); (S.T.)
| | - Chak Fong Cheang
- Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau; (X.Y.); (S.T.)
- Correspondence: (C.F.C.); (H.H.Y.)
| | - Hon Ho Yu
- Kiang Wu Hospital, Santo António, Macau;
- Correspondence: (C.F.C.); (H.H.Y.)
| | | |
Collapse
|
15
|
Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29:1389-1395. [DOI: 10.11569/wcjd.v29.i24.1389] [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
In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.
Collapse
Affiliation(s)
- Qing Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
| | - Bing-Rong Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
| |
Collapse
|
16
|
Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis. J Med Internet Res 2021; 23:e33267. [PMID: 34904949 PMCID: PMC8715364 DOI: 10.2196/33267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/10/2021] [Accepted: 10/13/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. OBJECTIVE To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CONCLUSIONS CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.
Collapse
Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Republic of Korea.,Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Republic of Korea
| |
Collapse
|
17
|
Bang CS. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. THE KOREAN JOURNAL OF HELICOBACTER AND UPPER GASTROINTESTINAL RESEARCH 2021. [DOI: 10.7704/kjhugr.2021.0030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.
Collapse
|
18
|
Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311136] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.
Collapse
|
19
|
Du W, Rao N, Yong J, Wang Y, Hu D, Gan T, Zhu L, Zeng B. Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning. J Med Syst 2021; 46:4. [PMID: 34807297 DOI: 10.1007/s10916-021-01782-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/11/2021] [Indexed: 02/05/2023]
Abstract
The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.
Collapse
Affiliation(s)
- Wenju Du
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Nini Rao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Jiahao Yong
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yingchun Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dingcan Hu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China.
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China
| | - Bing Zeng
- School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu, 610054, China
| |
Collapse
|
20
|
Du W, Rao N, Yong J, Wang Y, Hu D, Gan T, Zhu L. Automatic Early Gastric Cancer Segmentation in Gastroscopic Images Based on ResUnet. 2021 8TH INTERNATIONAL CONFERENCE ON BIOMEDICAL AND BIOINFORMATICS ENGINEERING 2021. [DOI: 10.1145/3502871.3502874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Wenju Du
- School of Life Science and Technology, University of Electronic Science and Technology of China, China and Center for Informational Biology, University of Electronic Science and Technology of China, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, China and Center for Informational Biology, University of Electronic Science and Technology of China, China
| | - Jiahao Yong
- School of Life Science and Technology, University of Electronic Science and Technology of China, China and Center for Informational Biology, University of Electronic Science and Technology of China, China
| | - Yingchun Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, China and Center for Informational Biology, University of Electronic Science and Technology of China, China
| | - Dingcan Hu
- School of Life Science and Technology, University of Electronic Science and Technology of China, China and Center for Informational Biology, University of Electronic Science and Technology of China, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, China
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, China
| |
Collapse
|
21
|
Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:cancers13215494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Gastrointestinal cancers cause over 2.8 million deaths annually worldwide. Currently, the diagnosis of various gastrointestinal cancer mainly relies on manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. Artificial intelligence (AI) may be useful in screening, diagnosing, and treating various cancers by accurately analyzing diagnostic clinical images, identifying therapeutic targets, and processing large datasets. The use of AI in endoscopic procedures is a significant breakthrough in modern medicine. Although the diagnostic accuracy of AI systems has markedly increased, it still needs collaboration with physicians. In the near future, AI-assisted systems will become a vital tool for the management of these cancer patients. Abstract Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
Collapse
|
22
|
Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
Collapse
Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
| |
Collapse
|
23
|
Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
Collapse
Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
| |
Collapse
|
24
|
Du W, Rao N, Dong C, Wang Y, Hu D, Zhu L, Zeng B, Gan T. Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network. BIOMEDICAL OPTICS EXPRESS 2021; 12:3066-3081. [PMID: 34221645 DOI: 10.1364/boe.420935] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/07/2021] [Accepted: 04/25/2021] [Indexed: 02/05/2023]
Abstract
The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.
