Clinical Imaging最新文献

{"title":"Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI","authors":"Omer Tarik Esengur ,&nbsp;Enis C. Yilmaz ,&nbsp;Kutsev B. Ozyoruk ,&nbsp;Alex Chen ,&nbsp;Nathan S. Lay ,&nbsp;David G. Gelikman ,&nbsp;Maria J. Merino ,&nbsp;Sandeep Gurram ,&nbsp;Bradford J. Wood ,&nbsp;Peter L. Choyke ,&nbsp;Stephanie A. Harmon ,&nbsp;Peter A. Pinto ,&nbsp;Baris Turkbey","doi":"10.1016/j.clinimag.2024.110363","DOIUrl":"10.1016/j.clinimag.2024.110363","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop and evaluate a multimodal approach including clinical parameters and biparametric MRI-based artificial intelligence (AI) model for determining the necessity of prostate biopsy in patients with PI-RADS 3 lesions.</div></div><div><h3>Methods</h3><div>This retrospective study included a prospectively recruited patient cohort with PI-RADS 3 lesions who underwent prostate MRI and MRI/US fusion-guided biopsy between April 2019 and February 2024 in a single institution. The study examined demographic data, PSA and PSA density (PSAD) levels, prostate volumes, prospective PI-RADS v2.1-compliant interpretations of a genitourinary radiologist, lesion characteristics, history of prior biopsies, and AI evaluations, focusing mainly on the detection of clinically significant prostate cancer (csPCa) (International Society of Urological Pathology grade group ≥2) on MRI/US fusion-guided biopsy. The AI model lesion segmentations were compared to manual segmentations and biopsy results. The statistical methods employed included Fisher's exact test and logistic regression.</div></div><div><h3>Results</h3><div>The cohort was comprised of 248 patients with 312 PI-RADS 3 lesions in total (<em>n</em> = 268 non-csPCa, <em>n</em> = 44 csPCa). The AI model's negative predictive value (NPV) was 89.2 % for csPCa in all lesions. In patient-level analysis, the NPV was 91.2 % for patients with a highest PI-RADS score of 3. PSAD was a significant predictor of csPCa (odds ratio = 5.8, <em>p</em> = 0.038). Combining AI and PSAD, where AI correctly mapped a lesion or PSAD ≥0.15 ng/mL², achieved higher sensitivity (77.8 %) while maintaining a high NPV (93.1 %).</div></div><div><h3>Conclusion</h3><div>Combining AI and PSAD has the potential to enhance biopsy decision-making for PI-RADS 3 lesions by minimizing missed csPCa occurrences and reducing unnecessary biopsies.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110363"},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695999","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":"Short-term treatment response assessment in non-surgical treatment of advanced non-small cell lung cancer based on radiomics of dual-energy CT","authors":"Xiuting Wu ,&nbsp;Yumin Lu ,&nbsp;Danmei Huang ,&nbsp;Zefeng Li ,&nbsp;Chunchen Wei ,&nbsp;Kai Li","doi":"10.1016/j.clinimag.2024.110362","DOIUrl":"10.1016/j.clinimag.2024.110362","url":null,"abstract":"<div><h3>Purpose</h3><div>To build and evaluate a pre-treatment dual-energy CT(DECT)-based clinical-radiomics nomogram for individualized prediction of short-term treatment response to non-surgical treatment in advanced non-small cell lung cancer (NSCLC).</div></div><div><h3>Methods</h3><div>Pre-treatment DECT images were retrospectively collected from 98 pathologically confirmed NSCLC with clinical stage III or IV. Short-term treatment response was determined with follow-up CT of 4–6 courses of treatment. Quantitative radiomics metrics of the lesion were extracted from dual-energy mixed images at venous phase. Least absolute shrinkage and selection operator and correlation analysis were used to select the most relevant radiomics features. Radiomics model, clinical model and clinical-radiomics model were established by multivariate logistic regression. The model with the best prediction performance was visualized as a nomogram, and the consistency between the probability of the actual occurrence of the outcome and the probability predicted by the model was measured by calibration curves.</div></div><div><h3>Results</h3><div>Clinical stage, difference in electron density in arteriovenous phase, difference in slope of energy spectrum in arteriovenous phase, and slope of energy spectrum in venous phase of the tumor were significant clinical predictors of therapy response (<em>P</em> &lt; 0.05). The clinical-radiomics model showed a higher predictive capability (AUC: 0.87 and 0.85 in training and validation sets, respectively) than the radiomics models and the clinical model. The clinical-radiomics nomogram integrating the DECT radiomics signature with clinical stage and spectrum parameters showed good calibration and discrimination.