Academic Radiology最新文献

{"title":"Development of a Nomogram for Predicting Tuberous Sclerosis Complex Genotypes in Children Using Advanced Diffusion MRI and Clinical Data.","authors":"Hui Sun, Zhiping Yan, Junhang Gao, Yingzhi Zheng, Yueyu Zheng, Yang Song, Yongji Liu, Zhixian Lin, Wencai Shen, Jin Fang, Hong Qu, Yanzhao Diao, Hongmei Liu, Sulian Su, Guihua Jiang","doi":"10.1016/j.acra.2025.03.022","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.022","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Tuberous sclerosis complex (TSC) is a multisystem genetic disorder. Focusing on central nervous system manifestations, this study developed an imaging-clinical model combining advanced diffusion MRI parameters with neurological clinical features to distinguish TSC1 vs. TSC2 genotypes.</p><p><strong>Materials and methods: </strong>Eighty-eight patients newly diagnosed with TSC were enrolled. All underwent a stratified genetic testing strategy comprising whole-exome sequencing, whole-genome sequencing, and tissue-specific deep sequencing. Diffusion spectrum imaging provided parameters from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI). A combined prediction model was constructed using logistic regression and validated via bootstrap resampling.</p><p><strong>Results: </strong>A younger age of onset, autism, neuropsychiatric disorders, intracellular volume fraction, and q-space inverse variance were independently associated with TSC2 mutations. The combined model achieved an AUC of 0.879 (95% CI: 0.841-0.917) in the training set and 0.864 (95% CI: 0.803-0.926) in the validation set. By DeLong's test, it significantly outperformed the clinical model (AUC: 0.637, 95% CI: 0.552-0.723; p < 0.001), while the difference from the imaging model (AUC: 0.833, 95% CI: 0.763-0.903) was not statistically significant (p = 0.068). However, net reclassification (NRI = 0.702, p < 0.001) and integrated discrimination improvement (IDI = 0.097, p < 0.001) both supported the combined model's superior classification ability.</p><p><strong>Conclusion: </strong>Integrating advanced diffusion MRI parameters with clinical data significantly improves prediction of TSC1 vs. TSC2 genotypes. This combined approach offers valuable support for early diagnosis and personalized treatment in TSC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Resonance Elastography Derived Stiffness to Predict Postoperative Pancreatic Fistula After Partial Pancreatectomy.","authors":"Dingxia Liu, Jiejun Chen, Yunfei Zhang, Yajia Gu, Xiuzhong Yao","doi":"10.1016/j.acra.2025.03.028","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.028","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To investigate the magnetic resonance elastography (MRE)-derived pancreatic stiffness for predicting the occurrence of clinically relative postoperative pancreatic fistula (CR-POPF) in patients with partial pancreatectomy, and establish a predictive model for POPF before surgery.</p><p><strong>Background: </strong>Pancreatic stiffness reflects fibrosis and fat infiltration, which are associated with CR-POPF. But preoperative prediction remains a challenge. MRE was proven to evaluate pancreatic stiffness accurately, potentially being a predictive imaging biomarker of POPF.</p><p><strong>Methods: </strong>This prospective study included adult patients who underwent magnetic resonance imaging with MRE sequence and subsequent partial pancreatectomy between August 2021 and December 2023. The relationships of MRE stiffness and main pancreatic duct diameter (MPD) with the risk of POPF were analyzed using logistic regression. Independent risk factors were identified to construct the nomogram prediction model. The predictive performance of each parameter and the model was conducted by calculating the area under the ROC curve (AUC).</p><p><strong>Results: </strong>A total of 73 patients (age 58.99±12.55 years; 30 pancreatoduodenectomy and 43 distal pancreatectomy) were enrolled, among whom 15 developed CR-POPF and 58 did not. After conducting uni- and multivariate logistic regression analyses, high BMI was found to be an independent risk factor for the occurrence of POPF (OR=2.916, 95% CI: 1.472-9.394, P=0.02), while high pancreatic MRE stiffness (OR=0.0633, 95% CI: 0.0022-0.5273, P=0.04) and large MPD (OR=0.0728, 95% CI: 0.003-0.5165, P=0.04) were independent protective factors. A preoperative prediction model for POPF was constructed by combining the three indicators, which has excellent predictive performance with an AUC of 0.97.</p><p><strong>Conclusion: </strong>MRE can quantitatively evaluate the mechanical property of pancreas, which is a reliable indicator for predicting the risk of POPF. The POPF prediction model established by combining BMI, pancreatic stiffness value, and MPD has promising clinical application prospects.