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Alshardan A, Saeed MK, Alotaibi SD, Alashjaee AM, Salih N, Marzouk R. Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection. Health Inf Sci Syst 2024; 12:35. [PMID: 38764569 PMCID: PMC11096294 DOI: 10.1007/s13755-024-00294-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/29/2024] [Indexed: 05/21/2024] Open
Abstract
Gastrointestinal (GI) cancer detection includes the detection of cancerous or potentially cancerous lesions within the GI tract. Earlier diagnosis is critical for increasing the success of treatment and improving patient outcomes. Medical imaging plays a major role in diagnosing and detecting GI cancer. CT scans, endoscopy, MRI, ultrasound, and positron emission tomography (PET) scans can help detect lesions, abnormal masses, and changes in tissue structure. Artificial intelligence (AI) and machine learning (ML) methods are being gradually applied to medical imaging for cancer diagnosis. ML algorithms, including deep learning methodologies like convolutional neural network (CNN), are applied frequently for cancer diagnosis. These models learn features and patterns from labelled datasets to discriminate between normal and abnormal areas in medical images. This article presents a new Harbor Seal Whiskers Optimization Algorithm with Deep Learning based Medical Imaging Analysis for Gastrointestinal Cancer Detection (HSWOA-DLGCD) technique. The goal of the HSWOA-DLGCD algorithm is to explore the GI images for the cancer diagnosis. In order to accomplish this, the HSWOA-DLGCD system applies bilateral filtering (BF) approach for the removal of noise. In addition, the HSWOA-DLGCD technique makes use of HSWOA with Xception model for feature extraction. For cancer recognition, the HSWOA-DLGCD technique applies extreme gradient boosting (XGBoost) model. Finally, the parameters compared with the XGBoost system can be selected by moth flame optimization (MFO) system. The experimental results of the HSWOA-DLGCD technique could be verified on the Kvasir database. The simulation outcome demonstrated a best possible solution of the HSWOA-DLGCD method than other recent methods.
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Affiliation(s)
- Amal Alshardan
- Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671 Riyadh, Saudi Arabia
| | - Muhammad Kashif Saeed
- Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
| | - Shoayee Dlaim Alotaibi
- Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia
| | - Abdullah M. Alashjaee
- Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, 91911 Rafha, Saudi Arabia
| | - Nahla Salih
- Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Radwa Marzouk
- Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671 Riyadh, Saudi Arabia
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Zha B, Cai A, Wang G. Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review. JMIR Med Inform 2024; 12:e56361. [PMID: 39093715 PMCID: PMC11296324 DOI: 10.2196/56361] [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: 01/15/2024] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 08/04/2024] Open
Abstract
Background Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. Objective This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy. Methods Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. Results A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. Conclusions AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness.
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Affiliation(s)
- Bowen Zha
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Angshu Cai
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guiqi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Matsubayashi CO, Cheng S, Hulchafo I, Zhang Y, Tada T, Buxbaum JL, Ochiai K. Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market. Dig Liver Dis 2024; 56:1156-1163. [PMID: 38763796 DOI: 10.1016/j.dld.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024]
Abstract
Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.
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Affiliation(s)
- Carolina Ogawa Matsubayashi
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo, Brasil; AI Medical Service Inc., Tokyo, Japan.
| | - Shuyan Cheng
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Ismael Hulchafo
- Columbia University School of Nursing, New York, NY 10032, USA
| | - Yifan Zhang
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan; Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - James L Buxbaum
- Division of Gastrointestinal and Liver Diseases, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Colon and Rectal Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Wu R, Qin K, Fang Y, Xu Y, Zhang H, Li W, Luo X, Han Z, Liu S, Li Q. Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis. J Gastrointest Surg 2024; 28:538-547. [PMID: 38583908 DOI: 10.1016/j.gassur.2023.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/16/2023] [Accepted: 12/30/2023] [Indexed: 04/09/2024]
Abstract
BACKGROUND With the development of endoscopic technology, endoscopic submucosal dissection (ESD) has been widely used in the treatment of gastrointestinal tumors. It is necessary to evaluate the depth of tumor invasion before the application of ESD. The convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist in the classification of the depth of invasion in endoscopic images. This meta-analysis aimed to evaluate the performance of CNN in determining the depth of invasion of gastrointestinal tumors. METHODS A search on PubMed, Web of Science, and SinoMed was performed to collect the original publications about the use of CNN in determining the depth of invasion of gastrointestinal neoplasms. Pooled sensitivity and specificity were calculated using an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. RESULTS A total of 17 articles were included; the pooled sensitivity was 84% (95% CI, 0.81-0.88), specificity was 91% (95% CI, 0.85-0.94), and the area under the curve (AUC) was 0.93 (95% CI, 0.90-0.95). The performance of CNN was significantly better than that of endoscopists (AUC: 0.93 vs 0.83, respectively; P = .0005). CONCLUSION Our review revealed that CNN is one of the most effective methods of endoscopy to evaluate the depth of invasion of early gastrointestinal tumors, which has the potential to work as a remarkable tool for clinical endoscopists to make decisions on whether the lesion is feasible for endoscopic treatment.
