Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.10.015
YiJie Dong MD , Qing Hua MD , ShuJun Xia MD , CongCong Yuan MD , Cheng Li MD , YanYan Song PhD , YuHang Zheng PhD , RuoLin Tao MD , ZhenHua Liu MD , YuLu Zhang MS , FangGang Wu MS , Wei Guo PhD , Yuan Tian MS , JianQiao Zhou MD
Background
Identifying the subtype of intranodal non-Hodgkin lymphoma (NHL) is crucial for clinical management.
Rationale and Objectives
To display dual-vessel systems (microvascular and microlymphatic circulation) of intranodal NHL using super-resolution ultrasound (SRUS), and explore the diagnostic performance of SRUS imaging for predicting B-cell and T-cell subtypes NHL.
Materials and Methods
A total of 49 patients with intranodal NHL underwent dual-vessel system SRUS imaging via intravenous and intra-lymph node routes. Least absolute shrinkage and selection operator (LASSO) regression, fitted the LASSO model and leave-one-out cross-validation (LOOCV) were used for model development and internal validation.
Results
Among the 49 patients, 40 were diagnosed with B-cell NHL and 9 with T-cell NHL. Variables including LDmax, LDLmin, and VCmin were selected and the logistic regression model achieved discrimination of B-cell and T-cell subtype of lymphoma with an AUC of 0.831 (0.594–0.969).
Conclusion
Dual-vessel SRUS imaging can display real time microvascular and microlymphatic circulation of intranodal NHL in physiological status. With quantitative analysis of SRUS offers a potential non-invasive diagnostic alternative in differentiating NHL subtype.
{"title":"Dual-Vessel Microcirculation Imaging in Discriminating Non-Hodgkin Lymphoma Subtypes Using Super-Resolution Ultrasound: An Exploring Study","authors":"YiJie Dong MD , Qing Hua MD , ShuJun Xia MD , CongCong Yuan MD , Cheng Li MD , YanYan Song PhD , YuHang Zheng PhD , RuoLin Tao MD , ZhenHua Liu MD , YuLu Zhang MS , FangGang Wu MS , Wei Guo PhD , Yuan Tian MS , JianQiao Zhou MD","doi":"10.1016/j.acra.2025.10.015","DOIUrl":"10.1016/j.acra.2025.10.015","url":null,"abstract":"<div><h3>Background</h3><div>Identifying the subtype of intranodal non-Hodgkin lymphoma (NHL) is crucial for clinical management.</div></div><div><h3>Rationale and Objectives</h3><div>To display dual-vessel systems (microvascular and microlymphatic circulation) of intranodal NHL using super-resolution ultrasound (SRUS), and explore the diagnostic performance of SRUS imaging for predicting B-cell and T-cell subtypes NHL.</div></div><div><h3>Materials and Methods</h3><div>A total of 49 patients with intranodal NHL underwent dual-vessel system SRUS imaging via intravenous and intra-lymph node routes. Least absolute shrinkage and selection operator (LASSO) regression, fitted the LASSO model and leave-one-out cross-validation (LOOCV) were used for model development and internal validation.</div></div><div><h3>Results</h3><div>Among the 49 patients, 40 were diagnosed with B-cell NHL and 9 with T-cell NHL. Variables including LDmax, LDLmin, and VCmin were selected and the logistic regression model achieved discrimination of B-cell and T-cell subtype of lymphoma with an AUC of 0.831 (0.594–0.969).</div></div><div><h3>Conclusion</h3><div>Dual-vessel SRUS imaging can display real time microvascular and microlymphatic circulation of intranodal NHL in physiological status. With quantitative analysis of SRUS offers a potential non-invasive diagnostic alternative in differentiating NHL subtype.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 35-46"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.10.023
Jiawei Yi , Ke Bi , Mengjun Shen , Kaiwen Wu , Xinyu Zhao , Runhe Xia , Yang Cong , Yi Li , Yin Wang
Objectives
This study aimed to develop a post-procedural predictive model for assessing the risk of false-negative results in ultrasound-guided percutaneous transthoracic needle lung biopsy (US-PTLB).