Collapse
Affiliation(s)
- Wenju Du
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Nini Rao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,
| | - Changlong Dong
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yingchun Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dingcan Hu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China
| | - Bing Zeng
- School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu 610054, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China.,
| |
Collapse
|
25
|
Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy. Gastrointest Endosc 2021; 93:1006-1015.e13. [PMID: 33290771 DOI: 10.1016/j.gie.2020.11.025] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/20/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Diagnosis of esophageal cancer or precursor lesions by endoscopic imaging depends on endoscopist expertise and is inevitably subject to interobserver variability. Studies on computer-aided diagnosis (CAD) using deep learning or machine learning are on the increase. However, studies with small sample sizes are limited by inadequate statistical strength. Here, we used a meta-analysis to evaluate the diagnostic test accuracy (DTA) of CAD algorithms of esophageal cancers or neoplasms using endoscopic images. METHODS Core databases were searched for studies based on endoscopic imaging using CAD algorithms for the diagnosis of esophageal cancer or neoplasms and presenting data on diagnostic performance, and a systematic review and DTA meta-analysis were performed. RESULTS Overall, 21 and 19 studies were included in the systematic review and DTA meta-analysis, respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer or neoplasms in the image-based analysis were 0.97 (95% confidence interval [CI], 0.95-0.99), 0.94 (95% CI, 0.89-0.96), 0.88 (95% CI, 0.76-0.94), and 108 (95% CI, 43-273), respectively. Meta-regression showed no heterogeneity, and no publication bias was detected. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD algorithms for the diagnosis of esophageal cancer invasion depth were 0.96 (95% CI, 0.86-0.99), 0.90 (95% CI, 0.88-0.92), 0.88 (95% CI, 0.83-0.91), and 138 (95% CI, 12-1569), respectively. CONCLUSIONS CAD algorithms showed high accuracy for the automatic endoscopic diagnosis of esophageal cancer and neoplasms. The limitation of a lack in performance in external validation and clinical applications should be overcome.
Collapse
Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea; Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Korea; Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea; Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea; Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Korea; Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea; Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Korea
| |
Collapse
|
26
|
Liu Y. Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step? World J Gastroenterol 2021; 27:1392-1405. [PMID: 33911463 PMCID: PMC8047537 DOI: 10.3748/wjg.v27.i14.1392] [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: 01/10/2021] [Revised: 02/23/2021] [Accepted: 03/13/2021] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. This paper reviews the application of AI in the field of endoscopic detection of early EC, including squamous cell carcinoma and adenocarcinoma, and describes the relevant progress. Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images, AI-assisted real-time detection based on live-stream video may be the next step.
Collapse
Affiliation(s)
- Yong Liu
- Department of Thoracic Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430011, Hubei Province, China
| |
Collapse
|
27
|
Jani KK, Srivastava R. A Survey on Medical Image Analysis in Capsule Endoscopy. Curr Med Imaging 2020; 15:622-636. [PMID: 32008510 DOI: 10.2174/1573405614666181102152434] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 10/14/2018] [Accepted: 10/22/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems. METHODS Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected. RESULTS This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above. CONCLUSIONS The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.
Collapse
Affiliation(s)
- Kuntesh Ketan Jani
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi, Uttar Pradesh, India
| | - Rajeev Srivastava
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi, Uttar Pradesh, India
| |
Collapse
|
28
|
Niu PH, Zhao LL, Wu HL, Zhao DB, Chen YT. Artificial intelligence in gastric cancer: Application and future perspectives. World J Gastroenterol 2020; 26:5408-5419. [PMID: 33024393 PMCID: PMC7520602 DOI: 10.3748/wjg.v26.i36.5408] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/02/2020] [Accepted: 08/29/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.
Collapse
Affiliation(s)
- Peng-Hui Niu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lu-Lu Zhao
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Hong-Liang Wu
- Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Dong-Bing Zhao
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ying-Tai Chen
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| |
Collapse
|
29
|
Zhang YH, Guo LJ, Yuan XL, Hu B. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020; 26:5256-5271. [PMID: 32994686 PMCID: PMC7504247 DOI: 10.3748/wjg.v26.i35.5256] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/29/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.
