Journal of Thoracic Imaging最新文献

{"title":"Anomalies of the Brachiocephalic Vein: A Pictorial Review.","authors":"Thazhathu Veettil Sreelal, Niraj Nirmal Pandey","doi":"10.1097/RTI.0000000000000840","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000840","url":null,"abstract":"<p><p>Anomalies of the brachiocephalic or \"innominate\" vein, which may be of course or number, are rare. Previously, most cases were incidentally detected during surgery or in autopsy specimens. Doppler evaluation may also demonstrate these anomalies; however, the sensitivity is poor due to suboptimal visualization of the entire course. With the advent of multidetector CT angiography, these anomalies are increasingly being detected. Although majority of cases are incidentally detected on scans performed for evaluation of congenital heart defects, these anomalies assume clinical importance when central venous cannulation or pacemaker insertion is contemplated through the anomalous side. Prior knowledge of the anomalous anatomy is also important before cardiothoracic surgeries. In this article, we present a pictorial review of the anomalies of the brachiocephalic vein using multidetector CT angiography.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-aided Nodule Detection in the Lung Apices in Head and Neck Computed Tomography Angiography: An Unexpected Opportunity.","authors":"Tamar Perel Kass, Jeffrey Chankowsky, Jacob Sosna, Benjamin Hyatt Taragin, Alla Khashper","doi":"10.1097/RTI.0000000000000836","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000836","url":null,"abstract":"<p><strong>Purpose: </strong>Computed tomography angiography (CTA) of the head and neck includes the pulmonary apices, a common location for pulmonary nodules. Computer-aided detection (CAD) is an adjunctive tool for the detection of lung nodules and is widely used in standard chest CT scans. We evaluated whether the available software can be applied to CTA head and neck examinations, which include the lung apices, resulting in improved accuracy for lung nodule detection.</p><p><strong>Materials and methods: </strong>In this retrospective single-center study, 191 previously reported head and neck CTA scans were re-evaluated for apical pulmonary nodules by 2 radiologists. Subsequently, CAD software (Syngo.via, Siemens Healthiness AG) was applied to the lung apices and the results were compared between CAD and research radiologists (first reading) or clinical radiologist (null reading). In addition, the CAD performance in limited lung fields was compared with the accepted CAD assessment applied to whole lungs.</p><p><strong>Results: </strong>Of the 191 patients, 110 (57.6%) were men, with a mean age of 68 years. In the 24 CT scans, the research radiologists detected 40 nodules. In the 180 scans evaluated by CAD, the software detected 39 nodules in 22 examinations, with a sensitivity of 60.8% and a PPV of 63.6%. In the remaining 158 examinations in which CAD did not detect nodules, the radiologists concurred in 149 scans, with a specificity of 94.9%, NPV of 94.3%, and accuracy of 90.6%.</p><p><strong>Conclusion: </strong>The study results indicate that CAD is an unexpected quick supportive tool for nodule detection, particularly for excluding clinically significant nodules in lung apices on CTA head and neck, showing similar results for partial and full lung fields.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging Spectrum of Mesenchymal Tumors of Lungs and Pleura.","authors":"Nivedita Chakrabarty, Ashu Seith Bhalla, Manisha Jana, Abhishek Mahajan","doi":"10.1097/RTI.0000000000000839","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000839","url":null,"abstract":"<p><p>This review covers World Health Organization classification and in-depth imaging of diverse adult and paediatric mesenchymal tumors of the thorax (lungs and pleura), highlighting their key imaging features predominantly on computed tomography (CT) including CT angiography (for intimal sarcoma), and also on other imaging modalities such as ultrasound, magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography CT (FDG PET-CT) wherever necessary. Although rare, it is essential to identify and differentiate these mesenchymal tumors from the common epithelial tumors of lungs on imaging, as their management is entirely different. Mesenchymal tumor should be suspected over epithelial tumor when the CT scan shows a large-sized, well-marginated tumor or a tumor containing fat and calcifications in adults, or a solid-cystic tumor in a relatively younger population. An algorithmic approach to diagnosing these tumors has been presented at the end based on age (children/adult), location (perihilar /intrapulmonary/ peripheral), nature of tumor (solid/solid-cystic), and content (calcification/fat), for the ease of evaluation by the radiologists.