scholarly journals Can Computed Tomography-Based Radiomics Potentially Discriminate Between Anterior Mediastinal Cysts and type B1 and B2 thymomas?

2020 ◽  
Author(s):  
Lulu Liu ◽  
Fangxiao Lu ◽  
Peipei Pang ◽  
Guoliang Shao
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Texture Features ◽  
Training Dataset ◽  
Features Selection ◽  
Clinical Factors ◽  
Combined Model ◽  
Test Dataset ◽  
Enhanced Ct ◽  
Mediastinal Cysts

Abstract Background: Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resources waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.Methods: A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the Artificial Intelligence Kit (AK) software. A total of 396 tumor texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis and LASSO were used to features selection and then the radiomics signature (radscore) were obtained. The combined model including radscore and independent clinical factors were developed. The model performances were evaluated on discrimination, calibration curve.Results: Two radscore model were constructed from the unenhanced and enhanced phases based on the selected 4 and 3 features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3% and 83.8% in the training dataset and 0.899, 84.6%, 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions: The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

scholarly journals Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Lulu Liu ◽  
Fangxiao Lu ◽  
Peipei Pang ◽  
Guoliang Shao
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Texture Features ◽  
Training Dataset ◽  
Features Selection ◽  
Clinical Factors ◽  
Combined Model ◽  
Test Dataset ◽  
Enhanced Ct ◽  
Mediastinal Cysts

Abstract Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

scholarly journals Can Computed Tomography-Based Radiomics Potentially Discrimination Between Anterior Mediastinal Cysts and type B1 and B2 thymomas

2020 ◽  
Author(s):  
Lulu Liu ◽  
Fangxiao Lu ◽  
Peipei Pang ◽  
Guoliang Shao
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Texture Features ◽  
Training Dataset ◽  
Features Selection ◽  
Clinical Factors ◽  
Combined Model ◽  
Test Dataset ◽  
Enhanced Ct ◽  
Mediastinal Cysts

Abstract Background Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resources waste. The purpose of this study was to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods A group of 188 patients with pathologically confirmed AMC (106 cases mischarged as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the Artificial Intelligence Kit (AK) software. A total of 396 tumor texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis and LASSO were used to features selection and then the radiomics signature (radscore) were obtained. The combined model including radscore and independent clinical factors were developed. The model performances were evaluated on discrimination, calibration curve. Results Two radscore model were constructed from the unenhanced and enhanced phases based on the selected 4 and 3 features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3% and 83.8% in the training dataset and 0.899, 84.6%, 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

scholarly journals Can Computed Tomography-Based Radiomics Potentially Discriminate Between Anterior Mediastinal Cysts and Type B1 and B2 Thymomas

2020 ◽  
Author(s):  
Lulu Liu ◽  
Fangxiao Lu ◽  
Peipei Pang ◽  
Guoliang Shao
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Texture Features ◽  
Training Dataset ◽  
Features Selection ◽  
Clinical Factors ◽  
Combined Model ◽  
Test Dataset ◽  
Enhanced Ct ◽  
Mediastinal Cysts

Abstract Background: Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resources waste. The purpose of this study was to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. Methods: A group of 188 patients with pathologically confirmed AMC (106 cases mischarged as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the Artificial Intelligence Kit (AK) software. A total of 396 tumor texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis and LASSO were used to features selection and then the radiomics signature (radscore) were obtained. The combined model including radscore and independent clinical factors were developed. The model performances were evaluated on discrimination, calibration curve. Results: Two radscore model were constructed from the unenhanced and enhanced phases based on the selected 4 and 3 features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3% and 83.8% in the training dataset and 0.899, 84.6%, 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). Conclusions: The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

scholarly journals Development and Validation of a Novel Diagnostic Model for Initially Clinical Diagnosed Gastrointestinal Stromal Tumors Using An Extreme Gradient-Boosting Machine

2021 ◽  
Author(s):  
Bozhi Hu ◽  
Chao Wang ◽  
Kewei Jiang ◽  
Zhanlong Shen ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Partial Gastrectomy ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Diagnostic Model ◽  
Gastric Gist ◽  
Test Dataset ◽  
Ct Value ◽  
Enhanced Ct ◽  
Extreme Gradient Boosting ◽  
Hematological Indicators

Abstract INTRODUCTION Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients. METHODS We used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset. RESULTS In the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25–37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST. CONCLUSIONS We developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.