Collapse
Affiliation(s)
- Yu-Hang Zhang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lin-Jie Guo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| |
Collapse
|
30
|
Hohmann M, Albrecht H, Lengenfelder B, Klämpfl F, Schmidt M. Factors influencing the accuracy for tissue classification in multi spectral in-vivo endoscopy for the upper gastro-internal tract. Sci Rep 2020; 10:3546. [PMID: 32103066 PMCID: PMC7044217 DOI: 10.1038/s41598-020-60389-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 02/10/2020] [Indexed: 11/12/2022] Open
Abstract
Hyper spectral imaging is a possible way for disease detection. However, for carcinoma detection most of the results are ex-vivo. However, in-vivo results of endoscopic studies still show fairly low accuracies in contrast to the good results of many ex-vivo studies. To overcome this problem and to provide a reasonable explanation, Monte-Carlo simulations of photon trajectories are proposed as a tool to generate multi spectral images including inter patient variations to simulate 40 patients. Furthermore, these simulations have the huge advantage that the position of the carcinoma is known. Due to this, the effect of mislabelled data can be studied. As shown in this study, a percentage of 30–35% of mislabelled data might lead to significant decrease of the accuracy from around 90% to around 70–75%. Therefore, the main focus of hyper spectral imaging has to be the exact characterization of the training data in the future.
Collapse
Affiliation(s)
- Martin Hohmann
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany. .,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, 91052, Erlangen, Germany.
| | - Heinz Albrecht
- Department of Internal Medicine II, Kliniken des Landkreises Neumarkt i.d.OPf., Nürnberger Str. 12, 92318, Neumarkt, Germany
| | - Benjamin Lengenfelder
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany.,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, 91052, Erlangen, Germany
| | - Florian Klämpfl
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany.,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, 91052, Erlangen, Germany
| | - Michael Schmidt
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany.,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, 91052, Erlangen, Germany
| |
Collapse
|
31
|
Mujtaba S, Chawla S, Massaad JF. Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives. J Clin Med 2020; 9:jcm9020402. [PMID: 32024301 PMCID: PMC7074258 DOI: 10.3390/jcm9020402] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 01/30/2023] Open
Abstract
Non-variceal gastrointestinal bleeding (GIB) is a significant cause of mortality and morbidity worldwide which is encountered in the ambulatory and hospital settings. Hemorrhage form the gastrointestinal (GI) tract is categorized as upper GIB, small bowel bleeding (also formerly referred to as obscure GIB) or lower GIB. Although the etiologies of GIB are variable, a strong, consistent risk factor is use of non-steroidal anti-inflammatory drugs. Advances in the endoscopic diagnosis and treatment of GIB have led to improved outcomes. We present an updated review of the current practices regarding the diagnosis and management of non-variceal GIB, and possible future directions.
Collapse
|
32
|
Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
Collapse
Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
| |
Collapse
|
33
|
Cummins G, Cox BF, Ciuti G, Anbarasan T, Desmulliez MPY, Cochran S, Steele R, Plevris JN, Koulaouzidis A. Gastrointestinal diagnosis using non-white light imaging capsule endoscopy. Nat Rev Gastroenterol Hepatol 2019; 16:429-447. [PMID: 30988520 DOI: 10.1038/s41575-019-0140-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.
Collapse
Affiliation(s)
- Gerard Cummins
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
| | | | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Marc P Y Desmulliez
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Sandy Cochran
- School of Engineering, University of Glasgow, Glasgow, UK
| | - Robert Steele
- School of Medicine, University of Dundee, Dundee, UK
| | - John N Plevris
- Centre for Liver and Digestive Disorders, The Royal Infirmary of Edinburgh, Edinburgh, UK
| | | |
Collapse
|
34
|
Pogorelov K, Suman S, Azmadi Hussin F, Saeed Malik A, Ostroukhova O, Riegler M, Halvorsen P, Hooi Ho S, Goh KL. Bleeding detection in wireless capsule endoscopy videos - Color versus texture features. J Appl Clin Med Phys 2019; 20:141-154. [PMID: 31251460 PMCID: PMC6698770 DOI: 10.1002/acm2.12662] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Revised: 10/15/2018] [Accepted: 05/26/2019] [Indexed: 12/22/2022] Open
Abstract
Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient’s video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine‐learning‐based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel‐level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state‐of‐the‐art approaches. In this research, we have conducted a broad comparison of a number of different state‐of‐the‐art features and classification methods that allows building an efficient and flexible WCE video processing system.