</div></div><div><h3>Conclusion</h3><div>The clinical-radiomics nomogram based on pre-treatment DECT showed good performance in predicting clinical response to non-surgical therapy in NSCLC.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110362"},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693791","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":"Reply to: “Comment: Radiologists’ Perspectives on AI and Opportunistic CT Screening (OS)”","authors":"Adam E.M. Eltorai ,&nbsp;Katherine P. Andriole","doi":"10.1016/j.clinimag.2024.110359","DOIUrl":"10.1016/j.clinimag.2024.110359","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110359"},"PeriodicalIF":1.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696019","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":"Low breast density is associated with epicardial adipose tissue volume and coronary artery disease","authors":"Emma Aldous ,&nbsp;Vinay Goel ,&nbsp;Chee Yeong ,&nbsp;Nushrat Sultana ,&nbsp;Rachael Hii ,&nbsp;Huong Tu ,&nbsp;Anthony Salib ,&nbsp;Edwin Xu ,&nbsp;Sarang Paleri ,&nbsp;Sheran Vasanthakumar ,&nbsp;Rhea Nandurkar ,&nbsp;Andrew Lin ,&nbsp;Nitesh Nerlekar","doi":"10.1016/j.clinimag.2024.110357","DOIUrl":"10.1016/j.clinimag.2024.110357","url":null,"abstract":"<div><h3>Purpose</h3><div>Epicardial adipose tissue volume (EATv), is well correlated with coronary artery disease (CAD), however not reported clinically. Breast density, measured on mammography, has shown promise as a reflector of cardiometabolic risk, with less dense breasts indicating greater proportion of adipose tissue. We aimed to evaluate the association between breast density, EATv and CAD.</div></div><div><h3>Method</h3><div>Retrospective, cross-sectional study including 153 women who had both clinically indicated coronary computed tomography angiogram (CCTA) and mammography. EATv was quantified using semi-automated software. Breast density was visually assessed by standard 4-level BI-RADS grading (low: BI-RADS A–B, high: BI-RADS C<img>D). CAD was categorised as presence/absence of coronary artery plaque and severity was quantified using CAD-RADS score.</div></div><div><h3>Results</h3><div>Among 153 patients (mean age 62 ± 10), 103 (67.3 %) had low breast density (high breast adiposity). Low breast density patients were older, had greater rates of hypertension, higher mean BMI (<em>p</em> <em>&lt;</em> 0.001) and EATv (106.6 ± 43.0 ml vs 81.0 ± 31.6 ml, <em>p</em> <em>&lt;</em> 0.001). EATv was predictive of low breast density (OR: 1.02[1.01–1.03], <em>p</em> <em>=</em> 0.006), independent of age and hypertension. Low breast density was strongly associated with presence of CAD (prevalence 75 % vs 48 %, OR: 3.21[1.58–6.53], <em>p</em> <em>=</em> 0.001) independent of EATv, and modifiable (OR: 2.69[1.24–5.92], <em>p</em> <em>=</em> 0.012) and non-modifiable (OR: 2.42[1.04–5.85], <em>p</em> <em>=</em> 0.047) cardiovascular risk factors. Low breast density made up a higher proportion of mild (76.5 %), moderate (73.9 %) and severe (80.0 %) CAD.</div></div><div><h3>Conclusions</h3><div>Low breast density is associated with higher EATv and independently associated with CAD presence beyond EATv and other cardiovascular risk factors. Mammographic breast density may therefore have value as an early risk identification tool for CAD in women.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110357"},"PeriodicalIF":1.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment: Radiologists' perspectives on AI and opportunistic CT screening (OS)","authors":"Md. Tauseef Qamar,&nbsp;Juhi Yasmeen","doi":"10.1016/j.clinimag.2024.110360","DOIUrl":"10.1016/j.clinimag.2024.110360","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110360"},"PeriodicalIF":1.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683181","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":"Debunking a mythology: Atelectasis is not a cause of postoperative fever","authors":"Hadassah Stein ,&nbsp;John Denning ,&nbsp;Huma Ahmed ,&nbsp;Michael A. Bruno ,&nbsp;Marc Gosselin ,&nbsp;Jinel Scott ,&nbsp;Stephen Waite","doi":"10.1016/j.clinimag.2024.110358","DOIUrl":"10.1016/j.clinimag.2024.110358","url":null,"abstract":"<div><div>Most physicians appreciate that practicing medicine is a commitment to continuous learning. However, “learning” can be mistakenly understood as simply the acquisition of facts and new knowledge. But learning also necessitates the constant re-examination and challenging of one's <em>existing</em> body of knowledge, as misinformation persists when one's beliefs are not challenged or questioned in the light of new information. One example is the pervasive belief that postoperative atelectasis causes fever despite ample evidence to the contrary. Herein we examine the imaging characteristics of atelectasis, and the means of differentiation of atelectasis from consolidation. We also explore the history of this persistent myth and review the existing literature on the actual causes of postoperative fever.