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Lv P, Cao Z, Zhu Z, Xu X, Zhao Z. Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study. Open Med (Wars) 2024; 19:20241031. [PMID: 39291279 PMCID: PMC11406433 DOI: 10.1515/med-2024-1031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 08/07/2024] [Accepted: 08/12/2024] [Indexed: 09/19/2024] Open
Abstract
Background The efficacy of artificial neural network (ANN) models employing laboratory variables for predicting fatty liver disease (FLD) remains inadequately established. The study aimed to develop ANN models to precisely predict FLD. Methods Of 12,058 participants undergoing the initial FLD screening, 7,990 eligible participants were included. A total of 6,309 participants were divided randomly into the training (4,415 participants, 70%) and validation (1,894 participants, 30%) sets for developing prediction models. The performance of ANNs was additionally tested in the testing set (1,681 participants). The area under the receiver operating characteristic curve (AUROC) was employed to assess the models' performance. Results The 18-variable, 11-variable, 3-variable, and 2-variable models each achieved robust FLD prediction performance, with AUROCs over 0.92, 0.91, and 0.89 in the training, validation, and testing, respectively. Although slightly inferior to the other three models in performance (AUROC ranges: 0.89-0.92 vs 0.91-0.95), the 2-variable model showed 80.3% accuracy and 89.7% positive predictive value in the testing. Incorporating age and gender increased the AUROCs of the resulting 20-variable, 13-variable, 5-variable, and 4-variable models each to over 0.93, 0.92, and 0.91 in the training, validation, and testing, respectively. Conclusions Implementation of the ANN models could effectively predict FLD, with enhanced predictive performance via the inclusion of age and gender.
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Affiliation(s)
- Panpan Lv
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Cao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhengqi Zhu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Xiaoqin Xu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Zhao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
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Kral J, Hradis M, Buzga M, Kunovsky L. Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2024. [PMID: 39234774 DOI: 10.5507/bp.2024.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.
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Affiliation(s)
- Jan Kral
- Department of Internal Medicine, University Hospital Motol and Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Hepatogastroenterology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Michal Hradis
- MAIA LABS s.r.o., Brno, Czech Republic
- Faculty of Information Technology, University of Technology, Brno, Czech Republic
| | - Marek Buzga
- Department of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic
| | - Lumir Kunovsky
- 2nd Department of Internal Medicine - Gastroenterology and Geriatrics, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
- Department of Surgery, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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3
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Liu H, Diao YK, Wei F, Wang SY, Liang YJ, Wu YF, Zheng QX, Wang XM, Wang H, Li J, Chen TH, Wu XC, Gu WM, Zhou YH, Guo HW, Shao GZ, Xu JH, Yao LQ, Wang MD, Shen F, Pawlik TM, Lau WY, Lv GY, Yang T. Stratifying risk of failure to achieve textbook outcomes among patients undergoing hepatectomy for hepatocellular carcinoma: A multicenter score validation study. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108477. [PMID: 38954879 DOI: 10.1016/j.ejso.2024.108477] [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: 05/26/2024] [Accepted: 06/08/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND AND AIMS The concept of textbook outcomes (TOs) has gained increased attention as a critical metric to assess the quality and success of outcomes following complex surgery. A simple yet effective scoring system was developed and validated to predict risk of not achieving textbook outcomes (non-TOs) following hepatectomy for hepatocellular carcinoma (HCC). METHODS Using a multicenter prospectively collected database, risk factors associated with non-TO among patients who underwent hepatectomy for HCC were identified. A predictive scoring system based on factors identified from multivariate regression analysis was used to risk stratify patients relative to non-TO. The score was developed using 70 % of the overall cohort and validated in the remaining 30 %. RESULTS Among 3681 patients, 1458 (39.6 %) failied to experience a TO. Based on the derivation cohort, obesity, American Society of Anaesthesiologists score(ASA score), Child-Pugh grade, tumor size, and extent of hepatectomy were identified as independent predictors of non-TO. The scoring system ranged from 0 to 10 points. Patients were categorized into low (0-3 points), intermediate (4-6 points), and high risk (7-10 points) of non-TO. In the validation cohort, the predicted risk of developing non-TOs was 39.0 %, which closely matched the observed risk of 39.9 %. There were no differences among the predicted and observed risks within the different risk categories. CONCLUSIONS A novel scoring system was able to predict risk of non-TO accurately following hepatectomy for HCC. The score may enable early identification of individuals at risk of adverse outcomes and inform surgical decision-making, and quality improvement initiatives.
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Affiliation(s)
- Han Liu
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Yong-Kang Diao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Si-Yuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Ying-Jian Liang
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yi-Fan Wu
- Department of Hepatobiliary Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Qi-Xuan Zheng
- Department of Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Xian-Ming Wang
- Department of General Surgery, First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Hong Wang
- Department of General Surgery, Liuyang People's Hospital, Liuyang, Hunan, China
| | - Jie Li
- Department of Hepatobiliary Surgery, Fuyang People's Hospital, Fuyang, Anhui, China
| | - Ting-Hao Chen
- Department of General Surgery, Ziyang First People's Hospital, Ziyang, Sichuan, China
| | - Xiao-Chang Wu
- Department of Hepatobiliary Surgery, Huzhou Central Hospital, Zhejiang University Huzhou Hospital, Huzhou, Zhejiang, China
| | - Wei-Min Gu
- The First Department of General Surgery, The Fourth Hospital of Harbin, Harbin, Heilongjiang, China
| | - Ya-Hao Zhou
- Department of Hepatobiliary Surgery, Pu'er People's Hospital, Pu'er, Yunnan, China
| | - Hong-Wei Guo
- The 2nd Department of General Surgery, The Second People's Hospital of Changzhi, Changzhi, China
| | - Guang-Zhao Shao
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Jia-Hao Xu
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Lan-Qing Yao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Ming-Da Wang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Feng Shen
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Timothy M Pawlik
- Department of Surgery, Ohio State University, Wexner Medical Center, Columbus, OH, United States
| | - Wan Yee Lau
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China; Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Guo-Yue Lv
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Tian Yang
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China; Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China.
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Mikkola M, Desmet KLJ, Kommisrud E, Riegler MA. Recent advancements to increase success in assisted reproductive technologies in cattle. Anim Reprod 2024; 21:e20240031. [PMID: 39176005 PMCID: PMC11340803 DOI: 10.1590/1984-3143-ar2024-0031] [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: 03/15/2024] [Accepted: 06/14/2024] [Indexed: 08/24/2024] Open
Abstract
Assisted reproductive technologies (ART) are fundamental for cattle breeding and sustainable food production. Together with genomic selection, these technologies contribute to reducing the generation interval and accelerating genetic progress. In this paper, we discuss advancements in technologies used in the fertility evaluation of breeding animals, and the collection, processing, and preservation of the gametes. It is of utmost importance for the breeding industry to select dams and sires of the next generation as young as possible, as is the efficient and timely collection of gametes. There is a need for reliable and easily applicable methods to evaluate sexual maturity and fertility. Although gametes processing and preservation have been improved in recent decades, challenges are still encountered. The targeted use of sexed semen and beef semen has obliterated the production of surplus replacement heifers and bull calves from dairy breeds, markedly improving animal welfare and ethical considerations in production practices. Parallel with new technologies, many well-established technologies remain relevant, although with evolving applications. In vitro production (IVP) has become the predominant method of embryo production. Although fundamental improvements in IVP procedures have been established, the quality of IVP embryos remains inferior to their in vivo counterparts. Improvements to facilitate oocyte maturation and development of new culture systems, e.g. microfluidics, are presented in this paper. New non-invasive and objective tools are needed to select embryos for transfer. Cryopreservation of semen and embryos plays a pivotal role in the distribution of genetics, and we discuss the challenges and opportunities in this field. Finally, machine learning (ML) is gaining ground in agriculture and ART. This paper delves into the utilization of emerging technologies in ART, along with the current status, key challenges, and future prospects of ML in both research and practical applications within ART.
