medRxiv - Radiology and Imaging最新文献

{"title":"Boosting LLM-Assisted Diagnosis: 10-Minute LLM Tutorial Elevates Radiology Residents' Performance in Brain MRI Interpretation","authors":"Su Hwan Kim, Severin Schramm, Jonas Wihl, Philipp Raffler, Marlene Tahedl, Julian Canisius, Ina Luiken, Lukas Endroes, Stefan Reischl, Alexander Marka, Robert Walter, Mathias Schillmaier, Claus Zimmer, Benedikt Wiestler, Dennis Martin Hedderich","doi":"10.1101/2024.07.03.24309779","DOIUrl":"https://doi.org/10.1101/2024.07.03.24309779","url":null,"abstract":"Purpose\u0000To evaluate the impact of a structured tutorial on the use of a large language model (LLM)-based search engine on radiology residents' performance in LLM-assisted brain MRI differential diagnosis. Materials & Methods\u0000In this retrospective study, nine radiology residents determined the three most likely differential diagnoses for three sets of ten brain MRI cases with a challenging yet definite diagnosis. Each set of cases was assessed 1) with the support of conventional internet search, 2) using an LLM-based search engine (© Perplexity AI) without prior training, or 3) with LLM assistance after a structured 10-minute tutorial on how to effectively use the tool for differential diagnosis. The tutorial content was based on the results of two studies on LLM-assisted radiological diagnosis and included a prompt template. Reader responses were rated using a binary and numeric scoring system. Reading times were tracked and confidence levels were recorded on a 5-point Likert scale. Binary and numeric scores were analyzed using chi-square tests and pairwise Mann-Whitney U tests each. Search engine logs were examined to quantify user interaction metrics, and to identify hallucinations and misinterpretations in LLM responses. Results\u0000Radiology residents achieved the highest accuracy when employing the LLM-based search engine following the tutorial, indicating the correct diagnosis among the top three differential diagnoses in 62.5% of cases (55/88). This was followed by the LLM-assisted workflow before the tutorial (44.8%; 39/87) and the conventional internet search workflow (32.2%; 28/87). The LLM tutorial led to significantly higher performance (binary scores: p = 0.042, numeric scores: p = 0.016) and confidence (p = 0.006) but resulted in no relevant differences in reading times. Hallucinations were found in 5.1% of LLM queries. Conclusion\u0000A structured 10-minute LLM tutorial increased performance and confidence levels in LLM-assisted brain MRI differential diagnosis among radiology residents.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive Self-supervised Learning for Neurodegenerative Disorder Classification","authors":"Vadym Gryshchuk, Devesh Singh, Stefan J. Teipel, Martin Dyrba","doi":"10.1101/2024.07.03.24309882","DOIUrl":"https://doi.org/10.1101/2024.07.03.24309882","url":null,"abstract":"Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans. Supervised machine learning approaches classifying neurodegenerative diseases require diagnostic-labels for each sample. However, it can be difficult to obtain expert labels for a large amount of data. Self-supervised learning (SSL) offers an alternative for training machine learning models without data-labels. We investigated if the SSL models can applied to distinguish between different neurodegenerative disorders in an interpretable manner. Our method comprises a feature extractor and a downstream classification head. A deep convolutional neural network trained in a contrastive self-supervised way serves as the feature extractor, learning latent representation, while the classifier head is a single-layer perceptron. We used N=2694 T1-weighted MRI scans from four data cohorts: two ADNI datasets, AIBL and FTLDNI, including cognitively normal controls (CN), cases with prodromal and clinical AD, as well as FTLD cases differentiated into its sub-types. Our results showed that the feature extractor trained in a self-supervised way provides generalizable and robust representations for the downstream classification. For AD vs. CN, our model achieves 82% balanced accuracy on the test subset and 80% on an independent holdout dataset. Similarly, the behavioral variant of frontotemporal dementia (BV) vs. CN model attains an 88% balanced accuracy on the test subset. The average feature attribution heatmaps obtained by the Integrated Gradient method highlighted hallmark regions, i.e., temporal gray matter atrophy for AD, and insular atrophy for BV. In conclusion, our models perform comparably to state-of-the-art supervised deep learning approaches. This suggests that the SSL methodology can successfully make use of unannotated neuroimaging datasets as training data while remaining robust and interpretable.