scholarly journals Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 928
Author(s):  
Mohammad W. Sahar ◽  
Annabelle Beaver ◽  
Marina A. G. von Keyserlingk ◽  
Daniel M. Weary
Keyword(s):  
Dairy Cattle ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Predictive Value ◽  
Predictive Ability ◽  
Training Dataset ◽  
Test Dataset ◽  
Increased Risk ◽  
High Prevalence ◽  
Random Method

Dairy cattle are particularly susceptible to metritis, hyperketonemia (HYK), and mastitis in the weeks after calving. These high-prevalence transition diseases adversely affect animal welfare, milk production, and profitability. Our aim was to use prepartum behavior to predict which cows have an increased risk of developing these conditions after calving. The behavior of 213 multiparous and 105 primiparous Holsteins was recorded for approximately three weeks before calving by an electronic feeding system. Cows were also monitored for signs of metritis, HYK, and mastitis in the weeks after calving. The data were split using a stratified random method: we used 70% of our data (hereafter referred to as the “training” dataset) to develop the model and the remaining 30% of data (i.e., the “test” dataset) to assess the model’s predictive ability. Separate models were developed for primiparous and multiparous animals. The area under the receiver operating characteristic (ROC) curve using the test dataset for multiparous cows was 0.83, sensitivity and specificity were 73% and 80%, positive predictive value (PPV) was 73%, and negative predictive value (NPV) was 80%. The area under the ROC curve using the test dataset for primiparous cows was 0.86, sensitivity and specificity were 71% and 84%, PPV was 77%, and NPV was 80%. We conclude that prepartum behavior can be used to predict cows at risk of metritis, HYK, and mastitis after calving.

scholarly journals A Novel Nomogram Combined With Radiomics Features, Age and Albuminuria to Predict the Pathological Grade of Bladder Cancer Running Title: Application Study on the Prediction Model of Bladder Cancer Pathological Grade Based on Parameters of Thin-layer Enhanced CT. Author Information

2021 ◽  
Author(s):  
Qi Zhou ◽  
Lu Ma ◽  
Haoyang Zhang ◽  
Xiaojie Ang ◽  
Can Hu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Urothelial Carcinoma ◽  
Texture Features ◽  
Comprehensive Model ◽  
Pathological Grade ◽  
Lasso Regression ◽  
Clinical Factors ◽  
Enhanced Ct

Abstract Background: Based on multi-parameter thin-slice enhanced CT texture features and related clinical indicators, a preoperative pathological grade prediction model of bladder urothelial carcinoma was established.Methods: The CT images and clinical data of 372 patients with urothelial carcinoma in our hospital from January 2015 to October 2020 were collected. 372 patients were divided into two groups: HGUC(n=190) and LGUC(n=182). All patients were divided into 10 groups on average, of which 7 were used as training group (n=259) and the remaining 3 as verification group (n=113). Then, by using 3D-Slicer software from all enhanced in patients with preoperative CT images (Arterial and Venous phase calibration chart) split out the region of interest (ROI), respectively from the tumor image data extraction based on First-order and Second-order, High-order and filtering characteristics of 1223 texture features, and use the inter/intra-class correlation coefficient(ICC > 0.75) between classes and least absolute shrinkage selection operator (LASSO) regression feature selection; Secondly, the clinical effective factors were obtained by logistic regression analysis, and the clinical predictive model was constructed. Finally, the selected clinical key indicators and radiomics features were mapped. In order to verify the predictive ability of the nomogram, conformance index (C-index), calibration curve, Receiver operator characteristic (ROC) curve and clinical decision curve analysis (DCA) were used to test the nomogram.Results: Lasso regression analysis showed that 11 radiomics features were significantly correlated with the pathological grade of bladder cancer. After comparing the four models, it is found that Logistic regression model has the best prediction ability (AUC=0.858). The results of multivariate analysis showed that age and albuminuria were independent influencing factors. A comprehensive model for predicting the pathological grade of bladder cancer (radiomics + clinical) was constructed by combining clinical independent risk factors with 11 radiomics features. Compared with clinical feature model and radiomics model, it was found that the predictive performance of imaging comprehensive model combined with clinical factors was the best (AUC=0.864).Conclusions: The radiomics model based on multi-parameter thin-layer enhanced CT, combined with clinical factors, can effectively predict high-and low-grade urothelial carcinoma.