Collapse
Affiliation(s)
- Konstantin Pogorelov
- Department of Communication Systems, Simula Research Laboratory, Fornebu, Norway
| | - Shipra Suman
- Center of Intelligent Signal & Imaging Research Group, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia
| | - Fawnizu Azmadi Hussin
- Center of Intelligent Signal & Imaging Research Group, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia
| | - Aamir Saeed Malik
- Center of Intelligent Signal & Imaging Research Group, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia
| | - Olga Ostroukhova
- Research Institute of Multiprocessor Computation Systems, n.a. A.V. Kalyaev, Russia
| | - Michael Riegler
- Department of Communication Systems, Simula Research Laboratory, Fornebu, Norway
| | - Pål Halvorsen
- Department of Communication Systems, Simula Research Laboratory, Fornebu, Norway
| | - Shiaw Hooi Ho
- Department of Medicine, University of Malaya Medical Center, Kuala Lumpur, Malaysia
| | - Khean-Lee Goh
- Department of Medicine, University of Malaya Medical Center, Kuala Lumpur, Malaysia
| |
Collapse
|
35
|
Hohmann M, Albrecht H, Mudter J, Nagulin KY, Klämpfl F, Schmidt M. Spectral Spatial Variation. Sci Rep 2019; 9:7512. [PMID: 31101855 PMCID: PMC6525256 DOI: 10.1038/s41598-019-43971-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 04/27/2019] [Indexed: 01/10/2023] Open
Abstract
Automatic carcinoma detection from hyper/multi spectral images is of essential importance due to the fact that these images cannot be presented directly to the clinician. However, standard approaches for carcinoma detection use hundreds or even thousands of features. This would cost a high amount of RAM (random access memory) for a pixel wise analysis and would slow down the classification or make it even impossible on standard PCs. To overcome this, strong features are required. We propose that the spectral-spatial-variation (SSV) is one of these strong features. SSV is the residuum of the three dimensional hyper spectral data cube minus its approximation with a fitting in a small volume of the 3D image. By using it, the classification results of carcinoma detection in the stomach with multi spectral imaging will be increase significantly compared to not using the SSV. In some cases, the AUC can be even as high as by the usage of 72 spatial features.
Collapse
Affiliation(s)
- Martin Hohmann
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany. .,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordon-Straße 6, 91052, Erlangen, Germany.
| | - Heinz Albrecht
- Kliniken des Landkreises Neumarkt i.d.OPf., Department of Internal Medicine II, Nürnberger Str. 12, 92318, Neumarkt, Germany
| | - Jonas Mudter
- Sana Clinic Ostholstein, Department of Gastroenterology, Hospitalstraße 22, 23701, Eutin, Germany
| | - Konstantin Yu Nagulin
- Kazan National Research Technical University named after AN Tupolev - KAI, Karl Marx Street 10, 420111, Kazan, Russia
| | - Florian Klämpfl
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany.,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordon-Straße 6, 91052, Erlangen, Germany
| | - Michael Schmidt
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Konrad-Zuse-Straße 3/5, 91052, Erlangen, Germany.,Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordon-Straße 6, 91052, Erlangen, Germany
| |
Collapse
|
36
|
Saikia AR, Bora K, Mahanta LB, Das AK. Comparative assessment of CNN architectures for classification of breast FNAC images. Tissue Cell 2019; 57:8-14. [PMID: 30947968 DOI: 10.1016/j.tice.2019.02.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/30/2019] [Accepted: 02/02/2019] [Indexed: 01/27/2023]
Abstract
Fine needle aspiration cytology (FNAC) entails using a narrow gauge (25-22 G) needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. FNAC is commonly used for diagnosis of breast cancer, with traditional practice being based on the subjective visual assessment of the breast cytopathology cell samples under a microscope to evaluate the state of various cytological features. Therefore, there are many challenges in maintaining consistency and reproducibility of findings. However, the advent of digital imaging and computational aid in diagnosis can improve the diagnostic accuracy and reduce the effective workload of pathologists. This paper presents a comparison of various deep convolutional neural network (CNN) based fine-tuned transfer learned classification approach for the diagnosis of the cell samples. The proposed approach has been tested using VGG16, VGG19, ResNet-50 and GoogLeNet-V3 (aka Inception V3) architectures of CNN on an image dataset of 212 images (99 benign and 113 malignant), later augmented and cleansed to 2120 images (990 benign and 1130 malignant), where the network was trained using images of 80% cell samples and tested on the rest. This paper presents a comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
Collapse
Affiliation(s)
- Amartya Ranjan Saikia
- The Department of Computer Science and Engineering, Assam Engineering College, Guwahati 781013, Assam, India.