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Affiliation(s)
| | | | - Elisabeth Kommisrud
- CRESCO, Centre for Embryology and Healthy Development, Department of Biotechnology, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Michael A. Riegler
- Holistic Systems Department, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
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Piao Z, Meng M, Yang H, Xue T, Jia Z, Liu W. Distinguishing between aldosterone-producing adenomas and non-functional adrenocortical adenomas using the YOLOv5 network. Acta Radiol 2024; 65:1007-1014. [PMID: 38767055 DOI: 10.1177/02841851241251446] [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] [Indexed: 05/22/2024]
Abstract
BACKGROUND You Only Look Once version 5 (YOLOv5), a one-stage deep-learning (DL) algorithm for object detection and classification, offers high speed and accuracy for identifying targets. PURPOSE To investigate the feasibility of using the YOLOv5 algorithm to non-invasively distinguish between aldosterone-producing adenomas (APAs) and non-functional adrenocortical adenomas (NF-ACAs) on computed tomography (CT) images. MATERIAL AND METHODS A total of 235 patients who were diagnosed with ACAs between January 2011 and July 2022 were included in this study. Of the 215 patients, 81 (37.7%) had APAs and 134 (62.3%) had NF-ACAs' they were randomly divided into either the training set or the validation set at a ratio of 9:1. Another 20 patients, including 8 (40.0%) with APA and 12 (60.0%) with NF-ACA, were collected for the testing set. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets. RESULTS In the testing set, the mAP_0.5 value for YOLOv5x (0.988) was higher than the values for YOLOv5n (0.969), YOLOv5s (0.965), YOLOv5m (0.974), and YOLOv5l (0.983). The mAP_0.5:0.95 value for YOLOv5x (0.711) was also higher than the values for YOLOv5n (0.587), YOLOv5s (0.674), YOLOv5m (0.671), and YOLOv5l (0.698) in the testing set. The inference speed of YOLOv5n was 2.4 ms in the testing set, which was the fastest among the five submodels. CONCLUSION The YOLOv5 algorithm can accurately and efficiently distinguish between APAs and NF-ACAs on CT images, especially YOLOv5x has the best identification performance.
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Affiliation(s)
- Zeyu Piao
- Graduate College, Dalian Medical University, Dalian, PR China
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Mingzhu Meng
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Huijie Yang
- Department of Endocrinology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Tongqing Xue
- Department of Interventional Radiology, Huaian Hospital of Huai'an City, Huai'an, PR China
| | - Zhongzhi Jia
- Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Wei Liu
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
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6
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Nam SJ, Moon G, Park JH, Kim Y, Lim YJ, Choi HS. Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy. Biomedicines 2024; 12:1704. [PMID: 39200169 PMCID: PMC11351118 DOI: 10.3390/biomedicines12081704] [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: 05/29/2024] [Revised: 07/16/2024] [Accepted: 07/22/2024] [Indexed: 09/02/2024] Open
Abstract
BACKGROUND Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. METHODS We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. RESULTS Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. CONCLUSIONS The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.
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Affiliation(s)
- Seung-Joo Nam
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Gwiseong Moon
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
| | | | - Yoon Kim
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, College of IT, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Yun Jeong Lim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang 10326, Republic of Korea
| | - Hyun-Soo Choi
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
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Pathipati MP, Shah ED. A Practical Approach to Navigating Relevance, Regulatory, and Reimbursement in Gastroenterology Innovation for Gastroenterology Trainees. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00545-7. [PMID: 39019081 DOI: 10.1016/j.cgh.2024.05.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 07/19/2024]
Affiliation(s)
- Mythili P Pathipati
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Center for Neurointestinal Health, Massachusetts General Hospital, Boston, Massachusetts
| | - Eric D Shah
- Division of Gastroenterology & Hepatology, Michigan Medicine, Ann Arbor, Michigan
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Uher O, Hadrava Vanova K, Taïeb D, Calsina B, Robledo M, Clifton-Bligh R, Pacak K. The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives. Endocr Rev 2024; 45:521-552. [PMID: 38377172 PMCID: PMC11244254 DOI: 10.1210/endrev/bnae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024]
Abstract
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.
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Affiliation(s)
- Ondrej Uher
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - Katerina Hadrava Vanova
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - David Taïeb
- Department of Nuclear Medicine, CHU de La Timone, Marseille 13005, France
| | - Bruna Calsina
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Familiar Cancer Clinical Unit, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institute of Health Carlos III (ISCIII), Madrid 28029, Spain
| | - Roderick Clifton-Bligh
- Department of Endocrinology, Royal North Shore Hospital, Sydney 2065, NSW, Australia
- Cancer Genetics Laboratory, Kolling Institute, University of Sydney, Sydney 2065, NSW, Australia
| | - Karel Pacak
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
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9
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Chen S, Zhuang D, Jia Q, Guo B, Hu G. Advances in Noninvasive Molecular Imaging Probes for Liver Fibrosis Diagnosis. Biomater Res 2024; 28:0042. [PMID: 38952717 PMCID: PMC11214848 DOI: 10.34133/bmr.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/08/2024] [Indexed: 07/03/2024] Open
Abstract
Liver fibrosis is a wound-healing response to chronic liver injury, which may lead to cirrhosis and cancer. Early-stage fibrosis is reversible, and it is difficult to precisely diagnose with conventional imaging modalities such as magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, and ultrasound imaging. In contrast, probe-assisted molecular imaging offers a promising noninvasive approach to visualize early fibrosis changes in vivo, thus facilitating early diagnosis and staging liver fibrosis, and even monitoring of the treatment response. Here, the most recent progress in molecular imaging technologies for liver fibrosis is updated. We start by illustrating pathogenesis for liver fibrosis, which includes capillarization of liver sinusoidal endothelial cells, cellular and molecular processes involved in inflammation and fibrogenesis, as well as processes of collagen synthesis, oxidation, and cross-linking. Furthermore, the biological targets used in molecular imaging of liver fibrosis are summarized, which are composed of receptors on hepatic stellate cells, macrophages, and even liver collagen. Notably, the focus is on insights into the advances in imaging modalities developed for liver fibrosis diagnosis and the update in the corresponding contrast agents. In addition, challenges and opportunities for future research and clinical translation of the molecular imaging modalities and the contrast agents are pointed out. We hope that this review would serve as a guide for scientists and students who are interested in liver fibrosis imaging and treatment, and as well expedite the translation of molecular imaging technologies from bench to bedside.
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Affiliation(s)
- Shaofang Chen
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Danping Zhuang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Qingyun Jia
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application,
Harbin Institute of Technology, Shenzhen 518055, China
| | - Genwen Hu
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
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Guo Z, Zhang Z, Liu L, Zhao Y, Liu Z, Zhang C, Qi H, Feng J, Yang C, Tai W, Banchini F, Inchingolo R. Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108362. [PMID: 38704899 DOI: 10.1016/j.ejso.2024.108362] [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: 12/03/2023] [Revised: 04/11/2024] [Accepted: 04/20/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC). METHODS Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients. RESULTS 51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611. CONCLUSION In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.
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Affiliation(s)
- Zhentian Guo
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zongming Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China.
| | - Limin Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Yue Zhao
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zhuo Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Chong Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Hui Qi
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Jinqiu Feng
- Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China; Department of Immunology, Peking University School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Chunmin Yang
- Department of Gastroenterology, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China
| | - Weiping Tai
- Department of Gastroenterology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Filippo Banchini
- General Surgery Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, "F. Miulli" Regional General Hospital, Acquaviva delle Fonti, 70021, Italy
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11
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Park MA, Whelan CJ, Ahmed S, Boeringer T, Brown J, Crowder SL, Gage K, Gregg C, Jeong DK, Jim HSL, Judge AR, Mason TM, Parker N, Pillai S, Qayyum A, Rajasekhara S, Rasool G, Tinsley SM, Schabath MB, Stewart P, West J, McDonald P, Permuth JB. Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration. Cancers (Basel) 2024; 16:2364. [PMID: 39001427 PMCID: PMC11240731 DOI: 10.3390/cancers16132364] [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: 05/29/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.
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Affiliation(s)
- Margaret A. Park
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Christopher J. Whelan
- Department of Metabolism and Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Sabeen Ahmed
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.A.); (G.R.)
| | - Tabitha Boeringer
- Department of Drug Discovery, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.B.); (S.P.)
| | - Joel Brown
- Department of Cancer Biology and Evolution, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (J.B.); (J.W.)