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT Features for Prognostic Assessment of Pulmonary Mucormycosis in Patients With Hematological Diseases.","authors":"Huiming Yi, Shuping Zhang, Jieru Wang, Chunhui Xu, Donglin Yang, Qingsong Lin, Xiaoxue Wang, Sizhou Feng","doi":"10.1097/RTI.0000000000000832","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000832","url":null,"abstract":"<p><strong>Purpose: </strong>To explore the CT features in prognostic evaluations for pulmonary mucormycosis in patients with hematological diseases.</p><p><strong>Materials and methods: </strong>A retrospective analysis of clinical data and chest CT features of 53 HD patients with PM was conducted. Univariate and multivariate logistic regression analyses were used to determine the risk factors for death. The Cox regression model was used to analyze the factors affecting the survival rate.</p><p><strong>Results: </strong>A total of 30 patients with proven PM and 23 with probable PM were included. All 30 patients with proven PM underwent bronchoscopy-guided biopsy, among which 9 cases underwent surgical resection. Of the 23 patients with probable PM, 5 cases had positive results in sputum smear microscopy, 4 cases in sputum culture, 13 cases in bronchoalveolar lavage fluid (BALF) microscopy, and 1 case in BALF culture. All identification of pathogen genera and partial species was conducted by metagenomic next-generation sequencing (mNGS) testing. In the multivariate regression analysis, the CT feature of multiple lesions (≥2) on the initial CT scan was an independent risk factor for mortality (P=0.019). Cox survival analysis demonstrated a significantly lower survival rate (P=0.043) in patients exhibiting the CT feature of multiple lesions on the initial CT scan.</p><p><strong>Conclusions: </strong>The CT feature of multiple lesions (≥2) on the initial CT may serve as an independent risk factor for mortality in patients with hematologic disorders with pulmonary mucormycosis.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection, Classification, and Segmentation of Rib Fractures From CT Data Using Deep Learning Models: A Review of Literature and Pooled Analysis.","authors":"Stella Den Hengst, Noor Borren, Esther M M Van Lieshout, Job N Doornberg, Theo Van Walsum, Mathieu M E Wijffels, Michael H J Verhofstad","doi":"10.1097/RTI.0000000000000833","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000833","url":null,"abstract":"<p><strong>Purpose: </strong>Trauma-induced rib fractures are common injuries. The gold standard for diagnosing rib fractures is computed tomography (CT), but the sensitivity in the acute setting is low, and interpreting CT slices is labor-intensive. This has led to the development of new diagnostic approaches leveraging deep learning (DL) models. This systematic review and pooled analysis aimed to compare the performance of DL models in the detection, segmentation, and classification of rib fractures based on CT scans.</p><p><strong>Materials and methods: </strong>A literature search was performed using various databases for studies describing DL models detecting, segmenting, or classifying rib fractures from CT data. Reported performance metrics included sensitivity, false-positive rate, F1-score, precision, accuracy, and mean average precision. A meta-analysis was performed on the sensitivity scores to compare the DL models with clinicians.</p><p><strong>Results: </strong>Of the 323 identified records, 25 were included. Twenty-one studies reported on detection, four on segmentation, and 10 on classification. Twenty studies had adequate data for meta-analysis. The gold standard labels were provided by clinicians who were radiologists and orthopedic surgeons. For detecting rib fractures, DL models had a higher sensitivity (86.7%; 95% CI: 82.6%-90.2%) than clinicians (75.4%; 95% CI: 68.1%-82.1%). In classification, the sensitivity of DL models for displaced rib fractures (97.3%; 95% CI: 95.6%-98.5%) was significantly better than that of clinicians (88.2%; 95% CI: 84.8%-91.3%).</p><p><strong>Conclusions: </strong>DL models for rib fracture detection and classification achieved promising results. With better sensitivities than clinicians for detecting and classifying displaced rib fractures, the future should focus on implementing DL models in daily clinics.