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Affiliation(s)
- Ruo Wu
- Nanfang Hospital (The First School of Clinical Medicine), Southern Medical University, Guangzhou, Guangdong, China
| | - Kaiwen Qin
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuxin Fang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuyuan Xu
- Department of Hepatology Unit and Infectious Diseases, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Haonan Zhang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Wenhua Li
- Nanfang Hospital (The First School of Clinical Medicine), Southern Medical University, Guangzhou, Guangdong, China
| | - Xiaobei Luo
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Zelong Han
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Side Liu
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Pazhou Lab, Guangzhou, Guangdong, China
| | - Qingyuan Li
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Kumar V, Prabha C, Sharma P, Mittal N, Askar SS, Abouhawwash M. Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images. BMC Med Imaging 2024; 24:63. [PMID: 38500083 PMCID: PMC10946139 DOI: 10.1186/s12880-024-01241-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/07/2024] [Indexed: 03/20/2024] Open
Abstract
Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.
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Affiliation(s)
- Vinod Kumar
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India
| | - Chander Prabha
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Preeti Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Nitin Mittal
- Skill Faculty of Engineering and Technology, Shri Vishwakarma Skill University, Palwal, Haryana, India.
| | - S S Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
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Shi Y, Fan H, Li L, Hou Y, Qian F, Zhuang M, Miao B, Fei S. The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis. World J Surg Oncol 2024; 22:40. [PMID: 38297303 PMCID: PMC10832162 DOI: 10.1186/s12957-024-03321-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. METHODS We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. RESULTS Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). CONCLUSION ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
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Affiliation(s)
- Yiheng Shi
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Haohan Fan
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Li Li
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Yaqi Hou
- College of Nursing, Yangzhou University, Yangzhou, 225009, China
| | - Feifei Qian
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Mengting Zhuang
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Bei Miao
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Institute of Digestive Diseases, Xuzhou Medical University, 84 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
| | - Sujuan Fei
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China.
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Kim GH. Endoscopic submucosal dissection for early gastric cancer: It is time to consider the quality of its outcomes. World J Gastroenterol 2023; 29:5800-5803. [PMID: 38074917 PMCID: PMC10701311 DOI: 10.3748/wjg.v29.i43.5800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/25/2023] [Accepted: 11/09/2023] [Indexed: 11/20/2023] Open
Abstract
Endoscopic resection, particularly endoscopic submucosal dissection (ESD), is widely used as a standard treatment modality for early gastric cancer (EGC) when the risk of lymph node metastasis is negligible. Compared with surgical gastrectomy, ESD is a minimally invasive procedure with additional advantages, such as preservation of the entire stomach and maintenance of the patient's quality of life. However, not all patients achieve curative resection after ESD of EGC. Several patients require surgical gastrectomy after ESD to achieve a curative treatment. Additional surgery after ESD, owing to non-curative resection, places considerable emotional and financial burdens on patients. Recently, as the number of endoscopists performing ESD has increased, the rate of non-curative resection after ESD has increased correspondingly. In order to decrease the non-curative resection rate, as well as determine the ideal rate of non-curative resection after ESD, it is time to consider quality indicators for the outcomes of ESD for EGC.
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Affiliation(s)
- Gwang Ha Kim
- Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 47241, South Korea
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Yang H, Xiang C, Mou Y, Zhou X, Li W, Duan Y, Hu B. The investigation of volatile organic compounds in diagnosing (early) esophageal squamous cell carcinoma and gastric adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:7029-7041. [PMID: 36859724 DOI: 10.1007/s00432-023-04595-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/21/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE The diagnosis of upper gastrointestinal cancer (UGIC) and early UGIC is currently based on endoscopy and histopathology. In this study, we aimed to explore whether intraluminal and exhaled volatile organic compounds (VOCs) could be used to diagnose (early) esophageal squamous cell carcinoma (ESCC) and gastric adenocarcinoma (GC). METHODS We prospectively recruited 259 patients and first collected intraluminal gas simples directly from upper GI tract via our designed device after passing endoscopic biopsy channel and collected exhaled gas samples in pairs. RESULTS 509 gas samples were totally collected and VOCs composed by peak compounds detected by gas chromatography-mass spectrometry (GC-MS) were used to train and test Multilayer Perceptron Network (MPN) for discrimination. Intraluminal and exhaled gas had more than 0.95 area under the curve (AUC) to discriminate UGIC (ESCC and GC) and early UGIC from benign control with different VOCs compositions. CONCLUSION Both intraluminal and exhaled VOCs had cancer-specific compositions to accurately discriminate early UGIC and UGIC, and the ability of intraluminal VOCs was better than that of exhaled VOCs. These suggested the potential role of VOCs in diagnosing and screening early UGIC and UGIC in the future.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Chengfang Xiang
- College of Chemistry, Sichuan University, Chengdu, 610041, China
| | - Yi Mou
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Xinyue Zhou
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Wenwen Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Yixiang Duan
- School of Mechanical Engineering, Sichuan University, Chengdu, 610064, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Wu Hou District, Chengdu, 610041, Sichuan, China.