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Sylvia L. Crowder
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Kenneth Gage
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Christopher Gregg
- School of Medicine, University of Utah, Salt Lake City, UT 84113, USA;
| | - Daniel K. Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Heather S. L. Jim
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Andrew R. Judge
- Department of Physical Therapy, University of Florida, Gainesville, FL 32610, USA;
| | - Tina M. Mason
- Department of Nursing Research, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Nathan Parker
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Smitha Pillai
- Department of Drug Discovery, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.B.); (S.P.)
| | - Aliya Qayyum
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Sahana Rajasekhara
- Department of Supportive Care Medicine, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Ghulam Rasool
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.A.); (G.R.)
| | - Sara M. Tinsley
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Paul Stewart
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Jeffrey West
- Department of Cancer Biology and Evolution, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (J.B.); (J.W.)
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Patricia McDonald
- Department of Metabolism and Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Lexicon Pharmaceuticals, Inc., Woodlands, TX 77381, USA
| | - Jennifer B. Permuth
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
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12
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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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13
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Mamone G, Comelli A, Porrello G, Milazzo M, Di Piazza A, Stefano A, Benfante V, Tuttolomondo A, Sparacia G, Maruzzelli L, Miraglia R. Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation. Life (Basel) 2024; 14:726. [PMID: 38929709 PMCID: PMC11204649 DOI: 10.3390/life14060726] [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: 03/24/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using "controlled expansion covered stents". MATERIALS AND METHODS This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients' outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. RESULTS A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the "clinical response" and "survival at 6 months" outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. CONCLUSIONS A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.
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Affiliation(s)
- Giuseppe Mamone
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (V.B.)
| | - Giorgia Porrello
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D), University of Palermo, Via del Vespro 127, 90127 Palermo, Italy;
| | - Mariapina Milazzo
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Ambra Di Piazza
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
| | - Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (V.B.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy;
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy;
| | - Gianvincenzo Sparacia
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Luigi Maruzzelli
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Roberto Miraglia
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
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14
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Zheng R, Su R, Fan Y, Xing F, Huang K, Yan F, Chen H, Liu B, Fang L, Du Y, Zhou F, Wang D, Feng S. Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures. Anal Chem 2024; 96:8772-8781. [PMID: 38743842 DOI: 10.1021/acs.analchem.4c01171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.
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Affiliation(s)
- Ran Zheng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Rui Su
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130021, China
| | - Fan Xing
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Keke Huang
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Fei Yan
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Huanwen Chen
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Botong Liu
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Laiping Fang
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Yechao Du
- Department of General Surgery Center, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130021, China
| | - Daguang Wang
- Department of Gastric Colorectal and Anal Surgery, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China
| | - Shouhua Feng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
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Cardoso P, Mascarenhas M, Afonso J, Ribeiro T, Mendes F, Martins M, Andrade P, Cardoso H, Mascarenhas Saraiva M, Ferreira JP, Macedo G. Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network. Therap Adv Gastroenterol 2024; 17:17562848241251569. [PMID: 38812708 PMCID: PMC11135072 DOI: 10.1177/17562848241251569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 04/14/2024] [Indexed: 05/31/2024] Open
Abstract
Background Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.
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Affiliation(s)
- Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João P.S. Ferreira
- Faculty of Engineering, University of Porto, Porto, Portugal
- Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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Cheng Y, Li L, Bi Y, Su S, Zhang B, Feng X, Wang N, Zhang W, Yao Y, Ru N, Xiang J, Sun L, Hu K, Wen F, Wang Z, Bai L, Wang X, Wang R, Lv X, Wang P, Meng F, Xiao W, Linghu E, Chai N. Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging. Dig Liver Dis 2024:S1590-8658(24)00723-0. [PMID: 38744557 DOI: 10.1016/j.dld.2024.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.
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Affiliation(s)
- Yaxuan Cheng
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yawei Bi
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Song Su
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Bo Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xiuxue Feng
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nanjun Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Wengang Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yi Yao
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nan Ru
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Jingyuan Xiang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lihua Sun
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Kang Hu
- Department of Gastroenterology, The 987 Hospital of PLA Joint Logistic Support Force, Baoji, 721004, PR China
| | - Feng Wen
- Department of Gastroenterology, General Hospital of Central Theater Command of PLA,Wuhan 430070, PR China
| | - Zixin Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lu Bai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xueting Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Runzi Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xingping Lv
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Pengju Wang
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Fanqi Meng
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Wen Xiao
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Enqiang Linghu
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
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Uchikov P, Khalid U, Vankov N, Kraeva M, Kraev K, Hristov B, Sandeva M, Dragusheva S, Chakarov D, Petrov P, Dobreva-Yatseva B, Novakov I. The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis. Diagnostics (Basel) 2024; 14:1004. [PMID: 38786302 PMCID: PMC11119852 DOI: 10.3390/diagnostics14101004] [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: 03/27/2024] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. METHOD A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. RESULTS Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. CONCLUSIONS When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Nikola Vankov
- University Multiprofile Hospital for Active Treatment “Saint George”, 4000 Plovdiv, Bulgaria;
| | - Maria Kraeva
- Department of Otorhynolaryngology, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Krasimir Kraev
- Department of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Section “Gastroenterology”, Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Milena Sandeva
- Department of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Snezhanka Dragusheva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
- Department of Anesthesiology, Emergency and Intensive Care Medicine, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Dzhevdet Chakarov
- Department of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Petko Petrov
- Department of Maxillofacial Surgery, Faculty of Dental Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Bistra Dobreva-Yatseva
- Section “Cardiology”, First Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Ivan Novakov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
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Gupta P, Basu S, Rana P, Dutta U, Soundararajan R, Kalage D, Chhabra M, Singh S, Yadav TD, Gupta V, Kaman L, Das CK, Gupta P, Saikia UN, Srinivasan R, Sandhu MS, Arora C. Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: a prospective diagnostic study. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2024; 24:100279. [PMID: 38756152 PMCID: PMC11096661 DOI: 10.1016/j.lansea.2023.100279] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/16/2023] [Accepted: 08/30/2023] [Indexed: 05/18/2024]
Abstract
Background Gallbladder cancer (GBC) is highly aggressive. Diagnosis of GBC is challenging as benign gallbladder lesions can have similar imaging features. We aim to develop and validate a deep learning (DL) model for the automatic detection of GBC at abdominal ultrasound (US) and compare its diagnostic performance with that of radiologists. Methods In this prospective study, a multiscale, second-order pooling-based DL classifier model was trained (training and validation cohorts) using the US data of patients with gallbladder lesions acquired between August 2019 and June 2021 at the Postgraduate Institute of Medical Education and research, a tertiary care hospital in North India. The performance of the DL model to detect GBC was evaluated in a temporally independent test cohort (July 2021-September 2022) and was compared with that of two radiologists. Findings The study included 233 patients in the training set (mean age, 48 ± (2SD) 23 years; 142 women), 59 patients in the validation set (mean age, 51.4 ± 19.2 years; 38 women), and 273 patients in the test set (mean age, 50.4 ± 22.1 years; 177 women). In the test set, the DL model had sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 92.3% (95% CI, 88.1-95.6), 74.4% (95% CI, 65.3-79.9), and 0.887 (95% CI, 0.844-0.930), respectively for detecting GBC which was comparable to both the radiologists. The DL-based approach showed high sensitivity (89.8-93%) and AUC (0.810-0.890) for detecting GBC in the presence of stones, contracted gallbladders, lesion size <10 mm, and neck lesions, which was comparable to both the radiologists (p = 0.052-0.738 for sensitivity and p = 0.061-0.745 for AUC). The sensitivity for DL-based detection of mural thickening type of GBC was significantly greater than one of the radiologists (87.8% vs. 72.8%, p = 0.012), despite a reduced specificity. Interpretation The DL-based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting GBC using US. However, multicentre studies are warranted to explore the potential of DL-based diagnosis of GBC fully. Funding None.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110016, India
| | - Pratyaksha Rana
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Usha Dutta
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Raghuraman Soundararajan
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Daneshwari Kalage
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Manika Chhabra
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Shravya Singh
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Thakur Deen Yadav
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Vikas Gupta
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Lileswar Kaman
- Department of General Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Chandan Krushna Das
- Department of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Parikshaa Gupta
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Uma Nahar Saikia
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Radhika Srinivasan
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Manavjit Singh Sandhu
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110016, India
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Takabayashi K, Kobayashi T, Matsuoka K, Levesque BG, Kawamura T, Tanaka K, Kadota T, Bise R, Uchida S, Kanai T, Ogata H. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc 2024; 36:582-590. [PMID: 37690125 DOI: 10.1111/den.14677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVES Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists. METHODS A ranking-convolutional neural network (ranking-CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking-CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI. RESULTS Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P < 0.01). CONCLUSIONS The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.