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naive patients with major depressive disorder.","authors":"Min Wang, Tao Chen, Zhongyi He, Lawrence Wing-Chi Chan, qinger guo, Shuyang Cai, Jingfeng Duan, Danbin Zhang, Xunda Wang, Yu Fang, Hong Yang","doi":"10.1101/2024.07.02.24309338","DOIUrl":"https://doi.org/10.1101/2024.07.02.24309338","url":null,"abstract":"Major depressive disorder (MDD) is characterized by disrupted functional network connectivity (FNC), with unclear underlying dynamics. We investigated both static FNC (sFNC) and dynamic FNC (dFNC) on resting-state fMRI data from drug-naive first-episode MDD patients and healthy controls (HC). MDD patients exhibited lower sFNC within and between sensory and motor networks than HC. Four dFNC states were identified, including a globally-weakly-connected state, a cognitive-control-dominated state, a globally-positively-connected state, and an antagonistic state. The antagonistic state was marked by strong positive connections within the sensorimotor domain and their anti-correlations with the executive-motor control domain. Notably, MDD patients exhibited significantly longer time dwelling in the globally-weakly-connected state, at the cost of significantly shorter time dwelling in the antagonistic state. Further, only the mean dwell time of this antagonistic state was significantly anticorrelated to disease severity measures. Our study highlights the altered dynamics of the antagonistic state as a fundamental aspect of disrupted FNC in early MDD.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative T1 and Effective Proton Density (PD*) mapping in children and adults at 7T from an MP2RAGE sequence optimised for uniform T1-weighted (UNI) and FLuid And White matter Suppression (FLAWS) contrasts","authors":"Ayşe Sıla Dokumacı, Katy Vecchiato, Raphael Tomi-Tricot, Michael Eyre, Philippa Bridgen, Pierluigi Di Cio, Chiara Casella, Tobias C. Wood, Jan Sedlacik, Tom Wilkinson, Sharon L. Giles, Joseph V. Hajnal, Jonathan O'Muircheartaigh, Shaihan J. Malik, David W. Carmichael","doi":"10.1101/2024.06.28.24307535","DOIUrl":"https://doi.org/10.1101/2024.06.28.24307535","url":null,"abstract":"<strong>Introduction</strong>\u0000Quantitative MRI is important for non-invasive tissue characterisation. In previous work we developed a clinically feasible multi-contrast protocol for T₁-weighted imaging based on the MP2RAGE sequence that was optimised for both children and adults. It was demonstrated that a range of Fluid And White Matter Suppression (FLAWS) related contrasts could be produced while maintaining T₁-weighted uniform image (UNI) quality, a challenge at higher field strengths. Here we introduce an approach to use these images to calculate effective proton density (PD^*) and quantitative T₁ relaxation maps especially for shorter repetition times (TR_MP2RAGE) than those typically used previously.\u0000<strong>Methods</strong>\u0000T₁ and PD^* were estimated from the analytical equations of the MP2RAGE signal derived for partial Fourier acquisitions. The sensitivity of the fitting results was evaluated with respect to the TR_MP2RAGE and B₁⁺ effects on both excitation flip angles and inversion efficiency and compared to vendor T₁ maps which do not use B₁⁺ information. Data acquired for a range of individuals (aged 10-54 years) at the shortest TR_MP2RAGE (4000ms) were compared across white matter (WM), cortical grey matter, and deep grey matter regions. <strong>Results</strong> The T₁ values were insensitive to the choice of different TR_MP2RAGE. The results were similar to the vendor T₁ maps if the B₁⁺ effects on the excitation flip angle and inversion efficiency were not included in the fits. T₁ values varied over development into adulthood, especially for the deep grey matter regions whereas only a very small difference was observed for WM T₁. Effective PD maps were produced which did not show a significant difference between children and adults for the age range included. <strong>Conclusion</strong>\u0000We produced PD^* maps and improved the accuracy of T₁ maps from an MP2RAGE protocol that is optimised for UNI and FLAWS-related contrasts in a single scan at 7T by incorporating the excitation flip angle and inversion efficiency related effects of B₁⁺ in the fitting. This multi-parametric protocol made it possible to acquire high resolution images (0.65mm iso) in children and adults within a clinically feasible duration (7:18 min:s). The combination of analytical equations utilizing B₁⁺ maps led to T₁ fits that were consistent at different TR_MP2RAGE values. Average WM T₁ values of adults and children were very similar (1092ms vs 1117ms) while expected reductions in T₁ with age were found for GM especially for deep GM.