| | - Kangkana Bora
- The Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India.
| | - Lipi B Mahanta
- The Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India
| | | |
Collapse
|
37
|
Diamantis DE, Iakovidis DK, Koulaouzidis A. Look-behind fully convolutional neural network for computer-aided endoscopy. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|
38
|
Liu DY, Jiang HX, Rao NN, Luo CS, Du WJ, Li ZW, Gan T. Computer Aided Annotation of Early Esophageal Cancer in Gastroscopic Images based on Deeplabv3+ Network. PROCEEDINGS OF THE 2019 4TH INTERNATIONAL CONFERENCE ON BIOMEDICAL SIGNAL AND IMAGE PROCESSING (ICBIP 2019) - ICBIP '19 2019. [DOI: 10.1145/3354031.3354046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Ding Yun Liu
- University of Electronic Science and Technology of China, China
| | - Hong Xiu Jiang
- University of Electronic Science and Technology of China, China
| | - Ni Ni Rao
- University of Electronic Science and Technology of China, China
| | - Cheng Si Luo
- University of Electronic Science and Technology of China, China
| | - Wen Ju Du
- University of Electronic Science and Technology of China, China
| | - Zheng Wen Li
- University of Electronic Science and Technology of China, China
| | - Tao Gan
- Digestive Endoscopic Center, West China Hospital, Sichuan University, China
| |
Collapse
|
39
|
Liu D, Rao N, Mei X, Jiang H, Li Q, Luo C, Li Q, Zeng C, Zeng B, Gan T. Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images. J Med Syst 2018; 42:237. [PMID: 30327890 DOI: 10.1007/s10916-018-1063-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 09/06/2018] [Indexed: 02/05/2023]
Abstract
Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required. However, automated annotation of EEC lesions using images is a challenging task owing to the fine-grained variability in the appearance of EEC lesions. This study modifies the traditional EEC annotation framework and utilizes visual salient information to develop a two saliency levels-based lesion annotation (TSL-BLA) for EEC annotations on gastroscopic images. Unlike existing methods, the proposed framework has a strong ability of constraining false positive outputs. What is more, TSL-BLA is also placed an additional emphasis on the annotation of small EEC lesions. A total of 871 gastroscopic images from 231 patients were used to validate TSL-BLA. 365 of those images contain 434 EEC lesions and 506 images do not contain any lesions. 101 small lesion regions are extracted from the 434 lesions to further validate the performance of TSL-BLA. The experimental results show that the mean detection rate and Dice similarity coefficients of TSL-BLA were 97.24 and 75.15%, respectively. Compared with other state-of-the-art methods, TSL-BLA shows better performance. Moreover, it shows strong superiority when annotating small EEC lesions. It also produces fewer false positive outputs and has a fast running speed. Therefore, The proposed method has good application prospects in aiding clinical EEC diagnoses.
Collapse
Affiliation(s)
- Dingyun Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. .,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China. .,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xinming Mei
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.,Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China
| | - Hongxiu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Quanchi Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - ChengSi Luo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Chengshi Zeng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China
| | - Bing Zeng
- School of Communication and Information Engineering, University Electronic Science and Technology of China, Chengdu, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
40
|
Zhao R, Zhang R, Tang T, Feng X, Li J, Liu Y, Zhu R, Wang G, Li K, Zhou W, Yang Y, Wang Y, Ba Y, Zhang J, Liu Y, Zhou F. TriZ-a rotation-tolerant image feature and its application in endoscope-based disease diagnosis. Comput Biol Med 2018; 99:182-190. [PMID: 29936284 DOI: 10.1016/j.compbiomed.2018.06.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 05/30/2018] [Accepted: 06/08/2018] [Indexed: 12/11/2022]
Abstract
Endoscopy is becoming one of the widely-used technologies to screen the gastric diseases, and it heavily relies on the experiences of the clinical endoscopists. The location, shape, and size are the typical patterns for the endoscopists to make the diagnosis decisions. The contrasting texture patterns also suggest the potential lesions. This study designed a novel rotation-tolerant image feature, TriZ, and demonstrated the effectiveness on both the rotation invariance and the lesion detection of three gastric lesion types, i.e., gastric polyp, gastric ulcer, and gastritis. TriZ achieved 87.0% in the four-class classification problem of the three gastric lesion types and the healthy controls, averaged over the twenty random runs of 10-fold cross-validations. Due to that biomedical imaging technologies may capture the lesion sites from different angles, the symmetric image feature extraction algorithm TriZ may facilitate the biomedical image based disease diagnosis modeling. Compared with the 378,434 features of the HOG algorithm, TriZ achieved a better accuracy using only 126 image features.
