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Chen W, Guo L, Xu H, Dai Y, Yao J, Wang L. NAC1 transcriptional activation of LDHA induces hepatitis B virus immune evasion leading to cirrhosis and hepatocellular carcinoma development. Oncogenesis 2024; 13:15. [PMID: 38704368 PMCID: PMC11069585 DOI: 10.1038/s41389-024-00515-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 04/08/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
Our study aimed to elucidate the molecular mechanisms underlying NAC1 (nucleus accumbens associated 1) transcriptional regulation of LDHA and its role in HBV immune evasion, thus contributing to the development of cirrhosis and hepatocellular carcinoma (HCC). Utilizing public datasets, we performed differential gene expression and weighted gene co-expression network analysis (WGCNA) on HBV-induced cirrhosis/HCC data. We identified candidate genes by intersecting differentially expressed genes with co-expression modules. We validated these genes using the TCGA database, conducting survival analysis to pinpoint key genes affecting HBV-HCC prognosis. We also employed the TIMER database for immune cell infiltration data and analyzed correlations with identified key genes to uncover potential immune escape pathways. In vitro, we investigated the impact of NAC1 and LDHA on immune cell apoptosis and HBV immune evasion. In vivo, we confirmed these findings using an HBV-induced cirrhosis model. Bioinformatics analysis revealed 676 genes influenced by HBV infection, with 475 genes showing differential expression in HBV-HCC. NAC1 emerged as a key gene, potentially mediating HBV immune escape through LDHA transcriptional regulation. Experimental data demonstrated that NAC1 transcriptionally activates LDHA, promoting immune cell apoptosis and HBV immune evasion. Animal studies confirmed these findings, linking NAC1-mediated LDHA activation to cirrhosis and HCC development. NAC1, highly expressed in HBV-infected liver cells, likely drives HBV immune escape by activating LDHA expression, inhibiting CD8 + T cells, and promoting cirrhosis and HCC development.
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Affiliation(s)
- Wenbiao Chen
- Department of Gastroenterology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China
| | - Liliangzi Guo
- Department of Gastroenterology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China
| | - Huixuan Xu
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China
| | - Yong Dai
- Clinical Medical Research Center, Guangdong Provincial Engineering Research Center of Autoimmune Disease Precision Medicine, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China
| | - Jun Yao
- Department of Gastroenterology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China.
| | - Lisheng Wang
- Department of Gastroenterology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, China.
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [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: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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Pitarch C, Ungan G, Julià-Sapé M, Vellido A. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology. Cancers (Basel) 2024; 16:300. [PMID: 38254790 PMCID: PMC10814384 DOI: 10.3390/cancers16020300] [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: 11/09/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
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Affiliation(s)
- Carla Pitarch
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Eurecat, Digital Health Unit, Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Gulnur Ungan
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Margarida Julià-Sapé
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain; (G.U.); (M.J.-S.)
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
| | - Alfredo Vellido
- Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) and Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center, 08034 Barcelona, Spain;
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
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Li Y, Lv X, Chen C, Yu R, Wang B, Wang D, Hou D. A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer. Eur Radiol Exp 2024; 8:2. [PMID: 38169047 PMCID: PMC10761638 DOI: 10.1186/s41747-023-00396-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. METHODS A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. RESULTS We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. CONCLUSIONS The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. RELEVANCE STATEMENT The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. KEY POINTS • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.
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Affiliation(s)
- Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Cancan Chen
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China
| | - Ruize Yu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China
| | - Bing Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, 100025, China.
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
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Rasheed Z, Ma YK, Ullah I, Ghadi YY, Khan MZ, Khan MA, Abdusalomov A, Alqahtani F, Shehata AM. Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques. Brain Sci 2023; 13:1320. [PMID: 37759920 PMCID: PMC10526310 DOI: 10.3390/brainsci13091320] [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: 07/31/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.
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Affiliation(s)
- Zahid Rasheed
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Muhammad Zubair Khan
- Faculty of Basic Sciences, Balochistan University of Information Technology Engineering and Management Sciences, Quetta 87300, Pakistan
| | - Muhammad Abbas Khan
- Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta 87300, Pakistan
| | - Akmalbek Abdusalomov
- Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan;
| | - Fayez Alqahtani
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
| | - Ahmed M. Shehata
- Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menofia 32511, Egypt
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Salehi W, Baglat P, Gupta G, Khan SB, Almusharraf A, Alqahtani A, Kumar A. An Approach to Binary Classification of Alzheimer's Disease Using LSTM. Bioengineering (Basel) 2023; 10:950. [PMID: 37627835 PMCID: PMC10451729 DOI: 10.3390/bioengineering10080950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.
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Affiliation(s)
- Waleed Salehi
- Yogananda School of AI, Shoolini University, Bajhol 173229, India; (W.S.); (G.G.)
| | - Preety Baglat
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), University of Madeira, 9000-082 Funchal, Portugal;
| | - Gaurav Gupta
- Yogananda School of AI, Shoolini University, Bajhol 173229, India; (W.S.); (G.G.)
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK;
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ali Alqahtani
- Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia;
| | - Adarsh Kumar
- School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
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