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Tan X, Deng M, Fang Z, Yang Q, Zhang M, Wu J, Chen W. A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis. Heliyon 2024; 10:e30281. [PMID: 38726150 PMCID: PMC11079104 DOI: 10.1016/j.heliyon.2024.e30281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Background The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system. Methods The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve. Results Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified. Conclusion The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.
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Affiliation(s)
- Xiaoli Tan
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Min Deng
- Department of Infectious Diseases, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Zhixian Fang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Qi Yang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Ming Zhang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jiasheng Wu
- Department of Respiratory and Critical Care Medicine, Jiaxing Second Hospital, Jiaxing, China
- Department of Respiratory Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wenyu Chen
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
- Department of Respiratory Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Cai F, Cheng L, Liao X, Xie Y, Wang W, Zhang H, Lu J, Chen R, Chen C, Zhou X, Mo X, Hu G, Huang L. An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion. Respiration 2024; 103:406-416. [PMID: 38422997 DOI: 10.1159/000536517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate between MPE and BPE. METHODS A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection processes were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest were employed to construct radiomic models. LR analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). RESULTS Out of the total 1,834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomic model. Among the radiomic models, the SVM model demonstrated the highest predictive performance [area under the curve (AUC), training cohort: 0.876, test cohort: 0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin, carcinoembryonic antigen, carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC: 0.932, 0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC: 0.850, 0.820) and the radiomic model (AUC: 0.876, 0.774). The calibration curves and DCA further confirmed the practicality of the combined model. CONCLUSION This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool for assisting in the clinical diagnosis of PE patients.
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Affiliation(s)
- Fangqi Cai
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China,
| | - Liwei Cheng
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaoling Liao
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yuping Xie
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Wu Wang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Haofeng Zhang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Jinhua Lu
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ru Chen
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Chunxia Chen
- Department of Clinical Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xing Zhou
- Department of Clinical Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xiaoyun Mo
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Guoping Hu
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Luying Huang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Zhu F, Yang C, Zou J, Ma W, Wei Y, Zhao Z. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis. Acta Radiol 2023; 64:3074-3084. [PMID: 37817511 DOI: 10.1177/02841851231205737] [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: 10/12/2023]
Abstract
Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.
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Affiliation(s)
- Fandong Zhu
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Chen Yang
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Jiajun Zou
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Weili Ma
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Hangzhou, Zhejiang, PR China
| | - Zhenhua Zhao
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
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Shi L, Sheng M, Wei Z, Liu L, Zhao J. CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis. Acad Radiol 2023; 30:3064-3075. [PMID: 37385850 DOI: 10.1016/j.acra.2023.05.026] [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: 04/21/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023]
Abstract
RATIONALE AND OBJECTIVES More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies. MATERIALS AND METHODS PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity. RESULTS In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs. CONCLUSION CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China (M.S.)
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Lei Liu
- Institutes of Intelligence Medicine, Fudan University, Shanghai, China (L.L.)
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China (J.Z.).
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Tan M, Ma W, Yang Y, Duan S, Jin L, Wu Y, Li M. Predictive value of peritumour radiomics in the diagnosis of benign and malignant pulmonary nodules with halo sign. Clin Radiol 2023; 78:e52-e62. [PMID: 36460488 DOI: 10.1016/j.crad.2022.09.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/05/2022] [Accepted: 09/26/2022] [Indexed: 12/03/2022]
Abstract
AIM To evaluate peritumour radiomics in predicting benign and malignant pulmonary nodules with halo sign. MATERIALS AND METHODS In this retrospective study, 305 pulmonary nodules with halo sign (benign, 120; adenocarcinoma, 185) were collected. Manual segmentation was used to mark the gross tumour volume (GTV) and the peritumour volume (PTV) was established by uniform dilation (1 cm) of the tumour area in three dimensions. The GTV and PTV radiomic features were combined to produce the gross tumour and peritumour volume (GPTV). The minimum-redundancy maximum-relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) algorithm were used to eliminate redundant radiomic features. Predictive models combined with clinical features and radiomic signatures were established. Multivarible logistic regression analysis was used to establish the combined model and develop a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the model. RESULTS In the testing cohort, the area under the ROC curve (AUC) of the GTV, PTV, and GPTV radiomic models was 0.701 (95% CI: 0.589-0.814), 0.674 (95% CI: 0.557-0.791) and 0.755 (95% CI: 0.643-0.867), respectively. The AUC of the nomogram model based on clinical and GPTV radiomic signatures was 0.804 (95% CI: 0.707-0.901). CONCLUSION The nomogram model based on clinical and GPTV radiomic signatures can better predict benign and malignant pulmonary nodules with halo signs, demonstrating that the model has potential as a convenient and effective auxiliary diagnostic tool for radiologists.
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Affiliation(s)
- M Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Chengdu Second People's Hospital, Chengdu, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Y Yang
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Y Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Yi L, Peng Z, Chen Z, Tao Y, Lin Z, He A, Jin M, Peng Y, Zhong Y, Yan H, Zuo M. Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features. Front Oncol 2022; 12:924055. [PMID: 36147924 PMCID: PMC9485677 DOI: 10.3389/fonc.2022.924055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/22/2022] [Indexed: 11/17/2022] Open
Abstract
To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.
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Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective. Semin Nucl Med 2020; 50:541-548. [PMID: 33059823 DOI: 10.1053/j.semnuclmed.2020.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms.
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Affiliation(s)
- Jérémy Dana
- IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France
| | - Vincent Agnus
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Farid Ouhmich
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Benoit Gallix
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France; Faculty of Medicine, University of Strasbourg, Strasbourg, France; Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
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