Collapse
Affiliation(s)
- Ruixue Zhao
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Ruochi Zhang
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Tongyu Tang
- First Hospital, Jilin University, Changchun, Jilin, 130012, China
| | - Xin Feng
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Jialiang Li
- College of Software, Jilin University, Changchun, Jilin, 130012, China
| | - Yue Liu
- College of Communication Engineering, Jilin University, Changchun, Jilin, 130012, China
| | - Renxiang Zhu
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Guangze Wang
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Kangning Li
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Wenyang Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Yunfei Yang
- College of Software, Jilin University, Changchun, Jilin, 130012, China
| | - Yuzhao Wang
- College of Software, Jilin University, Changchun, Jilin, 130012, China
| | - Yuanjie Ba
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Jiaojiao Zhang
- College of Software, Jilin University, Changchun, Jilin, 130012, China
| | - Yang Liu
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
| |
Collapse
|
41
|
A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information. Int J Colorectal Dis 2018. [PMID: 29520455 DOI: 10.1007/s00384-018-2980-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE The colonoscopy adenoma detection rate depends largely on physician experience and skill, and overlooked colorectal adenomas could develop into cancer. This study assessed a system that detects polyps and summarizes meaningful information from colonoscopy videos. METHODS One hundred thirteen consecutive patients had colonoscopy videos prospectively recorded at the Seoul National University Hospital. Informative video frames were extracted using a MATLAB support vector machine (SVM) model and classified as bleeding, polypectomy, tool, residue, thin wrinkle, folded wrinkle, or common. Thin wrinkle, folded wrinkle, and common frames were reanalyzed using SVM for polyp detection. The SVM model was applied hierarchically for effective classification and optimization of the SVM. RESULTS The mean classification accuracy according to type was over 93%; sensitivity was over 87%. The mean sensitivity for polyp detection was 82.1%, and the positive predicted value (PPV) was 39.3%. Polyps detected using the system were larger (6.3 ± 6.4 vs. 4.9 ± 2.5 mm; P = 0.003) with a more pedunculated morphology (Yamada type III, 10.2 vs. 0%; P < 0.001; Yamada type IV, 2.8 vs. 0%; P < 0.001) than polyps missed by the system. There were no statistically significant differences in polyp distribution or histology between the groups. Informative frames and suspected polyps were presented on a timeline. This summary was evaluated using the system usability scale questionnaire; 89.3% of participants expressed positive opinions. CONCLUSIONS We developed and verified a system to extract meaningful information from colonoscopy videos. Although further improvement and validation of the system is needed, the proposed system is useful for physicians and patients.