Material and Methods
Two prospective cohorts were designed for model development and validation. Patients scheduled for US-PTLB underwent B-mode ultrasound (B-US), color Doppler flow imaging (CDFI), ultrasound elastography, and contrast-enhanced ultrasound (CEUS) of the lesions, with the final diagnosis confirmed through comprehensive evaluation. Risk factors associated with false-negative results were identified, and multivariate logistic regression was used to construct the predictive model. The model's performance was further evaluated in an independent cohort to assess its impact on reducing the incidence of false-negative results through targeted interventions.
Results
The US-PTLB false-negative risk prediction model was constructed using data from 129 patients, of whom 35 (29.1%) were ultimately diagnosed with false-negative results. Predictors included age, lesion size, elasticity score, lesion necrosis, and enhancement intensity on CEUS. The model demonstrated excellent discrimination, with an area under the curve of 0.922, sensitivity of 88.6%, and specificity of 90.4%. Internal validation in 70 independently collected patients confirmed robust model performance. Application of the model in 423 patients, coupled with second biopsies for high-risk patients, led to a significant reduction in the incidence of false-negative results.
Conclusion
This predictive model, combining clinical parameters with multimodal ultrasound features, serves as a robust post-procedural tool for objectively assessing false-negative risk in ultrasound-guided percutaneous transthoracic needle lung biopsy. Its clinical application enables early risk stratification, minimizes false-negative rates, and enhances diagnostic precision.
{"title":"A Predictive Model for False-Negative Results in Ultrasound-Guided Percutaneous Transthoracic Needle Lung Biopsy","authors":"Jiawei Yi , Ke Bi , Mengjun Shen , Kaiwen Wu , Xinyu Zhao , Runhe Xia , Yang Cong , Yi Li , Yin Wang","doi":"10.1016/j.acra.2025.10.023","DOIUrl":"10.1016/j.acra.2025.10.023","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop a post-procedural predictive model for assessing the risk of false-negative results in ultrasound-guided percutaneous transthoracic needle lung biopsy (US-PTLB).</div></div><div><h3>Material and Methods</h3><div>Two prospective cohorts were designed for model development and validation. Patients scheduled for US-PTLB underwent B-mode ultrasound (B-US), color Doppler flow imaging (CDFI), ultrasound elastography, and contrast-enhanced ultrasound (CEUS) of the lesions, with the final diagnosis confirmed through comprehensive evaluation. Risk factors associated with false-negative results were identified, and multivariate logistic regression was used to construct the predictive model. The model's performance was further evaluated in an independent cohort to assess its impact on reducing the incidence of false-negative results through targeted interventions.</div></div><div><h3>Results</h3><div>The US-PTLB false-negative risk prediction model was constructed using data from 129 patients, of whom 35 (29.1%) were ultimately diagnosed with false-negative results. Predictors included age, lesion size, elasticity score, lesion necrosis, and enhancement intensity on CEUS. The model demonstrated excellent discrimination, with an area under the curve of 0.922, sensitivity of 88.6%, and specificity of 90.4%. Internal validation in 70 independently collected patients confirmed robust model performance. Application of the model in 423 patients, coupled with second biopsies for high-risk patients, led to a significant reduction in the incidence of false-negative results.</div></div><div><h3>Conclusion</h3><div>This predictive model, combining clinical parameters with multimodal ultrasound features, serves as a robust post-procedural tool for objectively assessing false-negative risk in ultrasound-guided percutaneous transthoracic needle lung biopsy. Its clinical application enables early risk stratification, minimizes false-negative rates, and enhances diagnostic precision.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 134-146"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.09.037
Chunxiao Wang , Yuxin Li , Yang Ji, Kang Yu, Chunhui Qin, Ling Liu, Yunjia Shuai, Jiahui Chen, Ao Li, Tong Zhang
Background
Determining predictive biomarkers for immunotherapy response in non-small cell lung cancer (NSCLC) patients is a complex task.