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Affiliation(s)
- Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Taku Kobayashi
- Center for Advanced IBD Research and Treatment, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Katsuyoshi Matsuoka
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Sakura Medical Center, Chiba, Japan
| | - Barrett G Levesque
- Division of Gastroenterology, Los Angeles County/University of Southern California Medical Center, Los Angeles, USA
| | - Takuji Kawamura
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Kiyohito Tanaka
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Takeaki Kadota
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Ryoma Bise
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Seiichi Uchida
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
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20
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Zhu Y, Du L, Fu PY, Geng ZH, Zhang DF, Chen WF, Li QL, Zhou PH. An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video). Bioengineering (Basel) 2024; 11:445. [PMID: 38790312 PMCID: PMC11118061 DOI: 10.3390/bioengineering11050445] [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: 03/05/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND AND AIMS Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images. METHODS Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos. RESULTS EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses. CONCLUSIONS We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice.
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Affiliation(s)
- Yan Zhu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Ling Du
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Pei-Yao Fu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Zi-Han Geng
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Dan-Feng Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Wei-Feng Chen
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Quan-Lin Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
| | - Ping-Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; (Y.Z.); (L.D.); (P.-Y.F.); (Z.-H.G.); (D.-F.Z.); (W.-F.C.)
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China
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21
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Rymarczyk D, Schultz W, Borowa A, Friedman JR, Danel T, Branigan P, Chałupczak M, Bracha A, Krawiec T, Warchoł M, Li K, De Hertogh G, Zieliński B, Ghanem LR, Stojmirovic A. Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity. J Crohns Colitis 2024; 18:604-614. [PMID: 37814351 PMCID: PMC11037111 DOI: 10.1093/ecco-jcc/jjad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND AND AIMS Histological disease activity in inflammatory bowel disease [IBD] is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD. METHODS Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease [CD] and ulcerative colitis [UC] were used to train artificial intelligence [AI] models to predict the Global Histology Activity Score [GHAS] for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets, and model predictions were compared against an expert central reader and five independent pathologists. RESULTS The model based on multiple instance learning and the attention mechanism [SA-AbMILP] demonstrated the best performance among competing models. AI-modelled GHAS and Geboes subgrades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features, with accuracies for colon in both CD and UC ranging from 87% to 94% and for CD ileum ranging from 76% to 83%. For both CD and UC and across anatomical compartments [ileum and colon] in CD, comparable accuracies against central readings were found between the model-assigned scores and scores by an independent set of pathologists. CONCLUSIONS Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.
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Affiliation(s)
- Dawid Rymarczyk
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Weiwei Schultz
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Adriana Borowa
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Joshua R Friedman
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Tomasz Danel
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Patrick Branigan
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | | | | | | | | | - Katherine Li
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Gert De Hertogh
- Department of Pathology, University Hospitals KU Leuven, Belgium
| | - Bartosz Zieliński
- AI Lab, Ardigen SA, Kraków, Poland
- Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
| | - Louis R Ghanem
- Immunology TA, Janssen Research & Development, LLC, Spring House, Pennsylvania
| | - Aleksandar Stojmirovic
- Data Science & Digital Health, Janssen Research & Development, LLC, Spring House, Pennsylvania
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Li H, Liu D, Zeng Y, Liu S, Gan T, Rao N, Yang J, Zeng B. Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:2676-2688. [PMID: 38530733 DOI: 10.1109/tip.2024.3379902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.
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23
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Peyrin-Biroulet L, Adsul S, Stancati A, Dehmeshki J, Kubassova O. An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis. United European Gastroenterol J 2024. [PMID: 38590110 DOI: 10.1002/ueg2.12562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/08/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND AND AIMS Assessment and scoring of histological images in Ulcerative colitis (UC) is prone to inter- and intra-observer variability. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index. METHODS A total of 200 histological images of patients with UC were used in this study. A novel AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by the Nancy index, were manually annotated and measured independently by four histopathologists. Manual and AI-automated measurements of the Nancy index score were conducted and assessed using the intraclass correlation coefficient (ICC). RESULTS The 200-image dataset was divided into two groups (80% was used for training and 20% for testing). Intraclass correlation coefficient statistical analyses were performed to evaluate the AI tool and used as a reference to calculate the accuracy. The average ICC among the histopathologists was 89.3 and the average ICC between histopathologists and the AI tool was 87.2. The AI tool was found to be highly correlated with histopathologists. CONCLUSIONS The high correlation of performance of the AI method suggests promising potential for inflammatory bowel disease clinical applications. A standardized automated histological AI-driven scoring system can potentially be used in daily inflammatory bowel disease practice to reduce training needs and resource use, eliminate the subjectivity of the pathologists, and assess disease severity for treatment decisions.
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Affiliation(s)
- Laurent Peyrin-Biroulet
- Department of Gastroenterology, INFINY Institute, FHU-CURE, INSERM NGERE, Nancy University Hospital, Vandoeuvre-lès-Nancy, France
| | - Shashi Adsul
- Takeda Pharmaceutical Company, Zurich, Switzerland
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Jiang X, Li T, Wang J, Zhang Z, Chen X, Zhang J, Zhao X. Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study. Cancer Biother Radiopharm 2024; 39:169-177. [PMID: 38193811 DOI: 10.1089/cbr.2023.0162] [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] [Indexed: 01/10/2024] Open
Abstract
Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.
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Affiliation(s)
- Xiaojing Jiang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Tianyue Li
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
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Mascarenhas Saraiva M, Spindler L, Fathallah N, Beaussier H, Mamma C, Ribeiro T, Afonso J, Carvalho M, Moura R, Cardoso P, Mendes F, Martins M, Adam J, Ferreira J, Macedo G, de Parades V. Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors. Clin Transl Gastroenterol 2024; 15:e00681. [PMID: 38270249 DOI: 10.14309/ctg.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Hélene Beaussier
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Célia Mamma
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Mariana Carvalho
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Rita Moura
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Julien Adam
- Department of Pathology, GH Paris Saint-Joseph, Paris, France
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
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Hudson AS, Wahbeh GT, Zheng HB. Imaging and endoscopic tools in pediatric inflammatory bowel disease: What's new? World J Clin Pediatr 2024; 13:89091. [PMID: 38596437 PMCID: PMC11000065 DOI: 10.5409/wjcp.v13.i1.89091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/04/2023] [Accepted: 01/04/2024] [Indexed: 03/06/2024] Open
Abstract
Pediatric inflammatory bowel disease (IBD) is a chronic inflammatory disorder, with increasing incidence and prevalence worldwide. There have been recent advances in imaging and endoscopic technology for disease diagnosis, treatment, and monitoring. Intestinal ultrasound, including transabdominal, transperineal, and endoscopic, has been emerging for the assessment of transmural bowel inflammation and disease complications (e.g., fistula, abscess). Aside from surgery, IBD-related intestinal strictures now have endoscopic treatment options including through-the-scope balloon dilatation, injection, and needle knife stricturotomy and new evaluation tools such as endoscopic functional lumen imaging probe. Unsedated transnasal endoscopy may have a role in patients with upper gastrointestinal Crohn's disease or those with IBD with new upper gastrointestinal symptoms. Improvements to dysplasia screening in pediatric patients with longstanding colonic disease or primary sclerosing cholangitis hold promise with the addition of virtual chromoendoscopy and ongoing research in the field of artificial intelligence-assisted endoscopic detection. Artificial intelligence and machine learning is a rapidly evolving field, with goals of further personalizing IBD diagnosis and treatment selection as well as prognostication. This review summarized these advancements, focusing on pediatric patients with IBD.
