Journal of Computer Assisted Tomography最新文献

{"title":"Diagnostic Performance of Multiparametric MRI for Detection of Prostate Cancer After Focal Therapy.","authors":"Robert D Petrocelli, Barun Bagga, Sooah Kim, Vinay Prabhu, Kun Qian, Ezequiel Becher, Samir S Taneja, Angela Tong","doi":"10.1097/RCT.0000000000001703","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001703","url":null,"abstract":"<p><strong>Background: </strong>Minimally invasive focal therapy of low- to intermediate-risk prostate cancer is becoming more common and has demonstrated lower morbidity compared to other treatments. Multiparametric prostate magnetic resonance imaging (mpMRI) has the potential to be an effective posttreatment evaluation method for residual/recurrent neoplasm.</p><p><strong>Objective: </strong>This study aimed to evaluate the ability of mpMRI to detect residual/recurrent neoplasm after focal therapy treatment of prostate cancer using a 3-point Likert scale.</p><p><strong>Methods: </strong>This retrospective study included patients who underwent focal therapy utilizing cryoablation, high-frequency ultrasound, and radiofrequency ablation for low- to intermediate-risk prostate cancer with baseline mpMRI and biopsy and a 6- to 12-month follow-up mpMRI and biopsy. Three abdominal fellowship-trained readers were asked to evaluate the follow-up mpMRI utilizing a 3-point Likert scale based on the level of suspicion as \"nonviable,\" \"equivocal,\" or \"viable.\" Diagnostic statistics and Light's κ for interreader variability were calculated.</p><p><strong>Results: </strong>A total of 142 patients were included (mean age, 65 ± 7 years). When considering \"equivocal\" or \"viable\" as positive, the overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) for detecting recurrent grade group (GG) 2 or greater disease for Reader 1 were 0.47, 0.83, 0.24, 0.93, and 0.65; for Reader 2, 0.73, 0.75, 0.26, 0.96, and 0.74; and for Reader 3, 0.73, 0.57, 0.17, 0.95, and 0.65. When considering \"viable\" as positive, the overall sensitivity, specificity, PPV, NPV, and AUC for Reader 1 were 0.47, 0.92, 0.41, 0.94, and 0.69; for Reader 2, 0.33, 0.97, 0.56, 0.93, and 0.65; and for Reader 3, 0.53, 0.84, 0.29, 0.94, and 0.69. κ was 0.39.</p><p><strong>Conclusions: </strong>This study suggests that DCE and DWI are the most important sequences in mpMRI and demonstrates the efficacy of utilizing a 3-point grading system in detecting and diagnosing prostate cancer after focal therapy.</p><p><strong>Clinical impact: </strong>mpMRI can be used to monitor for residual/recurrent disease after focal therapy.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813375","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":"Garbage Out: A Radiologist's Guide to Hospital Waste Streams.","authors":"Claire E White-Dzuro, Patrick W Doyle, Michael C Larson, Katherine C Frederick-Dyer","doi":"10.1097/RCT.0000000000001681","DOIUrl":"10.1097/RCT.0000000000001681","url":null,"abstract":"<p><strong>Abstract: </strong>What happens to trash after disposal? The management and processing of discarded items is often opaque and taken for granted, but an understanding of hospital waste streams is important for radiology departments and hospital systems for economic, regulatory, and environmental reasons. In this paper, we discuss the numerous waste pathways including general, hazardous, pharmaceutical, radioactive, and electronic waste as well as sustainable waste streams including laundry services, composting, and recycling. Costs, regulatory issues, and environmental considerations associated with each pathway are reviewed. We also describe radiology's specific contributions to each waste stream as well as variations in department practices, tips for optimal use, and future research investigations that could impact waste volume. Healthcare garbage disposal pathways will only increase in importance as our healthcare needs and systems continue to grow, and waste optimization efforts yield benefits to operation costs, environmental ecosystems, and human health.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780216","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 Imaging Methods in Predicting Lung Cancer Metastases.","authors":"Murat Aşık, Zeynep Nihal Kazci","doi":"10.1097/RCT.0000000000001706","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001706","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the possibility of distant organ metastasis using an algorithm developed to evaluate the morphology and localization of lung masses.</p><p><strong>Methods: </strong>Patients diagnosed with lung cancer between 2016 and 2023 were included. The lesion's morphological characteristics, proximity to important structures, and maximum standardized uptake value were recorded. Six common metastatic sites were identified: the contralateral lung, liver, brain, adrenal glands, bone, and other regions. The relationship between the characteristics of the mass and the metastatic location was investigated.</p><p><strong>Results: </strong>A total of 383 patients (260 men, 68%) with malignant lung lesions with a mean ± SD age of 65.50 ± 12.34 years (range: 36-74 years) were included in the study. Among them, 242 were diagnosed with primary lung cancer, and 106 (43.8%) exhibited metastases to other organs with primary lung tumors. Distant organ metastases were most frequently detected in the bones (n = 45, 42.5%) and were more frequent in male patients and lesions adjacent to the ribs and bronchi, those involving mediastinal lymph nodes, irregular contours, and maximum standardized uptake values above 11.15 ± 5.67 (mean ± SD).