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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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Liu J, Fu F. Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching. PLoS One 2023; 18:e0293411. [PMID: 37883500 PMCID: PMC10602282 DOI: 10.1371/journal.pone.0293411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Keyboard instruments play a significant role in the music teaching process, providing students with an enjoyable musical experience while enhancing their music literacy. This study aims to investigate the current state of keyboard instrument teaching in preschool education, identify existing challenges, and propose potential solutions using the literature review method. In response to identified shortcomings, this paper proposes integrating intelligent technology and subject teaching through the application of teaching robots in keyboard instrument education. Specifically, a Convolutional Neural Network model of Deep Learning is employed for system debugging, enabling the teaching robot to analyze students' images and movements during musical instrument play and deliver targeted teaching. Feedback from students who participated in keyboard instrument teaching with the robot indicates high satisfaction levels. This paper aims to diversify keyboard instruments' teaching mode, introduce the practical application of robots in classroom teaching, and facilitate personalized teaching catering to individual students' aptitudes.
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Affiliation(s)
- Jidong Liu
- Lingnan Normal University, Zhanjiang, China
| | - Fang Fu
- Xiangzhong Normal College For Preschool Education,Shaoyang, China
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3
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Yang Y, Shao Y, Wang J, Cheng Q, Yang H, Li Y, Liu J, Zhou Y, Zhou Z, Wang M, Ji B, Yao J. Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer. Front Oncol 2023; 13:1185240. [PMID: 37746295 PMCID: PMC10516559 DOI: 10.3389/fonc.2023.1185240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/07/2023] [Indexed: 09/26/2023] Open
Abstract
Purpose To explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease. Methods From November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms. Results Baseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R2 of 0.37, a Brier score of 0.15, and a Harrell's C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell's C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort. Conclusion The established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients.
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Affiliation(s)
- Yan Yang
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yu Shao
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Junjun Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Qianqian Cheng
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Hanqi Yang
- Department of Clinical Laboratory, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yulong Li
- Department of Surgical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Jing Liu
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Yangyang Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Zhengguang Zhou
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Mingxi Wang
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Baoan Ji
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, United States
| | - Jinghao Yao
- Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
- Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Liao WC, Mukundan A, Sadiaza C, Tsao YM, Huang CW, Wang HC. Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4383-4405. [PMID: 37799695 PMCID: PMC10549751 DOI: 10.1364/boe.492635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 10/07/2023]
Abstract
One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.
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Affiliation(s)
- Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Cleorita Sadiaza
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila, 1015, Philippines
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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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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Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. J Med Internet Res 2023; 25:e43832. [PMID: 36862499 PMCID: PMC10020907 DOI: 10.2196/43832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
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Affiliation(s)
- Peng Xue
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongxu Qin
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingrui Wei
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Zichen Ye
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sumeng Wang
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Ding
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenling Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anying Bai
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Dang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuqian Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science & Technology of China, Sichuan, China
| | - Remila Rezhake
- Affiliated Cancer Hospital, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University, Xinjiang, China
| | - Shaokai Zhang
- Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Henan, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yimin Qu
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Ochiai K, Ozawa T, Shibata J, Ishihara S, Tada T. Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy. Diagnostics (Basel) 2022; 12:diagnostics12123153. [PMID: 36553160 PMCID: PMC9777622 DOI: 10.3390/diagnostics12123153] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/07/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.
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Affiliation(s)
- Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tsuyoshi Ozawa
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
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Dzierżak R, Omiotek Z. Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8189. [PMID: 36365886 PMCID: PMC9655338 DOI: 10.3390/s22218189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.
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Jin T, Jiang Y, Mao B, Wang X, Lu B, Qian J, Zhou H, Ma T, Zhang Y, Li S, Shi Y, Yao Z. Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm. Front Oncol 2022; 12:953090. [PMID: 36052264 PMCID: PMC9425091 DOI: 10.3389/fonc.2022.953090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/27/2022] [Indexed: 11/24/2022] Open
Abstract
Objective Convolutional Neural Network(CNN) is increasingly being applied in the diagnosis of gastric cancer. However, the impact of proportion of internal data in the training set on test results has not been sufficiently studied. Here, we constructed an artificial intelligence (AI) system called EGC-YOLOV4 using the YOLO-v4 algorithm to explore the optimal ratio of training set with the power to diagnose early gastric cancer. Design A total of 22,0918 gastroscopic images from Yixing People’s Hospital were collected. 7 training set models were established to identify 4 test sets. Respective sensitivity, specificity, Youden index, accuracy, and corresponding thresholds were tested, and ROC curves were plotted. Results 1. The EGC-YOLOV4 system completes all tests at an average reading speed of about 15 ms/sheet; 2. The AUC values in training set 1 model were 0.8325, 0.8307, 0.8706, and 0.8279, in training set 2 model were 0.8674, 0.8635, 0.9056, and 0.9249, in training set 3 model were 0.8544, 0.8881, 0.9072, and 0.9237, in training set 4 model were 0.8271, 0.9020, 0.9102, and 0.9316, in training set 5 model were 0.8249, 0.8484, 0.8796, and 0.8931, in training set 6 model were 0.8235, 0.8539, 0.9002, and 0.9051, in training set 7 model were 0.7581, 0.8082, 0.8803, and 0.8763. Conclusion EGC-YOLOV4 can quickly and accurately identify the early gastric cancer lesions in gastroscopic images, and has good generalization.The proportion of positive and negative samples in the training set will affect the overall diagnostic performance of AI.In this study, the optimal ratio of positive samples to negative samples in the training set is 1:1~ 1:2.