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Affiliation(s)
- Alexandra S Hudson
- Department of Pediatrics, University of Washington, Seattle, WA 98109, United States
| | - Ghassan T Wahbeh
- Department of Pediatrics, University of Washington, Seattle, WA 98109, United States
| | - Hengqi Betty Zheng
- Department of Pediatrics, University of Washington, Seattle, WA 98109, United States
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [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: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Gupta P, Basu S, Arora C. Applications of artificial intelligence in biliary tract cancers. Indian J Gastroenterol 2024:10.1007/s12664-024-01518-0. [PMID: 38427281 DOI: 10.1007/s12664-024-01518-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/29/2023] [Indexed: 03/02/2024]
Abstract
Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
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Reddy S, Shaheed A, Seo Y, Patel R. Development of an Artificial Intelligence Model for the Classification of Gastric Carcinoma Stages Using Pathology Slides. Cureus 2024; 16:e56740. [PMID: 38650818 PMCID: PMC11033212 DOI: 10.7759/cureus.56740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
This study showcases a novel AI-driven approach to accurately differentiate between stage one and stage two gastric carcinoma based on pathology slide analysis. Gastric carcinoma, a significant contributor to cancer-related mortality globally, necessitates precise staging for optimal treatment planning and patient management. Leveraging a comprehensive dataset of 3540 high-resolution pathology images sourced from Kaggle.com, comprising an equal distribution of stage one and stage two tumors, the developed AI model demonstrates remarkable performance in tumor staging. Through the application of state-of-the-art deep learning techniques on Google's Collaboration platform, the model achieves outstanding accuracy and precision rates of 100%, accompanied by notable sensitivity (97.09%), specificity (100%), and F1-score (98.31%). Additionally, the model exhibits an impressive area under the receiver operating characteristic curve (AUC) of 0.999, indicating superior discriminatory power and robustness. By providing clinicians with an efficient and reliable tool for gastric carcinoma staging, this AI-driven approach has the potential to significantly enhance diagnostic accuracy, inform treatment decisions, and ultimately improve patient outcomes in the management of gastric carcinoma. This research contributes to the ongoing advancement of cancer diagnosis and underscores the transformative potential of artificial intelligence in clinical practice.
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Affiliation(s)
- Shreya Reddy
- Biomedical Sciences, Creighton University, Omaha, USA
| | - Avneet Shaheed
- Pathology, University of Illinois at Chicago, Chicago, USA
| | - Yui Seo
- Medicine, California Northstate University College of Medicine, Elk Grove, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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Sachan A, Kakadiya R, Mishra S, Kumar-M P, Jena A, Gupta P, Sebastian S, Deepak P, Sharma V. Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review. J Gastroenterol Hepatol 2024; 39:422-430. [PMID: 38058246 DOI: 10.1111/jgh.16430] [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: 11/15/2022] [Revised: 08/04/2023] [Accepted: 11/13/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND AND AIM Discrimination of gastrointestinal tuberculosis (GITB) and Crohn's disease (CD) is difficult. Use of artificial intelligence (AI)-based technologies may help in discriminating these two entities. METHODS We conducted a systematic review on the use of AI for discrimination of GITB and CD. Electronic databases (PubMed and Embase) were searched on June 6, 2022, to identify relevant studies. We included any study reporting the use of clinical, endoscopic, and radiological information (textual or images) to discriminate GITB and CD using any AI technique. Quality of studies was assessed with MI-CLAIM checklist. RESULTS Out of 27 identified results, a total of 9 studies were included. All studies used retrospective databases. There were five studies of only endoscopy-based AI, one of radiology-based AI, and three of multiparameter-based AI. The AI models performed fairly well with high accuracy ranging from 69.6-100%. Text-based convolutional neural network was used in three studies and Classification and regression tree analysis used in two studies. Interestingly, irrespective of the AI method used, the performance of discriminating GITB and CD did not match in discriminating from other diseases (in studies where a third disease was also considered). CONCLUSION The use of AI in differentiating GITB and CD seem to have acceptable accuracy but there were no direct comparisons with traditional multiparameter models. The use of multiple parameter-based AI models have the potential for further exploration in search of an ideal tool and improve on the accuracy of traditional models.
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Affiliation(s)
- Anurag Sachan
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Rinkalben Kakadiya
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Shubhra Mishra
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | | | - Anuraag Jena
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pankaj Gupta
- Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Shaji Sebastian
- IBD Unit, Hull University Teaching Hospitals NHS Trust, Hull, UK
| | - Parakkal Deepak
- Division of Gastroenterology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Vishal Sharma
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endosc Ultrasound 2024; 13:65-75. [PMID: 38947752 PMCID: PMC11213611 DOI: 10.1097/eus.0000000000000053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
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Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
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Malakar S, Sutaoney P, Madhyastha H, Shah K, Chauhan NS, Banerjee P. Understanding gut microbiome-based machine learning platforms: A review on therapeutic approaches using deep learning. Chem Biol Drug Des 2024; 103:e14505. [PMID: 38491814 DOI: 10.1111/cbdd.14505] [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: 10/08/2023] [Revised: 02/21/2024] [Accepted: 03/04/2024] [Indexed: 03/18/2024]
Abstract
Human beings possess trillions of microbial cells in a symbiotic relationship. This relationship benefits both partners for a long time. The gut microbiota helps in many bodily functions from harvesting energy from digested food to strengthening biochemical barriers of the gut and intestine. But the changes in microbiota composition and bacteria that can enter the gastrointestinal tract can cause infection. Several approaches like culture-independent techniques such as high-throughput and meta-omics projects targeting 16S ribosomal RNA (rRNA) sequencing are popular methods to investigate the composition of the human gastrointestinal tract microbiota and taxonomically characterizing microbial communities. The microbiota conformation and diversity should be provided by whole-genome shotgun metagenomic sequencing of site-specific community DNA associating genome mapping, gene inventory, and metabolic remodelling and reformation, to ease the functional study of human microbiota. Preliminary examination of the therapeutic potency for dysbiosis-associated diseases permits investigation of pharmacokinetic-pharmacodynamic changes in microbial communities for escalation of treatment and dosage plan. Gut microbiome study is an integration of metagenomics which has influenced the field in the last two decades. And the incorporation of artificial intelligence and deep learning through "omics-based" methods and microfluidic evaluation enhanced the capability of identification of thousands of microbes.
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Affiliation(s)
- Shilpa Malakar
- Department of Microbiology, Kalinga University, Raipur, Chhattisgarh, India
| | - Priya Sutaoney
- Department of Microbiology, Kalinga University, Raipur, Chhattisgarh, India
| | - Harishkumar Madhyastha
- Department of Cardiovascular Physiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Kamal Shah
- Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, India
| | - Nagendra Singh Chauhan
- Department of Medical education, Drugs Testing Laboratory Avam Anusandhan Kendra, Raipur, Chhattisgarh, India
| | - Paromita Banerjee
- Department of Cardiology, AIIMS Rishikesh, Rishikesh, Uttarkhand, India
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Nigam GB, Murphy MF, Travis SPL, Stanley AJ. Machine learning in the assessment and management of acute gastrointestinal bleeding. BMJ MEDICINE 2024; 3:e000699. [PMID: 38389720 PMCID: PMC10882311 DOI: 10.1136/bmjmed-2023-000699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/05/2024] [Indexed: 02/24/2024]
Affiliation(s)
- Gaurav Bhaskar Nigam
- Translational Gastroenterology Unit, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Michael F Murphy
- Transfusion Medicine, NHS Blood and Transplant, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Simon P L Travis
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences and, Biomedical Research Centre, Oxford University, Oxford, UK
| | - Adrian J Stanley
- Department of Gastroenterology, Glasgow Royal Infirmary, Glasgow, UK
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Vitello A, Maida M, Shahini E, Macaluso FS, Orlando A, Grova M, Ramai D, Serviddio G, Facciorusso A. Current Approaches for Monitoring of Patients with Inflammatory Bowel Diseases: A Narrative Review. J Clin Med 2024; 13:1008. [PMID: 38398321 PMCID: PMC10888591 DOI: 10.3390/jcm13041008] [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: 01/21/2024] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Patients with inflammatory bowel diseases (IBD) require proactive monitoring both during the active phase to evaluate therapeutic response and during the remission phase to evaluate relapse or colorectal cancer surveillance. However, monitoring may vary between patients with ulcerative colitis (UC) and Crohn's disease (CD), with distinct tools and intervals. METHODS This narrative review aims to focus on modern approaches to IBD monitoring, considering international guidelines and expert consensus. RESULTS The most recent European diagnostic guidelines advocate a combination of clinical, laboratory, endoscopic, and radiological parameters to evaluate the disease course of patients with IBD. Unfortunately, the conventional symptom-based therapeutic approach does not improve long-term outcomes and there is no single ideal biomarker available. Endoscopy plays a key role in evaluating response to therapy as well as monitoring disease activity. Recently, bedside intestinal ultrasound (IUS) has gained increasing interest and diffusion as it appears to offer several advantages including the monitoring of therapeutic response. CONCLUSION In light of growing clinical advances, we present a schematic evidence-based monitoring algorithm that can be easily applied in clinical practice which combines all major monitoring modalities, including noninvasive tools such as IUS and video-capsule endoscopy.