</p><p><strong>Level of evidence: </strong>Level III-systematic review and pooled analysis.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world Evaluation of Computer-aided Pulmonary Nodule Detection Software Sensitivity and False Positive Rate.","authors":"Raquelle El Alam, Khushboo Jhala, Mark M Hammer","doi":"10.1097/RTI.0000000000000835","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000835","url":null,"abstract":"<p><strong>Purpose: </strong>Evaluate the false positive rate (FPR) of nodule detection software in real-world use.</p><p><strong>Materials and methods: </strong>A total of 250 nonenhanced chest computed tomography (CT) examinations were randomly selected from an academic institution and submitted to the ClearRead nodule detection system (Riverain Technologies). Detected findings were reviewed by a thoracic imaging fellow. Nodules were classified as true nodules, lymph nodes, or other findings (branching opacity, vessel, mucus plug, etc.), and FPR was recorded. FPR was compared with the initial published FPR in the literature. True diagnosis was based on pathology or follow-up stability. For cases with malignant nodules, we recorded whether malignancy was detected by clinical radiology report (which was performed without software assistance) and/or ClearRead.</p><p><strong>Results: </strong>Twenty-one CTs were excluded due to a lack of thin-slice images, and 229 CTs were included. A total of 594 findings were reported by ClearRead, of which 362 (61%) were true nodules and 232 (39%) were other findings. Of the true nodules, 297 were solid nodules, of which 79 (27%) were intrapulmonary lymph nodes. The mean findings identified by ClearRead per scan was 2.59. ClearRead mean FPR was 1.36, greater than the published rate of 0.58 (P<0.0001). If we consider true lung nodules <6 mm as false positive, FPR is 2.19. A malignant nodule was present in 30 scans; ClearRead identified it in 26 (87%), and the clinical report identified it in 28 (93%) (P=0.32).</p><p><strong>Conclusion: </strong>In real-world use, ClearRead had a much higher FPR than initially reported but a similar sensitivity for malignant nodule detection compared with unassisted radiologists.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Society of Thoracic Radiology Mentorship Program: A Paradigm for Professional Societies.","authors":"Georgeann McGuinness, Linda B Haramati, Chi Wan Koo, Baskaran Sundaram","doi":"10.1097/RTI.0000000000000834","DOIUrl":"https://doi.org/10.1097/RTI.0000000000000834","url":null,"abstract":"<p><p>The Society of Thoracic Radiology (STR) membership enthusiastically embraced the launch of its mentorship program, with peaks in participation and engagement after annual meetings and during the COVID pandemic. The program provides a valuable resource for early to mid-career thoracic radiologists, especially those lacking local resources. This report describes the program's inception and design, and summarizes the program's successes and challenges at 5 years, based on a 2023 mentorship survey. STR mentees, spanning early to mid-career stages, most frequently sought mentorship in career development, graduate medical education, research portfolio development, publishing, cardiac imaging, grant funding, and artificial intelligence. Mentors offered expertise in these areas, plus lung cancer screening, career development, and workplace navigation. The committee prioritized creating dyads based on mutual interest and expertise, achieving mutual top-choice match rates of 70% to 97%. Enduring dyads flourished as the program matured. At 5 years, a survey of participants was fielded. Mentees reported moderate to high program impact on scholarly activities, leadership, networking, clinical service, education, and career satisfaction. Mentors described satisfaction in their roles, highlighting networking, career satisfaction, and the opportunity to influence upcoming generations of cardiothoracic radiologists, thereby impacting the field's future. Most participants expressed high career satisfaction. Descriptive comments further enriched findings. Survey results confirmed that strengthening dyad formation and enhancing mentoring outcomes remain pivotal. Remote mentorship, while valuable, presents challenges-personal connections and contextual familiarity, considered essential to successful mentorship relationships, are typically absent in these settings. Activities to potentially enhance the STR mentorship program are offered.