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110358"},"PeriodicalIF":1.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683209","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":"Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses","authors":"Mobina Fathi ,&nbsp;Kimia Vakili ,&nbsp;Ramtin Hajibeygi ,&nbsp;Ashkan Bahrami ,&nbsp;Shima Behzad ,&nbsp;Armin Tafazolimoghadam ,&nbsp;Hadiseh Aghabozorgi ,&nbsp;Reza Eshraghi ,&nbsp;Vivek Bhatt ,&nbsp;Ali Gholamrezanezhad","doi":"10.1016/j.clinimag.2024.110356","DOIUrl":"10.1016/j.clinimag.2024.110356","url":null,"abstract":"<div><div>Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures.</div><div>To enhance the utility of these AI models, radiologists need a more comprehensive understanding of the model's levels of confidence and certainty behind the results they produce. This allows radiologists to make more informed decisions that have the potential to drastically change a patient's clinical management. Several AI models, especially those utilizing deep learning models (DL) with convolutional neural networks (CNNs), have demonstrated significant potential in identifying subtle findings in medical imaging that are often missed by radiologists.</div><div>It is necessary to create standardized levels of confidence metrics in order for AI systems to be relevant and reliable in the clinical setting. Incorporating AI into clinical practice does have certain obstacles like the need for clinical validation, concerns regarding the interpretability of AI system results, and addressing confusion and misunderstandings within the medical community. This study emphasizes the importance of AI systems to clearly convey their level of confidence in radiological diagnosis. This paper highlights the importance of conducting research to establish AI confidence level metrics that are limited to a specific anatomical region or lesion type.</div></div><div><h3>Key point of the view</h3><div>Accurate fracture diagnosis relies on radiologic certainty, where Artificial intelligence (AI), especially convolutional neural networks (CNNs) and deep learning (DL), shows promise in enhancing X-ray interpretation amidst a shortage of radiologists. Overcoming integration challenges through improved AI interpretability and education is crucial for widespread acceptance and better patient outcomes.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110356"},"PeriodicalIF":1.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683186","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":"Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray","authors":"Caterina Beatrice Monti ,&nbsp;Lorenzo Maria Giuseppe Bianchi ,&nbsp;Francesco Rizzetto ,&nbsp;Luca Alessandro Carbonaro ,&nbsp;Angelo Vanzulli","doi":"10.1016/j.clinimag.2024.110355","DOIUrl":"10.1016/j.clinimag.2024.110355","url":null,"abstract":"<div><h3>Purpose</h3><div>Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists.</div></div><div><h3>Method</h3><div>We retrospectively ran an AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports.</div></div><div><h3>Results</h3><div>Overall, the AI system achieved an accuracy of 74 % (95%CI 68–79 %), with a sensitivity of 66 % (95%CI 59–73 %) and a specificity of 93 % (95%CI 85–97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71–82 %, <em>p</em> = 0.355), with higher sensitivity (73 %, 95%CI 66–79 %, <em>p</em> = 0.040), albeit lower specificity (85 %, 95%CI 75–91 %, <em>p</em> = 0.034). The performance of AI was influenced by patient positioning at CXR (<em>p</em> = 0.040).</div></div><div><h3>Conclusions</h3><div>The proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110355"},"PeriodicalIF":1.