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Affiliation(s)
- Alessandro Vitello
- Gastroenterology and Endoscopy Unit, S. Elia Hospital, 93100 Caltanissetta, Italy; (A.V.); (M.M.)
| | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia Hospital, 93100 Caltanissetta, Italy; (A.V.); (M.M.)
- Department of Medicine and Surgery, School of Medicine and Surgery, University of Enna ‘Kore’, 94100 Enna, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS “Saverio de Bellis”, 70013 Castellana Grotte, Italy;
| | - Fabio Salvatore Macaluso
- Inflammatory Bowel Disease Unit, Department of Medicine, A.O.O.R. “Villa Sofia-Cervello” Hospital, 90146 Palermo, Italy; (F.S.M.); (A.O.); (M.G.)
| | - Ambrogio Orlando
- Inflammatory Bowel Disease Unit, Department of Medicine, A.O.O.R. “Villa Sofia-Cervello” Hospital, 90146 Palermo, Italy; (F.S.M.); (A.O.); (M.G.)
| | - Mauro Grova
- Inflammatory Bowel Disease Unit, Department of Medicine, A.O.O.R. “Villa Sofia-Cervello” Hospital, 90146 Palermo, Italy; (F.S.M.); (A.O.); (M.G.)
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT 84132, USA;
| | - Gaetano Serviddio
- Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
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Yamaguchi R, Morikawa H, Akatsuka J, Numata Y, Noguchi A, Kokumai T, Ishida M, Mizuma M, Nakagawa K, Unno M, Miyake A, Tamiya G, Yamamoto Y, Furukawa T. Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment. Pancreas 2024; 53:e199-e204. [PMID: 38127849 DOI: 10.1097/mpa.0000000000002289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
OBJECTIVES Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. MATERIALS AND METHODS Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. RESULTS Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. CONCLUSIONS Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
- Ruri Yamaguchi
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
| | - Hiromu Morikawa
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Jun Akatsuka
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Yasushi Numata
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo
| | - Aya Noguchi
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Takashi Kokumai
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masaharu Ishida
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Masamichi Mizuma
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Kei Nakagawa
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Michiaki Unno
- Department of Surgery, Tohoku University Graduate School of Medicine
| | - Akimitsu Miyake
- Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, Sendai
| | | | | | - Toru Furukawa
- From the Department of Investigative Pathology, Tohoku University Graduate School of Medicine, Sendai
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Elshaarawy O, Alboraie M, El-Kassas M. Artificial Intelligence in endoscopy: A future poll. Arab J Gastroenterol 2024; 25:13-17. [PMID: 38220477 DOI: 10.1016/j.ajg.2023.11.008] [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: 11/20/2020] [Revised: 09/18/2022] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and computer-assisted diagnosis [CADx] for optical biopsy and lesion characterization. The technology behind these systems is based on a computer algorithm that is trained for a specific function. This function could be to recognize or characterize target lesions such as colonic polyps. Moreover, AI systems can offer technical assistance to improve endoscopic performance as scope insertion guidance. Currently, we believe that such technologies still lack legal and regulatory validations as a large sector of doctors and patients have concerns. However, there is no doubt that these technologies will bring significant improvement in the endoscopic management of patients as well as save money and time.
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Affiliation(s)
- Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt; Gastroenterology Department, Royal Liverpool University Hospital, NHS, UK
| | - Mohamed Alboraie
- Department of Internal Medicine, Al-Azhar University, Cairo, Egypt
| | - Mohamed El-Kassas
- Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt.
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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Zhang H, Wu Q, Sun J, Wang J, Zhou L, Cai W, Zou D. A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial. Front Med (Lausanne) 2024; 10:1341259. [PMID: 38327275 PMCID: PMC10847558 DOI: 10.3389/fmed.2023.1341259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 12/28/2023] [Indexed: 02/09/2024] Open
Abstract
Background Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists' performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting. Methods The CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18-75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists. Results A total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p < 0.001), and the specificity of the endoscopists in these two groups was comparable (100.00 vs. 100.00%). In a subgroup analysis, the CADe system improved the PDR of the 6-9 mm polyps (18.04 vs. 13.85%, p < 0.05) and reduced the miss detection rate, especially at 10:00-12:00 am (12.5 vs. 39.81%, p < 0.001). Conclusion The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC. Registration This clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988). Clinical trial registration website www.chictr.org.cn, identifier ChiCTR2100041988.
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Affiliation(s)
- Heng Zhang
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wu
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing Sun
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wang
- Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Lei Zhou
- Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Cai
- Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Zhu Y, Lyu X, Tao X, Wu L, Yin A, Liao F, Hu S, Wang Y, Zhang M, Huang L, Wang J, Zhang C, Gong D, Jiang X, Zhao L, Yu H. A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination. BMC Gastroenterol 2024; 24:10. [PMID: 38166722 PMCID: PMC10759410 DOI: 10.1186/s12876-023-03067-w] [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: 04/08/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.
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Affiliation(s)
- 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
| | - Xiaoguang Lyu
- 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
| | - 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
| | - Anning Yin
- 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
| | - Fei Liao
- 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
| | - Shan Hu
- School of Computer Science, Wuhan University, Wuhan, China
| | - Yang Wang
- 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
| | - Mengjiao Zhang
- 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
| | - 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
| | - Junxiao Wang
- 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
| | - Chenxia Zhang
- 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
| | - Dexin Gong
- 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
| | - Liang Zhao
- 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.
| | - 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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Su X, Liu W, Jiang S, Gao X, Chu Y, Ma L. Deep learning-based anatomical position recognition for gastroscopic examination. Technol Health Care 2024; 32:39-48. [PMID: 38669495 PMCID: PMC11191429 DOI: 10.3233/thc-248004] [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] [Indexed: 04/28/2024]
Abstract
BACKGROUND The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived images. These requirements are challenging for the education and training of junior doctors. OBJECTIVE The purpose of this study is to use deep learning to develop automatic position recognition technology for gastroscopic examination. METHODS A total of 17182 gastroscopic images in eight anatomical position categories are collected. Convolutional neural network model MogaNet is used to identify all the anatomical positions of the stomach for gastroscopic examination The performance of four models is evaluated by sensitivity, precision, and F1 score. RESULTS The average sensitivity of the method proposed is 0.963, which is 0.074, 0.066 and 0.065 higher than ResNet, GoogleNet and SqueezeNet, respectively. The average precision of the method proposed is 0.964, which is 0.072, 0.067 and 0.068 higher than ResNet, GoogleNet, and SqueezeNet, respectively. And the average F1-Score of the method proposed is 0.964, which is 0.074, 0.067 and 0.067 higher than ResNet, GoogleNet, and SqueezeNet, respectively. The results of the t-test show that the method proposed is significantly different from other methods (p< 0.05). CONCLUSION The method proposed exhibits the best performance for anatomical positions recognition. And the method proposed can help junior doctors meet the requirements of completeness of gastroscopic examination and the number and position of archived images quickly.
