BMC Medical Imaging最新文献

{"title":"Vascular ultrasound-based risk stratification model for atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus.","authors":"Cuie Chen, Qiuxiao Xu, Hong Xu, Yiyun Xu, Xueling He, Minhao Lin, Yuan Li, Yinhua Li, Lijuan Liu","doi":"10.1186/s12880-026-02340-0","DOIUrl":"https://doi.org/10.1186/s12880-026-02340-0","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to investigate the ability of an ultrasound-based risk stratification model integrating carotid intima thickness (CIT) and carotid-femoral pulse wave velocity (cfPWV) to aid in risk stratification and assessment of atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM), thereby providing an objective basis for identifying high-risk individuals and informing individualized management strategies.</p><p><strong>Methods: </strong>A total of 105 patients with T2DM were enrolled in this study. According to the 10-year ASCVD risk score, patients were further classified into T2DM patients with low-to-moderate burden of other cardiovascular risk factors and T2DM patients with high burden of other cardiovascular risk factors. CIT was measured using high-resolution ultrasound to assess vascular structure, while cfPWV was evaluated using the automatic measurement of arterial stiffness (AMAS) system to assess vascular function. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify independent risk factors of high ASCVD risk. Based on these risk factors, individual discriminative models and a nomogram were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance, and differences among models were assessed using the DeLong test.</p><p><strong>Results: </strong>CIT, cfPWV, and estimated glomerular filtration rate (eGFR) were identified as independent risk factors of high 10-year ASCVD risk in patients with T2DM. The areas under the curve (AUCs) for the CIT model, cfPWV model, eGFR model, combined CIT-cfPWV model, and the nomogram were approximately 0.781, 0.808, 0.797, 0.831, and 0.875, respectively. The constructed nomogram demonstrated excellent discrimination, calibration, and clinical applicability.</p><p><strong>Conclusions: </strong>CIT and cfPWV show strong potential for identifying T2DM patients at high ASCVD risk as estimated by the China-PAR model. Incorporating these parameters into vascular evaluation may aid in risk stratification and provide a robust basis for individualized clinical intervention strategies. Prospective studies are needed to validate their prognostic value for future ASCVD events.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of maras powder on mandibular bone microarchitecture: a fractal and radiomorphometric study.","authors":"Emine Ararat, Ayşe Gül Öner Talmaç","doi":"10.1186/s12880-026-02360-w","DOIUrl":"https://doi.org/10.1186/s12880-026-02360-w","url":null,"abstract":"<p><strong>Background: </strong>The aim of this study was to examine how maras powder (MP) affects on the cortical and trabecular bone of the mandible using the radiomorphometric indexes and fractal dimension (FD).</p><p><strong>Methods: </strong>A retrospective analysis of radiographic records of 150 male individuals, 50 of whom used MP, 50 of whom smoked cigarettes, and 50 of whom were healthy and did not use any tobacco derivatives, was performed. Cortical bone was evaluated with mandibular cortical width (MCW) and panoramic mandibular index (PMI). Trabecular bone in mandibular anterior was evaluated by FD. The ANOVA test was used to compare normally distributed variables across the three groups, and the Kruskal Wallis test was used to compare non-normally distributed variables across the three groups.</p><p><strong>Results: </strong>The mean age of MP users was 42.92 ± 10.21; in smokers, 40.46 ± 10.51; and in the healthy control group, 40 ± 15.05. When the FD measurements were examined in regions of interest (ROI) 1, ROI 2, ROI 3, and the mean ROI values, no significant difference was found between the three groups in terms of FD (p > 0.05), but the fractal dimension was found to be lower in individuals using MP. No significant difference was found between the groups in terms of histogram values ​​and MCW and PMI measurements (p > 0.05).</p><p><strong>Conclusion: </strong>No significant differences were found between users of MP, smokers, and healthy individuals. However, the decreasing trend in FD values ​​may indicate early effects of MP. Studies with larger sample sizes and advanced imaging techniques are needed.