Collapse
|
42
|
Liu H, Han Y, Li J, Qin M, Fu Q, Wang C, Liu Z. 18F-Alanine Derivative Serves as an ASCT2 Marker for Cancer Imaging. Mol Pharm 2018; 15:947-954. [DOI: 10.1021/acs.molpharmaceut.7b00884] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Hui Liu
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yuxiang Han
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jiyuan Li
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Ming Qin
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Qunfeng Fu
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Chunhong Wang
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Zhibo Liu
- Radiochemistry and Radiation Chemistry Key Laboratory of Fundamental Science, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| |
Collapse
|
43
|
Ke D, Li H, Zhang Y, An Y, Fu H, Fang X, Zheng X. The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer. Oncotarget 2017; 8:21516-21525. [PMID: 28423525 PMCID: PMC5400602 DOI: 10.18632/oncotarget.15628] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 02/14/2017] [Indexed: 12/13/2022] Open
Abstract
Background Suitable diagnostic markers for cancers are urgently required in clinical practice. Long non-coding RNAs, which have been reported in many cancer types, are a potential new class of biomarkers for tumor diagnosis. Results Five lncRNAs, including AK001058, INHBA-AS1, MIR4435-2HG, UCA1 and CEBPA-AS1 were validated to be increased in gastric cancer tissues. Furthermore, we found that plasma level of these five lncRNAs were significantly higher in gastric cancer patients compared with normal controls. By receiver operating characteristic analysis, we found that the combination of plasma lncRNAs with the area under the curve up to 0.921, including AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, is a better indicator of gastric cancer than their individual levels or other lncRNA combinations. Simultaneously, we found that the expression levels of a series of MIR4435-2HG fragments are different in gastric cancer plasma samples, but most of them higher than that in healthy control plasma samples. Materials and Methods LncRNA gene expression profiles were analyzed in two pairs of human gastric cancer and adjacent non-tumor tissues by microarray analysis. Nine gastric cancer-associated lncRNAs were selected and assessed by quantitative real-time polymerase chain reaction in gastric tissues, and 5 of them were further analyzed in gastric cancer patients’ plasma. Conclusions Our results demonstrate that certain lncRNAs, such as AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with gastric cancer. These findings indicate that the combination of these four lncRNAs might be used as diagnostic or prognostic markers for gastric cancer patients.
Collapse
Affiliation(s)
- Dong Ke
- General Surgery, The Second Hospital of Jilin University, Changchun, 130041, China.,Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China.,Gastrointestinal Colorectal and Anal Surgery, The China-Japan Union Hospital of Jilin University, Changchun, 130033, China
| | - Hanwei Li
- Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China
| | - Yi Zhang
- Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China.,Gastrointestinal Colorectal and Anal Surgery, The China-Japan Union Hospital of Jilin University, Changchun, 130033, China
| | - Yinghong An
- Clinical Laboratory Center, Chinese PLA Air Force General Hospital, Beijing, 100142, China
| | - Hanjiang Fu
- Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China
| | - Xuedong Fang
- General Surgery, The Second Hospital of Jilin University, Changchun, 130041, China.,Gastrointestinal Colorectal and Anal Surgery, The China-Japan Union Hospital of Jilin University, Changchun, 130033, China
| | - Xiaofei Zheng
- Beijing Key Laboratory for Radiobiology, Beijing institute of Radiation Medicine, Beijing, 100850, China
| |
Collapse
|
44
|
Sun JG, Qi J, Yang B, Gao Y, Huang JJ, Zhao C. The clinical characteristics and treatment of intestinal hamartomas. Lasers Med Sci 2016; 31:1761-1766. [PMID: 27539463 DOI: 10.1007/s10103-016-2046-0] [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: 04/12/2016] [Accepted: 08/05/2016] [Indexed: 10/21/2022]
Abstract
The study aims to investigate the intestinal (small and large intestine) clinical features and treatment of hamartoma. Nowadays, with the rapid development of new technologies, digestive system endoscopy has been proven to be an effective device for treatment, rather than just a diagnostic tool. Such development plays a revolutionary role in diagnosis and treatment for digestive diseases. And endoscopic treatment was used in this study (LED light source, wavelength 580 ∼ 595 nm, power 200 W). A retrospective analysis of 20 cases of intestinal hamartomas performed from January 2012 to January 2016 to summarize its clinical characteristics and follow-up study on the therapeutic effect of the patients. There were 8 cases for endoscopic operation, and 12 cases for surgical operation. Comparison of tumor size between endoscopic and surgical estimated by using Wilcoxon rank sum test for tumor length (Z = -3.134, p = 0.001), and for tumor diameter (Z = -2.920, p = 0.002). The results of this study showed that intestinal hamartomas and gender have no significant relationship. The incidence of the disease is concentrated under 60 years, the incidence of the small intestine is significantly higher than that of the large intestine, and the rate of misdiagnosis is high. Endoscopic and surgical treatment are the main treatment, the prognosis is good, and after the radical resection, the recurrence was less.
Collapse
Affiliation(s)
- Jian-Gang Sun
- Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China
| | - Jingwen Qi
- Pathology, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China
| | - Bo Yang
- Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China.
| | - Yongshun Gao
- Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China
| | - Jing-Jing Huang
- Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China
| | - Chengbin Zhao
- Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Erqi area Jianshe East Road No.1, Henan, China
| |
Collapse
|