scholarly journals Computed Tomography-Based Radiomics for Preoperative Prediction of Tumor Deposits in Rectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yumei Jin ◽  
Mou Li ◽  
Yali Zhao ◽  
Chencui Huang ◽  
Siyun Liu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Rectal Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Characteristic Curve ◽  
Clinical Factors ◽  
Combined Model ◽  
Portal Venous Phase ◽  
Preoperative Prediction

ObjectiveTo develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC).MethodsThis retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC).ResultsOne hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P < 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039).ConclusionsThe combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.

scholarly journals Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bozhi Hu ◽  
Chao Wang ◽  
Kewei Jiang ◽  
Zhanlong Shen ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Partial Gastrectomy ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Diagnostic Model ◽  
Gastric Gist ◽  
Test Dataset ◽  
Ct Value ◽  
Enhanced Ct ◽  
Extreme Gradient Boosting ◽  
Hematological Indicators

Abstract Introduction Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal soft tissue tumor. Clinical diagnosis mainly relies on enhanced CT, endoscopy and endoscopic ultrasound (EUS), but the misdiagnosis rate is still high without fine needle aspiration biopsy. We aim to develop a novel diagnostic model by analyzing the preoperative data of the patients. Methods We used the data of patients who were initially diagnosed as gastric GIST and underwent partial gastrectomy. The patients were randomly divided into training dataset and test dataset at a ratio of 3 to 1. After pre-experimental screening, max depth = 2, eta = 0.1, gamma = 0.5, and nrounds = 200 were defined as the best parameters, and in this way we developed the initial extreme gradient-boosting (XGBoost) model. Based on the importance of the features in the initial model, we improved the model by excluding the hematological features. In this way we obtained the final XGBoost model and underwent validation using the test dataset. Results In the initial XGBoost model, we found that the hematological indicators (including inflammation and nutritional indicators) examined before the surgery had little effect on the outcome, so we subsequently excluded the hematological indicators. Similarly, we also screened the features from enhanced CT and ultrasound gastroscopy, and finally determined the 6 most important predictors for GIST diagnosis, including the ratio of long and short diameter under CT, the CT value of the tumor, the enhancement of the tumor in arterial period and venous period, existence of liquid area and calcific area inside the tumor under EUS. Round or round-like tumors with a CT value of around 30 (25–37) and delayed enhancement, as well as liquid but not calcific area inside the tumor best indicate the diagnosis of GIST. Conclusions We developed a model to further differential diagnose GIST from other tumors in initially clinical diagnosed gastric GIST patients by analyzing the results of clinical examinations that most patients should have completed before surgical resection.

scholarly journals Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andra-Iza Iuga ◽  
Heike Carolus ◽  
Anna J. Höink ◽  
Tom Brosch ◽  
Tobias Klinder ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lymph Nodes ◽  
Detection Rate ◽  
Cross Validation ◽  
Bronchial Carcinoma ◽  
Ct Scans ◽  
Training Dataset ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

Abstract Background In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches. Methods The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma. Results The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5–10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%). Conclusions The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.

scholarly journals Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict hospital mortality risk in severe COVID-19 patients

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiude Fan ◽  
Bin Zhu ◽  
Masoud Nouri-Vaskeh ◽  
Chunguo Jiang ◽  
Xiaokai Feng ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Oxygen Saturation ◽  
Sensitivity And Specificity ◽  
Prediction Models ◽  
External Validation ◽  
Risk Scores ◽  
Training Dataset ◽  
Test Dataset ◽  
Risk Of Death ◽  
Neutrophil Percentage

Abstract Background Risk scores are needed to predict the risk of death in severe coronavirus disease 2019 (COVID-19) patients in the context of rapid disease progression. Methods Using data from China (training dataset, n = 96), prediction models were developed by logistic regression and then risk scores were established. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) was used for external validation. Results A NSL model (area under the curve (AUC) 0.932) and a NL model (AUC 0.903) were developed based on neutrophil percentage and lactate dehydrogenase with and without oxygen saturation (SaO2) using the training dataset. AUCs of the NSL and NL models in the test dataset were 0.910 and 0.871, respectively. The risk scoring systems corresponding to these two models were established. The AUCs of the NSL and NL scores in the training dataset were 0.928 and 0.901, respectively. At the optimal cut-off value of NSL score, the sensitivity and specificity were 94% and 82%, respectively. The sensitivity and specificity of NL score were 94% and 75%, respectively. Conclusions These scores may be used to predict the risk of death in severe COVID-19 patients and the NL score could be used in regions where patients' SaO2 cannot be tested.

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