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.","authors":"Siru Kang, Wenxia Yang, Yijun Yu, Kai Wang, Wenhuan Yuan, Yanli Jiang, Jing Zhang","doi":"10.1186/s12880-026-02391-3","DOIUrl":"https://doi.org/10.1186/s12880-026-02391-3","url":null,"abstract":"<p><strong>Background: </strong>Detecting pituitary microadenomas using non-contrast multi-parametric magnetic resonance imaging (MRI) is challenging yet essential for clinical decisions. This study aimed to develop a transformer deep learning (DL) model for detecting pituitary microadenomas based on non-contrast multiparametric MRI and explore the explainability techniques to enhance transparency in convolutional neural network (CNN)-based classification. The primary research question addressed is how to improve the accuracy, generalization, and interpretability of CNNs for microadenomas detection.</p><p><strong>Methods: </strong>Non-contrast multiparametric MRI sella area scans of 590 patients were retrospectively collected from three hospitals. The development and comparison of 2D_DL, 2.5D_DL, 2D_multichannel, and transformer models for classification. By incorporating Explainable AI (XAI), including Gradient-weighted Class Activation Mapping(Grad-CAM) and SHapley Additive exPlanations (SHAP), we improve model interpretability.</p><p><strong>Results: </strong>The performance of the 2D_multichannel model, with an area under the curve (AUC) of 0.893, was better to that of the 2D_T1SAG_DL, 2D_T1COR_DL, 2D_T2COR_DL (AUC, 0.884, 0.779, and 0.846, respectively). The performance of the transformer model, with an area under the curve (AUC) of 0.985, was superior to that of the 2.5D_T1SAG_DL, 2.5D_T1COR_DL, 2.5D_T2COR_DL (AUC, 0.763, 0.863, and 0.835, respectively). The non-contrast MRI-based 2.5D_DL transformer model all shows outperforming performance in the internal and two external test sets (AUC, 0.874, 0.829, and 0.819, respectively).</p><p><strong>Conclusions: </strong>Given its robust diagnostic performance and enhanced interpretability, this model demonstrates significant potential for clinical translation as a decision-support tool in the detection of pituitary microadenomas.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.","authors":"Yujie Liu, Jingyi Xie, Jinyan Li, Hongbo Chen, Siying Dong, Shuquan Zhou, Shufen Liang, Chuanbo Qin, Lin Xiao","doi":"10.1186/s12880-026-02356-6","DOIUrl":"https://doi.org/10.1186/s12880-026-02356-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Deep learning methods have made great progress in the automatic segmentation of nasopharyngeal carcinoma, but challenges remain.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Computer-aided automatic segmentation of nasopharyngeal cancer primary area is of great significance for automatic outlining of nasopharyngeal cancer target areas and accurate prediction of responsiveness and prognosis of metastatic lymph nodes in the neck after radiotherapy. In this paper, we use deep learning methods to construct an automatic segmentation network for gross target volume of nasopharynx, combine clinical factors and radiomics features to establish a radiomics nomogram model, which will then predict the final outcome of metastatic lymph nodes that have not achieved complete remission after radical radiotherapy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Clinical and IMRT radiotherapy plan CT data were retrospectively collected from 69 patients who received intensity-modulated radiation therapy between July 2014 and December 2016. These patients exhibited residual metastatic lymph node lesions without residual primary lesions on the first follow-up MRI and had continuous follow-up records. The median follow-up was 53 months (IQR 39.75-62.37), with 30 patients eventually regressing and 39 patients persisting or progressing. The ct images of 69 radiotherapy plans were randomly divided into training and test sets according to 8:2, and a fusion attention-based model was trained for automatic nasopharyngeal carcinoma segmentation. Based on the unet framework, a fusion attention model was proposed, and a 2·5 d convolutional neural network was used to deal with the anisotropy. An improved channel and spatial attention module is fused in the codec 4 layer to enable the network to focus on small targets. 2d interlaced sparse self-attention module is extended to 3d to better extract the feature information of the tumor target area and solve the problem of low contrast between the target area and the surrounding soft tissues, thus optimizing the overall segmentation effect. The performance of the segmentation model was evaluated using the mean dice coefficient, relative volume error (RVE), average symmetric surface distance (ASSD) and hausdorff distance (HD), using the target area of the primary lesion of nasopharyngeal carcinoma manually outlined by a senior radiation therapy specialist as the gold standard. Radiomics features were extracted using the pyradiomics package, and the classification performance of the radiomics model was assessed by the area under the curve of the receiver operating curve (ROC).