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetry of [64Cu]FBP8: a fibrin-binding PET probe","authors":"David Izquierdo-Garcia, Pauline Désogère, Anne L. Philip, David E. Sosnovik, Ciprian Catana, Peter Caravan","doi":"10.1101/2024.06.27.24309589","DOIUrl":"https://doi.org/10.1101/2024.06.27.24309589","url":null,"abstract":"Purpose: This study presents the biodistribution, clearance and dosimetry estimates of [64Cu]FBP8 Binding Probe #8 ([64Cu]FBP8) in healthy subjects.\u0000Procedures: This prospective study included 8 healthy subjects to evaluate biodistribution, safety and dosimetry estimates of [64Cu]FBP8, a fibrin-binding positron emission tomography (PET) probe. All subjects underwent up to 3 sessions of PET/Magnetic Resonance Imaging (PET/MRI) 0-2 hours, 4h and 24h post injection. Dosimetry estimates were obtained using OLINDA 2.2 software.\u0000Results: Subjects were injected with ~400 MBq of [64Cu]FBP8. Subjects did not experience adverse effects due to the injection of the probe. [64Cu]FBP8 PET images demonstrated fast blood clearance (half-life = 67 min) and renal excretion of the probe, showing low background signal across the body. The organs with the higher doses were: the urinary bladder (0.075 vs. 0.091 mGy/MBq for males and females, respectively); the kidneys (0.050 vs. 0.056 mGy/MBq respectively); and the liver (0.027 vs. 0.035 mGy/MBq respectively). The combined mean effective dose for males and females was 0.016 ± 0.0029 mSv/MBq, lower than the widely used [18F]fluorodeoxyglucose ([18F]FDG, 0.020mSv/MBq).\u0000Conclusions: This study demonstrates the following properties of the [64Cu]FBP8 probe: low dosimetry estimates; fast blood clearance and renal excretion; low background signal; and whole-body acquisition within 20 minutes in a single session. These properties provide the basis for [64Cu]FBP8 to be an excellent candidate for whole-body non-invasive imaging of fibrin, an important driver/feature in many cardiovascular, oncological and neurological conditions.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-based Multiclass Segmentation in Aneurysmal Subarachnoid Hemorrhage","authors":"Julia Kiewitz, Orhun Utku Aydin, Adam Hilbert, Marie Gultom, Anouar Nouri, Ahmed A Khalil, Peter Vajkoczy, Satoru Tanioka, Fujimaro Ishida, Nora F. Dengler, Dietmar Frey","doi":"10.1101/2024.06.24.24309431","DOIUrl":"https://doi.org/10.1101/2024.06.24.24309431","url":null,"abstract":"<strong>Introduction</strong> Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with a significant variability in patients’ outcomes. Radiographic scores used to assess the extent of SAH or other potentially outcome-relevant pathologies are limited by interrater variability and do not utilize all available information from the imaging. Image segmentation plays an important role in extracting relevant information from images by enabling precise identification and delineation of objects or regions of interest. Thus, segmentation offers the potential for automatization of score assessments and downstream outcome prediction using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aSAH outcome prediction.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility to virtually generate T2 fat-saturated breast MRI by convolutional neural networks","authors":"Andrzej Liebert, Dominique Hadler, Hannes Schreiter, Chris Ehring, Luise Brock, Lorenz A. Kapsner, Jessica Eberle, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt","doi":"10.1101/2024.06.25.24309404","DOIUrl":"https://doi.org/10.1101/2024.06.25.24309404","url":null,"abstract":"Background: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which are vital for tissue characterization but significantly increase scan time. Purpose: This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS images from routine multiparametric breast MRI sequences.\u0000Materials and Methods: This IRB approved, retrospective study included n=914 breast MRI examinations performed between January 2017 and June 2020. The dataset was divided into training (n=665), validation (n=74), and test sets (n=175). The U-Net was trained on T1-weighted (T1w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) sequences to generate virtual T2w-FS images (VirtuT2). Quantitative metrics and a qualitative multi-reader assessment by two radiologists were used to evaluate the VirtuT2 images.\u0000Results: VirtuT2 images demonstrated high structural similarity (SSIM=0.87) and peak signal-to-noise ratio (PSNR=24.90) compared to original T2w-FS images. High level of the frequency error norm (HFNE=0.87) indicates strong blurring presence in the VirtuT2 images, which was also confirmed in qualitative reading. Radiologists correctly identified VirtuT2 images with 92.3% and 94.2% accuracy, respectively. No significant difference in diagnostic image quality (DIQ) was noted for one reader (p=0.21), while the other reported significantly lower DIQ for VirtuT2 (p&lt;=0.001). Moderate inter-reader agreement was observed for edema detection on T2w-FS images (ƙ=0.43), decreasing to fair on VirtuT2 images (ƙ=0.36). Conclusion: The 2D-U-Net can technically generate virtual T2w-FS images with high similarity to real T2w-FS images, though blurring remains a limitation. Further investigation of other architectures and using larger datasets are needed to improve clinical applicability.