</p><p><strong>Conclusions: </strong>Evaluating radiological imaging of malignant lesions in patients with lung cancer using an algorithm that considers morphological and neighborhood characteristics can provide predictive information regarding the possibility of metastasis of malignant lung lesions and the metastatic location.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813374","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":"Opportunistic Quantitative Computed Tomography Assessing Bone Mineral Density in Patients With Laparoscopic Roux-En-Y-Gastric Bypass Metabolic Surgery Throughout a 5-Year Observation Window.","authors":"Mark-Stefan Noser, Daniel T Boll, Ioannis I Lazaridis, Tarik Delko, Thomas Koestler, Urs Zingg, Silke Potthast","doi":"10.1097/RCT.0000000000001705","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001705","url":null,"abstract":"<p><strong>Background: </strong>Bariatric surgery is associated with decreasing bone mineral density (BMD).</p><p><strong>Objective: </strong>To assess the long-term vertebral BMD, measured by opportunistic quantitative CT (QCT), and body mass index (BMI) in patients undergoing proximal laparoscopic Roux-en-Y surgery (LRYGB).</p><p><strong>Methods: </strong>In 62 patients undergoing LRYGB, opportunistic QCT measurements were performed extracting BMD and BMI on day 1 and years 1, 3, and 5 postoperatively.Primarily, one-way analyses of variance were performed on dependent variables BMI and BMD, with imaging interval defined as an independent factor. Student-Newman-Keuls tests performed pairwise comparisons of imaging interval permutations for BMI/BMD.Secondarily, analyses of covariance were used on dependent variables BMI and BMD, with imaging interval as an independent factor and gender/age as well as BMD/BMI, respectively, as covariates.</p><p><strong>Results: </strong>A total of 227 opportunistic QCT measurements in 62 patients were performed without the need of a phantom or extra software.The BMD decreased substantially and continuously during 1-, 3-, and 5-year follow-up observations, reaching statistical significance in pairwise comparisons for 3- and 5-year follow-up visits compared to initial BMD values as well as the 5-year follow-up visit compared to the 1-year BMD values, P < 0.001. Age and BMI were significant covariates, P < 0.001.The BMI decreased within 1 year and stayed constant until a slight increase at 5 years was observed. Statistical significance in pairwise comparisons for first-year and 3- and 5-year follow-up visits was reached compared to initial BMI values, P < 0.001. For the BMI assessment, none of the covariates reached statistical significance.</p><p><strong>Conclusion: </strong>Opportunistic QCT is suited for the calculation and follow-up of BMD. There was a continuous decrease of BMD after LRYGB over 5 years post-surgery, whereas BMI decreased in the first year and stayed constant thereafter. Older patients with lower BMI seem particularly prone to an accelerated BMD loss.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780220","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":"Prediction of Local Tumor Progression After Thermal Ablation of Colorectal Cancer Liver Metastases Based on Magnetic Resonance Imaging Δ-Radiomics.","authors":"Xiucong Zhu, Jinke Zhu, Chenwen Sun, Fandong Zhu, Bing Wu, Jiaying Mao, Zhenhua Zhao","doi":"10.1097/RCT.0000000000001702","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001702","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Δ-radiomics and clinical feature-based modeling was employed.</p><p><strong>Materials and methods: </strong>In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Δ-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Δ model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP.</p><p><strong>Results: </strong>LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size (P = 0.010), and ΔCEA (P = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Δ model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences (P = 0.6087).</p><p><strong>Conclusions: </strong>Combined models incorporating clinical data and Δ-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780222","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":"Reducing the Energy Consumption of Magnetic Resonance Imaging and Computed Tomography Scanners: Integrating Ecodesign and Sustainable Operations.","authors":"Andrew M Hernandez, Anthony F Chen, Omkar Ghatpande, Reed A Omary, Sean Woolen, Youngkyoo Jung, Ghaneh Fananapazir","doi":"10.1097/RCT.0000000000001700","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001700","url":null,"abstract":"<p><strong>Abstract: </strong>This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. This review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780224","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":"T1WI Radiomics Analysis of Anterior Scalene Muscle: A Preliminary Application in Neurogenic Thoracic Outlet Syndrome.","authors":"Meng Sun, Le Fang, Peiyun Tang, Fangruyue Wang, Ling Jiang, Tianwei Wang","doi":"10.1097/RCT.0000000000001701","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001701","url":null,"abstract":"<p><strong>Aim: </strong>This study aimed to analyze the differences in radiomic features of the anterior scalene muscle and evaluate the diagnostic performance of MRI-based radiomics model for neurogenic thoracic outlet syndrome (NTOS).