Objective
This research aimed to develop a multimodal model (CRDL) integrating clinical data, deep learning (DL), and radiomics (Rad) to predict immune responses in NSCLC patients receiving checkpoint blockade therapies. This study also evaluated whether CRDL outperforms unimodal, pre-fusion models (Pre-FMs) and post-fusion models (Post-FMs).
Methods
This is a retrospective study that utilized data from 228 lung cancer patients at the Memorial Sloan Kettering Cancer Center, with varying Programmed Death-Ligand 1(PD-L1) expression levels among the patients. 228 NSCLC patients were randomly divided into two groups in a 7:3 ratio: the training cohort (n = 159) and the validation cohort (n = 69). Image histological features were extracted using the "PyRadiomics" package, and DL features were obtained through the deep convolutional neural network from chest computed tomography images, and clinical data from the patients were also collected. Feature reduction was performed using t-tests and the Least absolute shrinkage and selection operator regression. Unimodal modal and Pre-FMs were constructed using random forests, while the post-fusion model was developed using a support vector machine approach. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC).
Results
512 DL features and 382 Rad features were extracted. The CRDL model demonstrated superior performance with AUC values of 0.884 in the validation dataset and 0.976 in the training dataset, surpassing the best DL model in both unimodal and pre-fusion settings, which had training and validation AUCs of 0.854 and 0.749.
Conclusion
The CRDL model accurately forecasts immunotherapy responses in NSCLC patients, offering one dependable non-invasive test.
{"title":"Predictions of Response in Non-small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Using Clinical Data, Deep Learning, and Radiomics","authors":"Chunxiao Wang , Yuxin Li , Yang Ji, Kang Yu, Chunhui Qin, Ling Liu, Yunjia Shuai, Jiahui Chen, Ao Li, Tong Zhang","doi":"10.1016/j.acra.2025.09.037","DOIUrl":"10.1016/j.acra.2025.09.037","url":null,"abstract":"<div><h3>Background</h3><div>Determining predictive biomarkers for immunotherapy response in non-small cell lung cancer (NSCLC) patients is a complex task.</div></div><div><h3>Objective</h3><div>This research aimed to develop a multimodal model (CRDL) integrating clinical data, deep learning (DL), and radiomics (Rad) to predict immune responses in NSCLC patients receiving checkpoint blockade therapies. This study also evaluated whether CRDL outperforms unimodal, pre-fusion models (Pre-FMs) and post-fusion models (Post-FMs).</div></div><div><h3>Methods</h3><div>This is a retrospective study that utilized data from 228 lung cancer patients at the Memorial Sloan Kettering Cancer Center, with varying Programmed Death-Ligand 1(PD-L1) expression levels among the patients. 228 NSCLC patients were randomly divided into two groups in a 7:3 ratio: the training cohort (<em>n<!--> </em>=<!--> <!-->159) and the validation cohort (<em>n<!--> </em>=<!--> <!-->69). Image histological features were extracted using the \"PyRadiomics\" package, and DL features were obtained through the deep convolutional neural network from chest computed tomography images, and clinical data from the patients were also collected. Feature reduction was performed using t-tests and the Least absolute shrinkage and selection operator regression. Unimodal modal and Pre-FMs were constructed using random forests, while the post-fusion model was developed using a support vector machine approach. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>512 DL features and 382 Rad features were extracted. The CRDL model demonstrated superior performance with AUC values of 0.884 in the validation dataset and 0.976 in the training dataset, surpassing the best DL model in both unimodal and pre-fusion settings, which had training and validation AUCs of 0.854 and 0.749.</div></div><div><h3>Conclusion</h3><div>The CRDL model accurately forecasts immunotherapy responses in NSCLC patients, offering one dependable non-invasive test.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 236-254"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.10.006
Fukun Shi , Xianqing Ren , Qian Xu , Jiameng Si , Yihao Yan , Junjie Shu , Shengli Shi , Ke Jin , Fenfen Li , Jiajia Zhang , Lan Zhang
Rationale and Objectives
To develop and validate an interpretable radiomics model based on pituitary MRI to predict growth hormone deficiency (GHD) in children with short stature.