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The average dice coefficient, RVE, ASSD and HD of our model for nasopharyngeal carcinoma were 75.05%, 14.63%, 2.224 mm, and 8.75 mm, respectively, which were 11.01%, 26.34%, 3.101 mm, and 52.58 mm better than the baseline 3dunet model. The radiomic features were an effective predictor of tumor outcome in nasopharyngeal carcinoma","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based radiomics to predict peri-device leakage after left atrial appendage closure.","authors":"Xuan Lu, Junfeng Zhang, Jun Li, Wenshuai Ma, Bin Wang, Wangang Guo, Di Zeng, Chiyao Wang, Yi Chu, Chuncheng Gao, Wei Fang, Zhenyu Yang, Xiaona Niu, He Wen, Qiuhe Wang, Yan Li","doi":"10.1186/s12880-026-02387-z","DOIUrl":"https://doi.org/10.1186/s12880-026-02387-z","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluoroscopic image-driven deep learning model for predicting intussusception irreducibility during air enema in children.","authors":"Haichun Zhou, Jian Huang, Youjian Zhang, Haipeng Pan, Zezhou Li, Ruifang Zhang, Xiaohui Ma, Zheming Li, Zhicheng Zhang, Gang Yu","doi":"10.1186/s12880-026-02385-1","DOIUrl":"https://doi.org/10.1186/s12880-026-02385-1","url":null,"abstract":"<p><strong>Background: </strong>Accurate identification of irreducible intussusception during air enema is crucial for optimizing enema strategies. Current methods are limited by subjective interpretation and inconsistent clinical criteria. We developed a deep learning (DL) framework to objectively predict irreducibility using air enema fluoroscopic images.</p><p><strong>Methods: </strong>In this retrospective study, a hybrid ensemble DL model was developed using fluoroscopic images acquired during air enema, comprising 770 irreducible and 1214 reducible cases. Model performance was evaluated on a real-world test set (46 irreducible vs. 802 reducible cases) and an external test set (9 irreducible vs. 101 reducible cases), with benchmarking against state-of-the-art techniques. The model's performance was further compared with radiologists' interpretations, and its ability to improve diagnostic accuracy was assessed. Performance was evaluated using receiver operating characteristic (ROC) analysis and confusion matrix-derived metrics.</p><p><strong>Results: </strong>The proposed model achieved areas under the ROC curves (AUCs) of 0.89 (95% CI: 0.836-0.944) and 0.883 (95% CI: 0.78-0.968) on the real-world and external test sets, respectively, outperforming comparative methods (AUC ranges: 0.823-0.877 and 0.634-0.826). The model demonstrated superior performance compared with that of the intermediate radiologist (AUC: 0.89 vs. 0.804; P < 0.001) and comparable performance to that of a senior radiologist (AUC: 0.89 vs. 0.842; P = 0.108). When used as an assistive tool, the model significantly improved radiologists' diagnostic performance (all P < 0.01), with AUC improvements of 0.095-0.072, balanced accuracy gains of 8.6-11.7%, and specificity increases of 18.7-22.6%.</p><p><strong>Conclusions: </strong>The proposed model demonstrated promising diagnostic performance in identifying irreducible intussusception and may serve as an effective decision-support tool to improve radiologists' diagnostic accuracy during air enema procedure.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gray matter volume and structural covariance alterations in young males with childhood-onset growth hormone deficiency.","authors":"Shengyu Wang, Wenting Su, Xiao Liu, Yue Zhao, Ming Zhang, Lunbo Lu, Suzhen Zhang, Dongmei Wang, Wenjie Yang, Yuwen Zhang, Xinping Kuai","doi":"10.1186/s12880-026-02388-y","DOIUrl":"https://doi.org/10.1186/s12880-026-02388-y","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should pre-measurement physical activity be standardized in muscle thickness and stiffness evaluations? - A randomized controlled four arm cross-over study.","authors":"Konstantin Warneke, Gerit Plöschberger, Manuel Oraze, Daniel Jochum, Stanislav D Siegel","doi":"10.1186/s12880-026-02373-5","DOIUrl":"https://doi.org/10.1186/s12880-026-02373-5","url":null,"abstract":"<p><strong>Background: </strong>High standardization is of crucial relevance for reliability in imaging diagnostics. When quantifying muscle properties (muscle thickness and stiffness) by ultrasound or myotonometry, internal validity can be compromised by examiner-related factors and participant biologic variability. A frequently neglected source of bias is pre-measurement activity, which may acutely alter muscle perfusion and muscle blood inflow.