</p><p><strong>Materials and methods: </strong>Imaging data of patients with NTOS who underwent preoperative brachial plexus magnetic resonance neurography were collected and were randomly divided into training and test groups. The anterior scalene muscle area was sliced in the T1WI sequence as the region of interest for the extraction of radiomics features. The most significant features were identified using feature selection and dimensionality-reduction methods. Various machine learning algorithms were applied to construct regression models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC).</p><p><strong>Results: </strong>Totally, 267 radiomics features were extracted, of which 57 showed significant differences (P ≤ 0.05) between the abnormal and normal anterior scalene muscle groups. The least absolute shrinkage and selection operator regression model identified 13 optimal radiomic features with nonzero coefficients for constructing the model. In the training set, the AUROCs of diagnostic models built by different machine learning algorithms, ranked from highest to lowest, were as follows: support vector machine (SVM), 0.953; multilayer perception (MLP), 0.936; logistic regression (LR), 0.926; light gradient boosting machine (LightGBM), 0.906; and K-nearest neighbors (KNN), 0.813. In the testing set, the rankings were as follows: LR, 0.933; SVM, 0.886; KNN, 0.843; LightGBM, 0.824; and MLP, 0.706.</p><p><strong>Conclusions: </strong>NTOS is attributed to anterior scalene muscle abnormalities and exhibits distinct radiomic features. Integrating these features with machine learning can improve traditional manual image interpretation, offering further clarity in NTOS diagnosis.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780227","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 Environmental Impact of Iodinated Contrast Media: Strategies for Optimized Use and Recycling.","authors":"Giuseppe V Toia, Lakshmi Ananthakrishnan","doi":"10.1097/RCT.0000000000001674","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001674","url":null,"abstract":"<p><strong>Abstract: </strong>Iodinated contrast media (ICM) is an integral and ubiquitous component of modern diagnostic imaging. Although most radiology practices are familiar with ICM administration and physiological excretion, they may be less aware of how much ICM is wasted on a per exam basis. Furthermore, radiologists may not recognize the environmental fate of discarded ICM waste. In an evolving world where medical practices are increasingly cognizant of their environmental footprint and radiology practices are considered high consumers of resources, it behooves the radiology community to understand the ICM lifecycle and ways to mitigate unnecessary waste. This review article explains the origin and environmental fate of discarded ICM, with special focus on wastewater contamination. Secondly, the article focuses on feasible options to both optimize use and decrease consumable waste. Specifically, the article addresses ICM vial size inventory diversification, multi-use ICM vials, syringeless contrast injectors, and the potential for using multi-energy imaging (dual-energy or photon counting CT) to accomplish these goals. Finally, the authors share their institutional experience participating in an ICM recycling program and its current departmental impact.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780229","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":"Resident Education in the Age of AI.","authors":"Erin Gomez, Cheng Ting Lin","doi":"10.1097/RCT.0000000000001697","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001697","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is a rapidly expanding field of interest to radiologists for its utility as an adjunct in detecting and reporting disease and its potential influence on the role of radiologists and their practices. As radiology educators, we are responsible for developing and providing access to curricular elements that will prepare residents to be good stewards of artificial intelligence resources while also acquiring fundamental knowledge and skills that are essential to daily practice. Residency programs should consider collaborative approaches as well as solicit support from national societies in the development and curation of their AI curricula.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780226","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 Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer.","authors":"Tingting Xu, Xueli Zhang, Huan Tang, Ting Hua, Fuxia Xiao, Zhijun Cui, Guangyu Tang, Lin Zhang","doi":"10.1097/RCT.0000000000001691","DOIUrl":"https://doi.org/10.1097/RCT.0000000000001691","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.</p><p><strong>Methods: </strong>This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.</p><p><strong>Results: </strong>In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.</p><p><strong>Conclusions: </strong>Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780234","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}