Methods
This retrospective multicenter study enrolled 202 children (105 GHD, 97 idiopathic short stature [ISS]) as an internal cohort (7:3 ratio for training/testing cohorts) from institution I, and 138 children (61 GHD, 77 ISS) from institution II and institution III as an external validation cohort. Radiomics features were selected by SelectKBest and least absolute shrinkage and selection operator (LASSO), subsequently used to construct six machine learning models. Diagnostic performance of model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration curves. The interpretability of the model was assessed using Shapley additive explanations (SHAP).
Results
A total of 17 radiomics features were selected. Among all classifiers, support vector machine (SVM)-based radiomics model exhibited the highest diagnostic performance, with AUCs of 0.877 (95% CI: 0.813, 0.928), 0.878 (95% CI: 0.786, 0.951), and 0.885 (95% CI: 0.833, 0.937) in training, testing, and external validation cohorts, respectively. The SVM-integrated clinical-radiomics model yielded comparable efficacy, with AUCs of 0.874 (95% CI: 0.812, 0.928), 0.878 (95% CI: 0.786, 0.952), and 0.889 (95% CI: 0.830, 0.939) across the same cohorts. Both radiomics-based models significantly outperformed the clinical model (all p<0.001), while no statistically significant difference was observed between the radiomics and clinical-radiomics models (all p>0.05). The SHAP analysis identified three key radiomics features with significant differences between GHD and ISS groups (all p<0.001).
Conclusions
The interpretable radiomics-driven SVM model effectively predicts GH levels, providing a clinically viable, non-invasive alternative to GH stimulation test in children with short stature.
{"title":"An Interpretable Radiomics Model Based on Pituitary MRI to Predict Growth Hormone Deficiency in Short-statured Children: A Multicenter Study","authors":"Fukun Shi , Xianqing Ren , Qian Xu , Jiameng Si , Yihao Yan , Junjie Shu , Shengli Shi , Ke Jin , Fenfen Li , Jiajia Zhang , Lan Zhang","doi":"10.1016/j.acra.2025.10.006","DOIUrl":"10.1016/j.acra.2025.10.006","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To develop and validate an interpretable radiomics model based on pituitary MRI to predict growth hormone deficiency (GHD) in children with short stature.</div></div><div><h3>Methods</h3><div>This retrospective multicenter study enrolled 202 children (105 GHD, 97 idiopathic short stature [ISS]) as an internal cohort (7:3 ratio for training/testing cohorts) from institution I, and 138 children (61 GHD, 77 ISS) from institution II and institution III as an external validation cohort. Radiomics features were selected by SelectKBest and least absolute shrinkage and selection operator (LASSO), subsequently used to construct six machine learning models. Diagnostic performance of model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration curves. The interpretability of the model was assessed using Shapley additive explanations (SHAP).</div></div><div><h3>Results</h3><div>A total of 17 radiomics features were selected. Among all classifiers, support vector machine (SVM)-based radiomics model exhibited the highest diagnostic performance, with AUCs of 0.877 (95% <em>CI</em>: 0.813, 0.928), 0.878 (95% <em>CI</em>: 0.786, 0.951), and 0.885 (95% <em>CI</em>: 0.833, 0.937) in training, testing, and external validation cohorts, respectively. The SVM-integrated clinical-radiomics model yielded comparable efficacy, with AUCs of 0.874 (95% <em>CI</em>: 0.812, 0.928), 0.878 (95% <em>CI</em>: 0.786, 0.952), and 0.889 (95% <em>CI</em>: 0.830, 0.939) across the same cohorts. Both radiomics-based models significantly outperformed the clinical model (all <em>p</em><0.001), while no statistically significant difference was observed between the radiomics and clinical-radiomics models (all <em>p</em>>0.05). The SHAP analysis identified three key radiomics features with significant differences between GHD and ISS groups (all <em>p</em><0.001).</div></div><div><h3>Conclusions</h3><div>The interpretable radiomics-driven SVM model effectively predicts GH levels, providing a clinically viable, non-invasive alternative to GH stimulation test in children with short stature.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 168-179"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145395046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.09.039
Kelly M. Gillen PhD, MBA , Mert Şişman , Alan Wu , Arindam RoyChoudhury PhD , Ajay Gupta MD, MS
Rationale and Objectives
With a budget of almost $48 billion in 2024, including $37 billion allocated for extramural funding, the National Institutes of Health (NIH) is the major funding source for biomedical research in the United States. Given the multi-faceted impact of NIH funding on academic institutions and their communities, we sought to characterize trends in research project grant funding to departments of radiology.