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Affiliation(s)
- Tao Jin
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Yancai Jiang
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Boneng Mao
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Xing Wang
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Bo Lu
- Microsoft Ltd Co., Suzhou, China
| | - Ji Qian
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Hutao Zhou
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Tieliang Ma
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
| | - Yefei Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Sisi Li
- Department of Gastroenterology, Civil Aviation Hospital of Shanghai, A Branch of Ruijin Hospital, Shanghai, China
| | - Yun Shi
- Department of Gastroenterology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Department of Internal Medicine, Yixing Maternity and Child Health Care Hospital, Yixing, China
- *Correspondence: Zhendong Yao, ; Yun Shi,
| | - Zhendong Yao
- Department of Gastroenterology, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China
- *Correspondence: Zhendong Yao, ; Yun Shi,
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Tang D, Ni M, Zheng C, Ding X, Zhang N, Yang T, Zhan Q, Fu Y, Liu W, Zhuang D, Lv Y, Xu G, Wang L, Zou X. A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy. Surg Endosc 2022; 36:7800-7810. [PMID: 35641698 DOI: 10.1007/s00464-022-09319-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/27/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Diagnosis of early gastric cancer (EGC) under narrow band imaging endoscopy (NBI) is dependent on expertise and skills. We aimed to elucidate whether artificial intelligence (AI) could diagnose EGC under NBI and evaluate the diagnostic assistance of the AI system. METHODS In this retrospective diagnostic study, 21,785 NBI images and 20 videos from five centers were divided into a training dataset (13,151 images, 810 patients), an internal validation dataset (7057 images, 283 patients), four external validation datasets (1577 images, 147 patients), and a video validation dataset (20 videos, 20 patients). All the images were labeled manually and used to train an AI system using You look only once v3 (YOLOv3). Next, the diagnostic performance of the AI system and endoscopists were compared and the diagnostic assistance of the AI system was assessed. The accuracy, sensitivity, specificity, and AUC were primary outcomes. RESULTS The AI system diagnosed EGCs on validation datasets with AUCs of 0.888-0.951 and diagnosed all the EGCs (100.0%) in video dataset. The AI system achieved better diagnostic performance (accuracy, 93.2%, 95% CI, 90.0-94.9%) than senior (85.9%, 95% CI, 84.2-87.4%) and junior (79.5%, 95% CI, 77.8-81.0%) endoscopists. The AI system significantly enhanced the performance of endoscopists in senior (89.4%, 95% CI, 87.9-90.7%) and junior (84.9%, 95% CI, 83.4-86.3%) endoscopists. CONCLUSION The NBI AI system outperformed the endoscopists and exerted potential assistant impact in EGC identification. Prospective validations are needed to evaluate the clinical reinforce of the system in real clinical practice.
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Affiliation(s)
- Dehua Tang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Muhan Ni
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Chang Zheng
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Xiwei Ding
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Nina Zhang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Tian Yang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Qiang Zhan
- Department of Gastroenterology, Wuxi People's Hospital, Affiliated Wuxi People's Hospital With Nanjing Medical University, Wuxi, 214023, Jiangsu, China
| | - Yiwei Fu
- Department of Gastroenterology, Taizhou People's Hospital, The Fifth Affiliated Hospital With Nantong University, Taizhou, 225300, Jiangsu, China
| | - Wenjia Liu
- Department of Gastroenterology, Changzhou Second People's Hospital, Changzhou, 213003, Jiangsu, China
| | - Duanming Zhuang
- Department of Gastroenterology, Nanjing Gaochun People's Hospital, Nanjing, 211300, Jiangsu, China
| | - Ying Lv
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Guifang Xu
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
| | - Lei Wang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
| | - Xiaoping Zou
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
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Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022; 2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]
Abstract
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image‐enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.