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimizing prostate diffusion weighted MRI examination time through deep learning reconstruction","authors":"Rory L. Cochran ,&nbsp;Eugene Milshteyn ,&nbsp;Soumyadeep Ghosh ,&nbsp;Nabih Nakrour ,&nbsp;Nathaniel D. Mercaldo ,&nbsp;Arnaud Guidon ,&nbsp;Mukesh G. Harisinghani","doi":"10.1016/j.clinimag.2024.110341","DOIUrl":"10.1016/j.clinimag.2024.110341","url":null,"abstract":"<div><h3>Purpose</h3><div>To study the diagnostic image quality of high b-value diffusion weighted images (DWI) derived from standard and variably reduced datasets reconstructed with a commercially available deep learning reconstruction (DLR) algorithm.</div></div><div><h3>Materials and methods</h3><div>This was a retrospective study of 52 patients undergoing conventional prostate MRI with raw image data reconstructed using both conventional 2D Cartesian and DLR algorithms. Simulated shortened DWI acquisition times were performed by reconstructing images using DLR datasets harboring a reduced number of excitations (NEX). Two radiologists independently evaluated the image quality using a 4-point Likert scale. Signal-to-noise ratio (SNR) analysis was performed for the entire cohort and a subset of patients identified as having clinically significant prostate cancer identified at MRI, and later confirmed by pathology.</div></div><div><h3>Results</h3><div>Radiologists perceived less image noise for both restricted and large field of view (FOV) standard NEX dataset DLR diffusion images compared to conventionally reconstructed images with good interreader agreement. Diagnostic image quality was maintained for all DLR images using variably reduced NEX compared to conventionally reconstructed images employing the standard NEX. Improved signal to noise was observed for the restricted FOV DLR images compared to conventional reconstruction using standard NEX. DLR diffusion images derived from reduced NEX datasets translated to time reductions of up to 68 % and 39 % for the restricted and large FOV series acquisitions, respectively.</div></div><div><h3>Conclusion</h3><div>DLR of diffusion weighted images can reduce image noise at standard NEX and potentially reduce prostate MRI exam time when utilizing reduced NEX datasets without sacrificing diagnostic image quality.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"117 ","pages":"Article 110341"},"PeriodicalIF":1.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631855","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":"Beyond the surface: A comprehensive radiological review of primary retroperitoneal neoplasms","authors":"Yagmur Basak Polat,&nbsp;Mehmet Ali Gultekin,&nbsp;Ahmet Akcay,&nbsp;Ummuhan Ebru Karabulut,&nbsp;Bahar Atasoy,&nbsp;Huseyin Toprak","doi":"10.1016/j.clinimag.2024.110340","DOIUrl":"10.1016/j.clinimag.2024.110340","url":null,"abstract":"<div><div>Primary retroperitoneal neoplasms (PRNs) are a complex and diverse group of tumors arising in the retroperitoneal space, excluding those from retroperitoneal organs. These masses present significant diagnostic challenges due to their heterogeneous nature. PRNs primarily include sarcomas, neurogenic tumors, extragonadal germ cell tumors, and lymphomas, with the majority being malignant. This necessitates thorough evaluation by radiologists to assess resectability and the need for biopsy.</div><div>Liposarcomas, the most common primary retroperitoneal sarcomas, and leiomyosarcomas, known for potential vessel involvement, exhibit distinct imaging patterns aiding differentiation. Neurogenic tumors, originating from nerve sheath, ganglionic, or paraganglionic cells, often appear in younger patients and have characteristic imaging features. Primary retroperitoneal extragonadal germ cell tumors are rare and are believed to originate from primordial germ cells that do not successfully migrate during embryonic development. Lymphomas are generally homogeneous on cross-sectional imaging; however, non-Hodgkin lymphomas can sometimes appear heterogeneous, complicating differentiation from other non-lipomatous retroperitoneal masses. Additionally, conditions like retroperitoneal fibrosis and Erdheim-Chester disease can mimic PRNs, complicating diagnosis and management.</div><div>This review aims to provide radiologists with essential diagnostic points for identifying PRNs, emphasizing the importance of precise imaging interpretation. Understanding these distinctions is vital for guiding clinical management and optimizing patient outcomes.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"116 ","pages":"Article 110340"},"PeriodicalIF":1.8,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592660","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}