This study aimed to assess trends in R01 and R01 equivalent award funding to departments of radiology from 2014 to 2024.
Materials and Methods
All funding data were retrieved from NIH RePORTER (Research Portfolio Online Reporting Tools) and limited to R01 and R01 equivalent awards (R01+) to all clinical departments (ACDs) from NIH FY 2014 and FY 2024. Awards and funding data included ACDs as categorized by the Blue Ridge Institute for Medical Research. Information on principal investigator (PI) advanced degrees was obtained by web searches and visiting the PI’s faculty page through their respective academic institution.
Results
From 2014 to 2024, there was a 54.3% increase in the number of R01s awarded to radiology as compared to a 31.7% increase in R01s awarded to ACDs. There was a 69.0% increase in the number of R01+s awarded to radiology as compared to a 34.4% increase in R01+s awarded to ACDs during this same period.
Conclusion
Since FY2014, there has been an increase in funding from the NIH to ACDs and specifically to radiology, but departments of radiology are outpacing ACDs in several key R01 and R01+ funding metrics, including greater increases in the number of awards.
{"title":"Trends in R01 and R01 Equivalent Funding to Radiology","authors":"Kelly M. Gillen PhD, MBA , Mert Şişman , Alan Wu , Arindam RoyChoudhury PhD , Ajay Gupta MD, MS","doi":"10.1016/j.acra.2025.09.039","DOIUrl":"10.1016/j.acra.2025.09.039","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>With a budget of almost $48 billion in 2024, including $37 billion allocated for extramural funding, the National Institutes of Health (NIH) is the major funding source for biomedical research in the United States. Given the multi-faceted impact of NIH funding on academic institutions and their communities, we sought to characterize trends in research project grant funding to departments of radiology.</div><div>This study aimed to assess trends in R01 and R01 equivalent award funding to departments of radiology from 2014 to 2024.</div></div><div><h3>Materials and Methods</h3><div>All funding data were retrieved from NIH RePORTER (Research Portfolio Online Reporting Tools) and limited to R01 and R01 equivalent awards (R01<sup>+</sup>) to all clinical departments (ACDs) from NIH FY 2014 and FY 2024. Awards and funding data included ACDs as categorized by the Blue Ridge Institute for Medical Research. Information on principal investigator (PI) advanced degrees was obtained by web searches and visiting the PI’s faculty page through their respective academic institution.</div></div><div><h3>Results</h3><div>From 2014 to 2024, there was a 54.3% increase in the number of R01s awarded to radiology as compared to a 31.7% increase in R01s awarded to ACDs. There was a 69.0% increase in the number of R01<sup>+</sup>s awarded to radiology as compared to a 34.4% increase in R01<sup>+</sup>s awarded to ACDs during this same period.</div></div><div><h3>Conclusion</h3><div>Since FY2014, there has been an increase in funding from the NIH to ACDs and specifically to radiology, but departments of radiology are outpacing ACDs in several key R01 and R01<sup>+</sup> funding metrics, including greater increases in the number of awards.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 15-19"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.acra.2025.09.042
Deniz Esin Tekcan Sanli , Ahmet Necati Sanli
{"title":"Challenges in Imaging-Based Nomogram Models for ALNM in TNBC: Commentary on Mammography and Ultrasound Approaches","authors":"Deniz Esin Tekcan Sanli , Ahmet Necati Sanli","doi":"10.1016/j.acra.2025.09.042","DOIUrl":"10.1016/j.acra.2025.09.042","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"33 1","pages":"Pages 82-83"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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