</p><p><strong>Methods: </strong>The acute influence of different physical activity routines on tissue parameters was investigated in 30 healthy participants (16 m, 14f). Ten minutes before, immediately before, immediately after and 10 min retention of cycling, jogging, calf raises or control, muscle thickness and stiffness measurements via shear wave elastography (SWE) and myotonometry were measured.</p><p><strong>Results: </strong>Reliability was excellent for muscle thickness (ICC = 0.94-1.00; CV = 1.7-9.1%), good-excellent for SWE stiffness (ICC = 0.68-0.97; CV = up to 26% for inter-day) and myotonometry (muscle ICC = 0.77-0.98; CV = 4.0-17% tendon 0.86-0.93 (CV = 11-17%). Muscle thickness significantly increased after calf raises (d = 1.60, 10.3%) and jogging (d = 0.60, 3.0%), without effects after cycling or control. Shear-wave elastography showed muscle stiffness decreased after calf raises (d=-0.73, -16.7%). Myotonometry indicated a stiffness increase (d = 1.04, 20.1%). The 10-minute retention showed consistent effects for muscle thickness (d = 0.80, 5.3%) and stiffness (SWE: d = 0.78, 21.1%, myotonometry: d=-0.82, -13.0%).</p><p><strong>Conclusion: </strong>Pre-measurement activity could systematically affect muscle thickness and stiffness with dependence on activity type and intensity. This highlights the importance of monitoring pre-measurement activity to minimize potential reliability issues as this, depending on several potential moderators, could enhance the random error if within sample pre-measurement activity is not standardized. Before ultrasound evaluation, for some activity (i.e. calf raises), > 10 min of rest was required to diminish this bias.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised radiomics-driven endotyping of chronic rhinosinusitis with nasal polyps: a multimodal characterization of CT imaging, clinical phenotypes, and proteomic profiling.","authors":"Hongfei Zhao, Qi Wang, Yan Hao, Pengyi Yu, Jingjing Li, Jianwei Wang, Haicheng Zhang, Yu Zhang, Yujuan Yang, Yakui Mou, Ning Mao, Xicheng Song","doi":"10.1186/s12880-026-02369-1","DOIUrl":"https://doi.org/10.1186/s12880-026-02369-1","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of cardiac allograft vasculopathy in heart transplant patients using multidimensional dynamic CTA and principal components analysis.","authors":"Xuesong Zhang, Ming Yang, Tianming Huang, Qian Qin, Peidong Qian, Yuanming Luo, Jing Wang","doi":"10.1186/s12880-026-02368-2","DOIUrl":"https://doi.org/10.1186/s12880-026-02368-2","url":null,"abstract":"<p><strong>Background: </strong>Cardiac allograft vasculopathy (CAV) is a major cause of late graft failure post heart transplantation. While coronary angiography remains the gold standard, non-invasive techniques, such as CT angiography (CTA), are emerging alternatives. Electrocardiogram-gated multidimensional dynamic CTA (MD CTA) allows to track dynamic motions of coronary artery throughout the cardiac cycles, potentially revealing valuable insights into coronary abnormalities.</p><p><strong>Methods: </strong>Principal component analysis (PCA) is employed to analyze the left anterior descending artery (LAD) motion, aiming to assess CAV in heart transplant patients. The motions were determined through registration of MD CTA images, and the incremental displacement of LAD between adjacent phases in a complete cardiac cycle was used as input in PCA. Two-sample t-test and logistic regression were used to compare and differentiate the control and CAV group based on PCA results, and a linear regression was used to correlate PCA results with the degree of stenosis.</p><p><strong>Results: </strong>The resulted contribution rate of the first principal component (PC1) in control group (0.61 ± 0.05) is significantly higher than the value observed in CAV group (0.46 ± 0.06, p < 0.05). A univariate logistic model (AUC = 0.97) based on contribution rate can sharply discriminate the control and CAV group. Importantly, a negative correlation was found between the contribution rate of PC1 and the degree of stenosis in CAV group.</p><p><strong>Conclusion: </strong>This study employs PCA and multidimensional CTA to analyze LAD dynamic motion for assessment of CAV. The contribution rate of the first principal component (PC1) was identified as a promising indicator for evaluating CAV and tracking stenosis progression. These findings offer a quantitative, non-invasive approach that may enhance clinical decision-making in post heart transplantation care.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}