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Affiliation(s)
- Sayaka Nagao
- Department of Gastroenterology Graduate School of Medicine the University of Tokyo Tokyo Japan
- Department of Endoscopy and Endoscopic Surgery Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Yasuhiro Tani
- Department of Gastrointestinal Oncology Osaka International Cancer Institute Osaka Japan
| | - Junichi Shibata
- Tada Tomohiro Institute of Gastroenterology and Proctology Saitama Japan
| | - Yosuke Tsuji
- Department of Gastroenterology Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and Proctology Saitama Japan
- AI Medical Service Inc. Tokyo Japan
- Department of Surgical Oncology Graduate School of Medicine the University of Tokyo Tokyo Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology Osaka International Cancer Institute Osaka Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology Graduate School of Medicine the University of Tokyo Tokyo Japan
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Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y. Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc 2022; 95:599-609.e7. [PMID: 34979114 DOI: 10.1016/j.gie.2021.12.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists. METHODS PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis. RESULTS Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively. CONCLUSIONS The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.
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Affiliation(s)
- Fang Xie
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Keqiang Zhang
- Second Hospital of Jilin University, Changchun, Jilin, China
| | - Feng Li
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Guorong Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Ni
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Wei Zhang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Junchao Wang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, Jilin, China
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Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest Endosc 2022; 95:671-678.e4. [PMID: 34896101 DOI: 10.1016/j.gie.2021.11.040] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/20/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning-based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE). METHODS M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice. RESULTS A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice. CONCLUSIONS This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.).
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Barbulescu GI, Buica TP, Goje ID, Bojin FM, Ordodi VL, Olteanu GE, Heredea RE, Paunescu V. Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks. MICROMACHINES 2022; 13:mi13010079. [PMID: 35056244 PMCID: PMC8778756 DOI: 10.3390/mi13010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 02/01/2023]
Abstract
Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.
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Affiliation(s)
- Greta Ionela Barbulescu
- Immuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania; (F.M.B.); (V.P.)
- Department of Clinical Practical Skills, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania;
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania; (T.P.B.); (V.L.O.)
- Correspondence: (G.I.B.); (I.D.G.); Tel.: +40-733177583 (G.-I.B.)
| | - Taddeus Paul Buica
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania; (T.P.B.); (V.L.O.)
| | - Iacob Daniel Goje
- Department of Medical Semiology I, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania
- Advanced Cardiology and Hemostaseology Research Center, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania
- Correspondence: (G.I.B.); (I.D.G.); Tel.: +40-733177583 (G.-I.B.)
| | - Florina Maria Bojin
- Immuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania; (F.M.B.); (V.P.)
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania; (T.P.B.); (V.L.O.)
| | - Valentin Laurentiu Ordodi
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania; (T.P.B.); (V.L.O.)
- Department of Applied Chemistry, Organic and Natural Compounds Engineering, Faculty of Industrial Chemistry and Environmental Engineering, “Politehnica” University Timisoara, No. 2 Victoriei Square, 300006 Timisoara, Romania
| | - Gheorghe Emilian Olteanu
- Department of Pathology, “Dr Victor Babes” Clinical Hospital of Infectious Disease and Pneumophysiology, 300041 Timisoara, Romania;
| | - Rodica Elena Heredea
- Department of Clinical Practical Skills, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania;
- Advanced Cardiology and Hemostaseology Research Center, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania
- Department of Pathology, “Louis Turcanu” Children’s Clinical Emergency Hospital, 300041 Timisoara, Romania
| | - Virgil Paunescu
- Immuno-Physiology and Biotechnologies Center (CIFBIOTEH), Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, No. 2 Eftimie Murgu Square, 300041 Timisoara, Romania; (F.M.B.); (V.P.)
- Center for Gene and Cellular Therapies in the Treatment of Cancer Timisoara-OncoGen, Clinical Emergency County Hospital “Pius Brinzeu” Timisoara, No. 156 Liviu Rebreanu, 300723 Timisoara, Romania; (T.P.B.); (V.L.O.)
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Wu L, Wang J, He X, Zhu Y, Jiang X, Chen Y, Wang Y, Huang L, Shang R, Dong Z, Chen B, Tao X, Wu Q, Yu H. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc 2022; 95:92-104.e3. [PMID: 34245752 DOI: 10.1016/j.gie.2021.06.033] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND AND AIMS We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition. METHODS This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs. RESULTS One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively). CONCLUSIONS The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yiyun Chen
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qi Wu
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers (Basel) 2021; 13:4919. [PMID: 34638408 PMCID: PMC8507866 DOI: 10.3390/cancers13194919] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/11/2022] Open
Abstract
Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.
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Affiliation(s)
- Sebastian Klein
- Gerhard-Domagk-Institute for Pathology, University Hospital Münster, 48149 Münster, Germany
- Institute for Pathology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Dan G. Duda
- Edwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02478, USA
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