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Photon-counting Detector Computed Tomography on a Quantitative Interstitial Lung Disease Machine Learning Model.","authors":"Chi Wan Koo, Sean J Huls, Francis Baffour, Cynthia H McCollough, Lifeng Yu, Brian J Bartholmai, Zhongxing Zhou","doi":"10.1097/RTI.0000000000000807","DOIUrl":"10.1097/RTI.0000000000000807","url":null,"abstract":"<p><strong>Purpose: </strong>Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.</p><p><strong>Materials and methods: </strong>A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05.</p><p><strong>Results: </strong>Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations).</p><p><strong>Conclusions: </strong>There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Diagnostic Performance of Large Language Models and General Radiologists in Thoracic Radiology Cases: A Comparative Study.","authors":"Yasin Celal Gunes, Turay Cesur","doi":"10.1097/RTI.0000000000000805","DOIUrl":"10.1097/RTI.0000000000000805","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate and compare the diagnostic performance of 10 different large language models (LLMs) and 2 board-certified general radiologists in thoracic radiology cases published by The Society of Thoracic Radiology.</p><p><strong>Materials and methods: </strong>We collected publicly available 124 \"Case of the Month\" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into LLMs for diagnosis and differential diagnosis, while radiologists independently visually provided their assessments. Cases were categorized anatomically (parenchyma, airways, mediastinum-pleura-chest wall, and vascular) and further classified as specific or nonspecific for radiologic diagnosis. Diagnostic accuracy and differential diagnosis scores (DDxScore) were analyzed using the χ 2 , Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests.</p><p><strong>Results: </strong>Among the 124 cases, Claude 3 Opus showed the highest diagnostic accuracy (70.29%), followed by ChatGPT 4/Google Gemini 1.5 Pro (59.75%), Meta Llama 3 70b (57.3%), ChatGPT 3.5 (53.2%), outperforming radiologists (52.4% and 41.1%) and other LLMs ( P <0.05). Claude 3 Opus DDxScore was significantly better than other LLMs and radiologists, except ChatGPT 3.5 ( P <0.05). All LLMs and radiologists showed greater accuracy in specific cases ( P <0.05), with no DDxScore difference for Perplexity and Google Bard based on specificity ( P >0.05). There were no significant differences between LLMs and radiologists in the diagnostic accuracy of anatomic subgroups ( P >0.05), except for Meta Llama 3 70b in the vascular cases ( P =0.040).</p><p><strong>Conclusions: </strong>Claude 3 Opus outperformed other LLMs and radiologists in text-based thoracic radiology cases. LLMs hold great promise for clinical decision systems under proper medical supervision.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute Pulmonary Injury: An Imaging and Clinical Review.","authors":"Taylor Sellers, Kirsten Alman, Maxwell Machurick, Hilary Faust, Jeffrey Kanne","doi":"10.1097/RTI.0000000000000825","DOIUrl":"10.1097/RTI.0000000000000825","url":null,"abstract":"<p><p>Acute pulmonary injury can occur in response to any number of inciting factors. The body's response to these insults is much less diverse and usually categorizable as one of several patterns of disease defined by histopathology, with corresponding patterns on chest CT. Common patterns of acute injury include diffuse alveolar damage, organizing pneumonia, acute eosinophilic pneumonia, and hypersensitivity pneumonitis. The ultimate clinical diagnosis is multidisciplinary, requiring a detailed history and relevant laboratory investigations from referring clinicians, identification of injury patterns on imaging by radiologists, and sometimes tissue evaluation by pathologists. In this review, several clinical diagnoses will be explored, grouped by imaging pattern, with a representative clinical presentation, a review of the current literature, and a discussion of typical imaging findings. Additional information on terminology and disambiguation will be provided to assist with comprehension and standardization of descriptions. The focus will be on the acute phase of illness from presentation to diagnosis; treatment methods and chronic sequela of acute disease are beyond the scope of this review.</p>","PeriodicalId":49974,"journal":{"name":"Journal of Thoracic Imaging","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}