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Affiliation(s)
- Xiufeng Su
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China
| | - Weiyu Liu
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China
| | - Suyi Jiang
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China
| | - Xiaozhong Gao
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China
| | - Yanliu Chu
- Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong, China
| | - Liyong Ma
- School of Information Science and Engineering, Harbin Institute of Technology, Weihai, Shandong, China
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Kimura N, Takahashi K, Setsu T, Horibata Y, Kaneko Y, Miyazaki H, Ogawa K, Kawata Y, Sakai N, Watanabe Y, Abe H, Kamimura H, Sakamaki A, Yokoo T, Kamimura K, Tsuchiya A, Terai S. Development and validation of machine learning model for predicting treatment responders in patients with primary biliary cholangitis. Hepatol Res 2024; 54:67-77. [PMID: 37691006 DOI: 10.1111/hepr.13966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/24/2023] [Accepted: 08/31/2023] [Indexed: 09/12/2023]
Abstract
AIMS Ursodeoxycholic acid is the first-line treatment for primary biliary cholangitis, and treatment response is one of the factors predicting the outcome. To prescribe alternative therapies, clinicians might need additional information before deciphering the treatment response to ursodeoxycholic acid, contributing to a better patient prognosis. In this study, we developed and validated machine learning (ML) algorithms to predict treatment responses using pretreatment data. METHODS This multicenter cohort study included collecting datasets from two data samples. Data 1 included 245 patients from 18 hospitals for ML development, and was divided into (i) training and (ii) development sets. Data 2 (iii: test set) included 51 patients from our hospital for validation. An extreme gradient boosted tree predicted the treatment response in the ML model. The area under the curve was used to evaluate the efficacy of the algorithm. RESULTS Data 1 showed that patients complying with the Paris II treatment response had significantly lower serum alkaline phosphatase and total bilirubin levels than those who did not respond. Three factors, total bilirubin, total protein, and alanine aminotransferase levels were selected as essential variables for prediction. Data 2 showed that patients complying with the Paris II criteria had significantly high prothrombin time and low total bilirubin levels. The area under the curve of extreme gradient boosted tree was good for (ii) (0.811) and (iii) (0.856). CONCLUSIONS We demonstrated the efficacy of ML in predicting the treatment response for patients with primary biliary cholangitis. Early identification of cases requiring additional treatment with our novel ML model may improve prognosis.
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Affiliation(s)
- Naruhiro Kimura
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kazuya Takahashi
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Toru Setsu
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yusuke Horibata
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yusuke Kaneko
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Haruka Miyazaki
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kohei Ogawa
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuzo Kawata
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Norihiro Sakai
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yusuke Watanabe
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroyuki Abe
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroteru Kamimura
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Akira Sakamaki
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takeshi Yokoo
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kenya Kamimura
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Atsunori Tsuchiya
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shuji Terai
- Division of Gastroenterology and Hepatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Siddiqui F, Aslam D, Tanveer K, Soudy M. The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. STUDIES IN COMPUTATIONAL INTELLIGENCE 2024:61-75. [DOI: 10.1007/978-981-99-9029-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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Lu MY, Huang CF, Hung CH, Tai C, Mo LR, Kuo HT, Tseng KC, Lo CC, Bair MJ, Wang SJ, Huang JF, Yeh ML, Chen CT, Tsai MC, Huang CW, Lee PL, Yang TH, Huang YH, Chong LW, Chen CL, Yang CC, Yang S, Cheng PN, Hsieh TY, Hu JT, Wu WC, Cheng CY, Chen GY, Zhou GX, Tsai WL, Kao CN, Lin CL, Wang CC, Lin TY, Lin C, Su WW, Lee TH, Chang TS, Liu CJ, Dai CY, Kao JH, Lin HC, Chuang WL, Peng CY, Tsai CW, Chen CY, Yu ML. Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program. Clin Mol Hepatol 2024; 30:64-79. [PMID: 38195113 PMCID: PMC10776298 DOI: 10.3350/cmh.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/02/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND/AIMS Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
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Affiliation(s)
- Ming-Ying Lu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chung-Feng Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
| | - Chao-Hung Hung
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chi‐Ming Tai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Lein-Ray Mo
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
| | - Hsing-Tao Kuo
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
| | - Kuo-Chih Tseng
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
| | - Ching-Chu Lo
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
| | - Ming-Jong Bair
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
| | - Szu-Jen Wang
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
| | - Jee-Fu Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Lun Yeh
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Ting Chen
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Chang Tsai
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chien-Wei Huang
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
| | - Pei-Lun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | | | - Yi-Hsiang Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Lee-Won Chong
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Chien-Lin Chen
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
| | - Chi-Chieh Yang
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Sheng‐Shun Yang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Pin-Nan Cheng
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tsai-Yuan Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jui-Ting Hu
- Liver Center, Cathay General Hospital, Taipei, Taiwan
| | - Wen-Chih Wu
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
| | - Chien-Yu Cheng
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Guei-Ying Chen
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
| | | | - Wei-Lun Tsai
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chien-Neng Kao
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
| | - Chih-Lang Lin
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
| | - Chia-Chi Wang
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
| | - Ta-Ya Lin
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
| | - Chih‐Lin Lin
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
| | - Wei-Wen Su
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
| | - Tzong-Hsi Lee
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Te-Sheng Chang
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chun-Jen Liu
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chia-Yen Dai
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jia-Horng Kao
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Chieh Lin
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Wan-Long Chuang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Cheng-Yuan Peng
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Chun-Wei- Tsai
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Chi-Yi Chen
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
| | - Ming-Lung Yu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - TACR Study Group
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
- Lotung Poh-Ai Hospital, Yilan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Liver Center, Cathay General Hospital, Taipei, Taiwan
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
- Zhou Guoxiong Clinic, Penghu, Taiwan
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
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Mahoney LB, Huang JS, Lightdale JR, Walsh CM. Pediatric endoscopy: how can we improve patient outcomes and ensure best practices? Expert Rev Gastroenterol Hepatol 2024; 18:89-102. [PMID: 38465446 DOI: 10.1080/17474124.2024.2328229] [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: 10/25/2023] [Accepted: 03/05/2024] [Indexed: 03/12/2024]
Abstract
INTRODUCTION Strategies to promote high-quality endoscopy in children require consensus around pediatric-specific quality standards and indicators. Using a rigorous guideline development process, the international Pediatric Endoscopy Quality Improvement Network (PEnQuIN) was developed to support continuous quality improvement efforts within and across pediatric endoscopy services. AREAS COVERED This review presents a framework, informed by the PEnQuIN guidelines, for assessing endoscopist competence, granting procedural privileges, audit and feedback, and for skill remediation, when required. As is critical for promoting quality, PEnQuIN indicators can be benchmarked at the individual endoscopist, endoscopy facility, and endoscopy community levels. Furthermore, efforts to incorporate technologies, including electronic medical records and artificial intelligence, into endoscopic quality improvement processes can aid in creation of large-scale networks to facilitate comparison and standardization of quality indicator reporting across sites. EXPERT OPINION PEnQuIN quality standards and indicators provide a framework for continuous quality improvement in pediatric endoscopy, benefiting individual endoscopists, endoscopy facilities, and the broader endoscopy community. Routine and reliable measurement of data, facilitated by technology, is required to identify and drive improvements in care. Engaging all stakeholders in endoscopy quality improvement processes is crucial to enhancing patient outcomes and establishing best practices for safe, efficient, and effective pediatric endoscopic care.
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Affiliation(s)
- Lisa B Mahoney
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA
| | - Jeannie S Huang
- Rady Children's Hospital, San Diego, CA and University of California San Diego, La Jolla, CA, USA
| | - Jenifer R Lightdale
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, MA, USA
| | - Catharine M Walsh
- Division of Gastroenterology, Hepatology and Nutrition and the Research and Learning Institutes, The Hospital for Sick Children, Department of Paediatrics and the Wilson Centre, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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Tsai MC, Yen HH, Tsai HY, Huang YK, Luo YS, Kornelius E, Sung WW, Lin CC, Tseng MH, Wang CC. Artificial intelligence system for the detection of Barrett's esophagus. World J Gastroenterol 2023; 29:6198-6207. [PMID: 38186865 PMCID: PMC10768395 DOI: 10.3748/wjg.v29.i48.6198] [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: 09/03/2023] [Revised: 11/13/2023] [Accepted: 12/12/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Barrett's esophagus (BE), which has increased in prevalence worldwide, is a precursor for esophageal adenocarcinoma. Although there is a gap in the detection rates between endoscopic BE and histological BE in current research, we trained our artificial intelligence (AI) system with images of endoscopic BE and tested the system with images of histological BE. AIM To assess whether an AI system can aid in the detection of BE in our setting. METHODS Endoscopic narrow-band imaging (NBI) was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital, resulting in 724 cases, with 86 patients having pathological results. Three senior endoscopists, who were instructing physicians of the Digestive Endoscopy Society of Taiwan, independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE. The test set consisted of 160 endoscopic images of 86 cases with histological results. RESULTS Six pre-trained models were compared, and EfficientNetV2B2 (accuracy [ACC]: 0.8) was selected as the backbone architecture for further evaluation due to better ACC results. In the final test, the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE, resulting in an ACC of 94.37%. CONCLUSION Our AI system, which was trained by NBI of endoscopic BE, can adequately predict endoscopic images of histological BE. The ACC, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively.