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Vasconcelos AC, Dinis-Ribeiro M, Libânio D. Endoscopic Resection of Early Gastric Cancer and Pre-Malignant Gastric Lesions. Cancers (Basel) 2023; 15:3084. [PMID: 37370695 DOI: 10.3390/cancers15123084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/25/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Early gastric cancer comprises gastric malignancies that are confined to the mucosa or submucosa, irrespective of lymph node metastasis. Endoscopic resection is currently pivotal for the management of such early lesions, and it is the recommended treatment for tumors presenting a very low risk of lymph node metastasis. In general, these lesions consist of two groups of differentiated mucosal adenocarcinomas: non-ulcerated lesions (regardless of their size) and small ulcerated lesions. Endoscopic submucosal dissection is the technique of choice in most cases. This procedure has high rates of complete histological resection while maintaining gastric anatomy and its functions, resulting in fewer adverse events than surgery and having a lesser impact on patient-reported quality of life. Nonetheless, approximately 20% of resected lesions do not fulfill curative criteria and demand further treatment, highlighting the importance of patient selection. Additionally, the preservation of the stomach results in a moderate risk of metachronous lesions, which underlines the need for surveillance. We review the current evidence regarding the endoscopic treatment of early gastric cancer, including the short-and long-term results and management after resection.
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Affiliation(s)
- Ana Clara Vasconcelos
- Department of Gastroenterology, Porto Comprehensive Cancer Center Raquel Seruca, and RISE@CI-IPO (Health Research Network), 4200-072 Porto, Portugal
| | - Mário Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center Raquel Seruca, and RISE@CI-IPO (Health Research Network), 4200-072 Porto, Portugal
- MEDCIDS (Department of Community Medicine, Health Information, and Decision), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center Raquel Seruca, and RISE@CI-IPO (Health Research Network), 4200-072 Porto, Portugal
- MEDCIDS (Department of Community Medicine, Health Information, and Decision), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Martins BC, Moura RN, Kum AST, Matsubayashi CO, Marques SB, Safatle-Ribeiro AV. Endoscopic Imaging for the Diagnosis of Neoplastic and Pre-Neoplastic Conditions of the Stomach. Cancers (Basel) 2023; 15:cancers15092445. [PMID: 37173912 PMCID: PMC10177554 DOI: 10.3390/cancers15092445] [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: 02/21/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Gastric cancer is an aggressive disease with low long-term survival rates. An early diagnosis is essential to offer a better prognosis and curative treatment. Upper gastrointestinal endoscopy is the main tool for the screening and diagnosis of patients with gastric pre-neoplastic conditions and early lesions. Image-enhanced techniques such as conventional chromoendoscopy, virtual chromoendoscopy, magnifying imaging, and artificial intelligence improve the diagnosis and the characterization of early neoplastic lesions. In this review, we provide a summary of the currently available recommendations for the screening, surveillance, and diagnosis of gastric cancer, focusing on novel endoscopy imaging technologies.
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Affiliation(s)
- Bruno Costa Martins
- Endoscopy Unit, Instituto do Cancer do Estado de São Paulo, University of São Paulo, São Paulo 01246-000, Brazil
- Fleury Medicina e Saude, São Paulo 01333-010, Brazil
| | - Renata Nobre Moura
- Endoscopy Unit, Instituto do Cancer do Estado de São Paulo, University of São Paulo, São Paulo 01246-000, Brazil
- Fleury Medicina e Saude, São Paulo 01333-010, Brazil
| | - Angelo So Taa Kum
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo 05403-010, Brazil
| | - Carolina Ogawa Matsubayashi
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo 05403-010, Brazil
| | - Sergio Barbosa Marques
- Fleury Medicina e Saude, São Paulo 01333-010, Brazil
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo 05403-010, Brazil
| | - Adriana Vaz Safatle-Ribeiro
- Endoscopy Unit, Instituto do Cancer do Estado de São Paulo, University of São Paulo, São Paulo 01246-000, Brazil
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Namasivayam V, Uedo N. Quality indicators in the endoscopic detection of gastric cancer. DEN OPEN 2023; 3:e221. [PMID: 37051139 PMCID: PMC10083214 DOI: 10.1002/deo2.221] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 02/10/2023] [Accepted: 02/26/2023] [Indexed: 04/14/2023]
Abstract
Gastroscopy is the reference standard for the diagnosis of gastric cancer, but it is operator-dependent and associated with missed gastric cancer. The proliferation of gastroscopic examinations, increasingly for the screening and detection of subtle premalignant lesions, has motivated scrutiny of quality in gastroscopy. The concept of a high-quality endoscopic examination for the detection of superficial gastric neoplasia has been defined by expert guidelines to improve mucosal visualization, engender a systematic examination process and detect superficial neoplasia. This review discusses the evidence supporting the components of a high-quality diagnostic gastroscopic examination in relation to the detection of gastric cancer, and their potential role as procedural quality indicators to drive a structured improvement in clinically meaningful outcomes.
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Affiliation(s)
| | - Noriya Uedo
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
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