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fingerprint method applied to data from a phase III clinical trial","authors":"Lars Edenbrandt","doi":"10.1101/2024.06.25.24309472","DOIUrl":"https://doi.org/10.1101/2024.06.25.24309472","url":null,"abstract":"Researchers in the RECOMIA network have been developing AI tools for the automated analysis of PET/CT studies in lymphoma patients. To enhance these AI tools, the CALGB 50303 dataset from The Cancer Imaging Archive was identified for inclusion in their project. Ensuring the quality of databases used for AI training is crucial, and one quality control (QC) measure involves the AI-based Fingerprint method to verify correct de-identification of clinical trial images. The study applied the Fingerprint method to PET/CT studies from 130 patients, successfully detecting an incorrectly de-identified study and identifying its correct trial identification number. This demonstrates the feasibility of using AI for QC in clinical trials. AI-based methods offer significant opportunities for enhancing QC, providing automated, consistent, and objective analyses that reduce the workload on human annotators. Integrating AI into QC processes promises to improve accuracy, consistency, and efficiency, thereby enhancing data integrity and the reliability of clinical trial results. This study underscores the importance of further developing AI-based QC methods in clinical trials.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering Radiologists with ChatGPT-4o: Comparative Evaluation of Large Language Models and Radiologists in Cardiac Cases","authors":"Turay Cesur, Yasin Celal Gunes, Eren Camur, Mustafa Dağlı","doi":"10.1101/2024.06.25.24309247","DOIUrl":"https://doi.org/10.1101/2024.06.25.24309247","url":null,"abstract":"<strong>Purpose</strong> This study evaluated the diagnostic accuracy and differential diagnosis capabilities of 12 Large Language Models (LLMs), one cardiac radiologist, and three general radiologists in cardiac radiology. The impact of ChatGPT-4o assistance on radiologist performance was also investigated.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application","authors":"Hung-Ching Chang, Yusi Fang, Michael T. Gorczyca, Kayhan Batmanghelich, George C. Tseng","doi":"10.1101/2024.06.23.24309362","DOIUrl":"https://doi.org/10.1101/2024.06.23.24309362","url":null,"abstract":"Causal mediation analysis provides a systematic approach to explore the causal role of one or more mediators in the association between exposure and outcome. In omics or imaging data analysis, mediators are often high-dimensional, which brings new statistical challenges. Existing methods either violate causal assumptions or fail in interpretable variable selection. Additionally, mediators are often highly correlated, presenting difficulties in selecting and prioritizing top mediators. To address these issues, we develop a framework using Partial Sum Statistic and Sample Splitting Strategy, namely PS5, for high-dimensional causal mediation analysis. The method provides a powerful global mediation test satisfying causal assumptions, followed by an algorithm to select and prioritize active mediators with quantification of individual mediation contributions. We demonstrate its accurate type I error control, superior statistical power, reduced bias in mediation effect estimation, and accurate mediator selection using extensive simulations of varying levels of effect size, signal sparsity, and mediator correlations. Finally, we apply PS5 to an imaging genetics dataset of chronic obstructive pulmonary disease (COPD) patients (<em>N</em>=8,897) in the COPDGene study to examine the causal mediation role of lung images (<em>p</em>=5,810) in the associations between polygenic risk score and lung function and between smoking exposure and lung function, respectively. Both causal mediation analyses successfully estimate the global indirect effect and detect mediating image regions. Collectively, we find a region in the lower lobe of the right lung with a strong and concordant mediation effect for both genetic and environmental exposures. This suggests that targeted treatment toward this region might mitigate the severity of COPD due to genetic and smoking effects.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}