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Affiliation(s)
- Ming-Chang Tsai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
| | - Hsu-Heng Yen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 500, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 400, Taiwan
| | - Hui-Yu Tsai
- Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
| | - Yu-Kai Huang
- Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Yu-Sin Luo
- Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Edy Kornelius
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
- Department of Endocrinology and Metabolism, Chung-Shan Medical University Hospital, Taichung 402, Taiwan
| | - Wen-Wei Sung
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
- Department of Urology, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Chun-Che Lin
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
| | - Ming-Hseng Tseng
- Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
- Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Chi-Chih Wang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
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Wei MT, Chen Y, Quan SY, Pan JY, Wong RJ, Friedland S. Evaluation of computer aided detection during colonoscopy among Veterans: Randomized clinical trial. Artif Intell Med Imaging 2023; 4:1-9. [DOI: 10.35711/aimi.v4.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 10/10/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND There has been significant interest in use of computer aided detection (CADe) devices in colonoscopy to improve polyp detection and reduce miss rate.
AIM To investigate the use of CADe amongst veterans.
METHODS Between September 2020 and December 2021, we performed a randomized controlled trial to evaluate the impact of CADe. Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe. Primary outcomes of interest included adenoma detection rate (ADR), adenomas per colonoscopy (APC), and adenomas per extraction. In addition, we measured serrated polyps per colonoscopy, non-adenomatous, non-serrated polyps per colonoscopy, serrated polyp detection rate, and procedural time.
RESULTS A total of 244 patients were enrolled (124 with CADe), with similar patient characteristics (age, sex, body mass index, indication) between the two groups. Use of CADe was found to have decreased number of adenomas (1.79 vs 2.53, P = 0.030) per colonoscopy compared to without CADe. There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups. Overall, use of CADe was found to have lower ADR (68.5% vs 80.0%, P = 0.041) compared to without use of CADe. Serrated polyp detection rate was lower with CADe (3.2% vs 7.5%) compared to without CADe, but this was not statistically significant (P = 0.137). There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction (71.4% vs 73.1%, P = 0.613). No adverse events were identified.
CONCLUSION While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe, in this RCT performed at a center with high ADR, use of CADe was found to have decreased APC and ADR. Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.
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Affiliation(s)
- Mike T Wei
- Department of Gastroenterology and Hepatology, Stanford University Medical Center, Palo Alto, CA 94305, United States
| | - Yu Chen
- Department of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94305, United States
| | - Susan Y Quan
- Department of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94305, United States
| | - Jennifer Y Pan
- Department of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94305, United States
| | - Robert J Wong
- Department of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94305, United States
| | - Shai Friedland
- Department of Gastroenterology and Hepatology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94305, United States
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Yadav A, Kumar A. Artificial intelligence in rectal cancer: What is the future? Artif Intell Cancer 2023; 4:11-22. [DOI: 10.35713/aic.v4.i2.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 12/07/2023] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.
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Affiliation(s)
- Alka Yadav
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
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Carter D, Albshesh A, Shimon C, Segal B, Yershov A, Kopylov U, Meyers A, Brzezinski RY, Ben Horin S, Hoffer O. Automatized Detection of Crohn's Disease in Intestinal Ultrasound Using Convolutional Neural Network. Inflamm Bowel Dis 2023; 29:1901-1906. [PMID: 36794834 DOI: 10.1093/ibd/izad014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Indexed: 02/17/2023]
Abstract
INTRODUCTION The use of intestinal ultrasound (IUS) for the diagnosis and follow-up of inflammatory bowel disease is steadily growing. Although access to educational platforms of IUS is feasible, novice ultrasound operators lack experience in performing and interpreting IUS. An artificial intelligence (AI)-based operator supporting system that automatically detects bowel wall inflammation may simplify the use of IUS by less experienced operators. Our aim was to develop and validate an artificial intelligence module that can distinguish bowel wall thickening (a surrogate of bowel inflammation) from normal bowel images of IUS. METHODS We used a self-collected image data set to develop and validate a convolutional neural network module that can distinguish bowel wall thickening >3 mm (a surrogate of bowel inflammation) from normal bowel images of IUS. RESULTS The data set consisted of 1008 images, distributed uniformly (50% normal images, 50% abnormal images). Execution of the training phase and the classification phase was performed using 805 and 203 images, respectively. The overall accuracy, sensitivity, and specificity for detection of bowel wall thickening were 90.1%, 86.4%, and 94%, respectively. The network exhibited an average area under the ROC curve of 0.9777 for this task. CONCLUSIONS We developed a machine-learning module based on a pretrained convolutional neural network that is highly accurate in the recognition of bowel wall thickening on intestinal ultrasound images in Crohn's disease. Incorporation of convolutional neural network to IUS may facilitate the use of IUS by inexperienced operators and allow automatized detection of bowel inflammation and standardization of IUS imaging interpretation.
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Affiliation(s)
- Dan Carter
- Institute of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ahmed Albshesh
- Institute of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Carmi Shimon
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Batel Segal
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Alex Yershov
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Uri Kopylov
- Institute of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adele Meyers
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Rafael Y Brzezinski
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
| | - Shomron Ben Horin
- Institute of Gastroenterology, Chaim Sheba Medical Center, Ramat Gan, Israel
- Sackler faculty of medicine, Tel Aviv University, Tel Aviv, Israel
| | - Oshrit Hoffer
- School of Electrical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel
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Wazir H, Abid M, Essani B, Saeed H, Ahmad Khan M, Nasrullah F, Qadeer U, Khalid A, Varrassi G, Muzammil MA, Maryam A, Syed ARS, Shah AA, Kinger S, Ullah F. Diagnosis and Treatment of Liver Disease: Current Trends and Future Directions. Cureus 2023; 15:e49920. [PMID: 38174191 PMCID: PMC10763979 DOI: 10.7759/cureus.49920] [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: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
This narrative review delves into the intricate landscape of liver diseases, providing a comprehensive background of the diverse conditions that afflict this vital organ. Liver diseases, ranging from viral hepatitis and non-alcoholic fatty liver disease (NAFLD) to cirrhosis and hepatocellular carcinoma (HCC), pose significant global health challenges. Understanding these diseases' multifaceted origins and progression is pivotal for developing effective diagnostic and therapeutic strategies. The epidemiology and etiology of liver diseases emphasize the global impact of viral hepatitis, with hepatitis B and C as significant contributors. Concurrently, the rising prevalence of NAFLD, linked to lifestyle factors and metabolic syndrome, underscores the intricate relationship between modern living and liver health. Chronic liver diseases often evolve insidiously, progressing from inflammation to fibrosis and, ultimately, to cirrhosis - a stage characterized by irreversible scarring and compromised function. The heightened risk of HCC in advanced liver disease stages further underscores the urgency of effective diagnostic and therapeutic interventions. The evolving landscape of non-invasive diagnostic tools is explored for their role in enabling early detection and accurate staging of liver diseases. In the realm of treatment, there is a continuous transition toward personalized medicine, customized to suit the unique profiles of individual patients. This shift encompasses a broad spectrum, ranging from personalized pharmacological interventions to lifestyle modifications and surgical options. Delving into innovative therapies, such as gene editing and immunomodulation, offers a glimpse into the promising future directions that have the potential to redefine the landscape of liver disease diagnosis and treatment.
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Affiliation(s)
- Hina Wazir
- Internal Medicine, Khyber Medical College, Peshawar, PAK
| | - Marium Abid
- Medicine, Jinnah Medical and Dental College, Karachi, PAK
| | - Binish Essani
- Medicine, Jinnah Medical and Dental College, Karachi, PAK
| | - Hira Saeed
- Medicine, Federal Medical College, Islamabad, PAK
| | | | - Fnu Nasrullah
- Internal Medicine, Shadab Medical Center, Karachi, PAK
| | - Usama Qadeer
- Medicine, Allama Iqbal Medical College, Lahore, PAK
| | - Ayesha Khalid
- Medicine, Fatima Memorial Hospital College of Medicine and Dentistry, Lahore, PAK
| | | | | | - Areeba Maryam
- Emergency Medicine, Holy Family Hospital, Rawalpindi, PAK
| | | | - Abdul Ahad Shah
- Dermatology, Medical College, Dow University of Health Sciences, Karachi, PAK
| | | | - Farhan Ullah
- Internal Medicine, Khyber Teaching Hospital, Peshawar, PAK
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