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer.","authors":"Yexin Su, Hongyue Zhao, Zhehao Lyu, Peng Xu, Ziyue Zhang, Huiting Zhang, Mengjiao Wang, Lin Tian, Peng Fu","doi":"10.1016/j.acra.2025.03.014","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.014","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Preoperative knowledge of the status of lymphovascular invasion (LVI) status in colorectal cancer (CRC) patients can provide valuable information for choosing appropriate treatment strategies. This study aimed to explore the value of heterogeneity features derived from the habitat analysis of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images in predicting LVI.</p><p><strong>Materials and methods: </strong>Pretreatment ¹⁸F-FDG PET/computed tomography (CT) images from 177 patients diagnosed with CRC were retrospectively obtained (training cohort, n=106; validation cohort, n=71). Conventional radiomics features and habitat-derived tumor heterogeneity features were extracted from ¹⁸F-FDG PET scans. The output probabilities of the imaging-based random forest model were used to generate a radiomics score (Radscore) and intratumoral heterogeneity score (ITHscore). Multivariate logistic regression analysis was used to determine the independent risk factors for LVI. On this basis, four LVI status classification models were developed using (a) clinical variables (Clinical model), (b) tumor heterogeneity features (ITHscore model), (c) radiomics features (Radscore model), and (d) clinical variables, tumor heterogeneity features, and radiomics features (Combined model). The area under the curve (AUC) and decision curve analysis were used to evaluate model performance.</p><p><strong>Results: </strong>Among all of the variables, the PET/CT-reported lymph node status, ITHscore, and Radscore were retained as predictors related to the risk of LVI in CRC patients (P<0.05). The predictive effect of the ITHscore model (AUC: 0.712) was better than that of the Radscore model (AUC: 0.650) and Clinical model (AUC: 0.652) in the validation cohort. The Combined model achieved better classification effects and clinical usefulness, and the AUCs of the training and validation cohorts were 0.857 and 0.798, respectively. A nomogram of the Combined model was established, and the calibration plot was well fitted (P>0.05). In addition, the results of Spearman's rank correlation tests showed that there was no significant correlation between the ITHscore and Radscore (R=0.044, P=0.655 in the training cohort; R=0.067, P=0.580 in the validation cohort).</p><p><strong>Conclusion: </strong>Our results showed that the ITHscore is a novel and stable quantitative indicator of LVI and is helpful for effectively facilitating the risk stratification of LVI in CRC patients after integrating clinical variables and radiomics features.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating CT Dose Variation Across Scanner Technologies: Implications for Compliance with New CMS CT Radiation Dose Measure.","authors":"Madan M Rehani, Maria T Mataac, Xinhua Li","doi":"10.1016/j.acra.2025.03.026","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.026","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>In 2025, the Centers for Medicare and Medicaid Services introduced a computed tomography (CT) dose measure for pay-for-performance programs. Hospitals employ diverse scanner fleets, but the impact of scanner technologies on dose benchmarking remains unclear. This study evaluates dose variation across scanner models and its benchmarking implications.</p><p><strong>Materials and methods: </strong>A retrospective analysis examined CT exams from January to December 2023 at a quaternary-care hospital, focusing on median-sized adults (water-equivalent diameter: 16-19cm head, 18-22cm neck, 29-33cm torso). Dose indices from seven scanner models across eight adult exams were evaluated. The 50^th and 75^th percentile doses were calculated per exam and scanner model combination, compared to American College of Radiology achievable doses and diagnostic reference levels.</p><p><strong>Results: </strong>Analyzing 34,166 studies, significant dose variations with scanner models emerged. Head without contrast (N=21,654) had median volume CT-dose-index (CTDI_vol) of 36.1-68.3mGy and dose-length-product (DLP) 704-1307.8mGy·cm; 75^th percentiles were 43.1-69.1mGy and 838.2-1378.1mGy·cm. Chest with contrast (N=3065) showed median CTDI_vol of 6.7-16.1mGy and DLP 263.8-579.7mGy·cm; 75^th percentiles were 8.2-19.5mGy and 329-713.7mGy·cm. Abdomen/pelvis with contrast (N=2740) had median CTDI_vol of 8.8-15.2mGy and DLP 429.3-782.1mGy·cm; 75^th percentiles were 10-18.5mGy and 533.4-941.5mGy·cm. While the number of studies was smaller, five other exams also showed large dose variations across scanner models.</p><p><strong>Conclusion: </strong>Single-value dose benchmarks ignoring scanner technology may be inadequate, even for similar-sized patients, potentially requiring scanner removal. Incorporating benchmarks with diverse technologies could prevent increased healthcare costs and patient care disruptions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.","authors":"Yi Zhou, Guihan Lin, Weiyue Chen, Yongjun Chen, Changsheng Shi, Zhiyi Peng, Ling Chen, Shibin Cai, Ying Pan, Minjiang Chen, Chenying Lu, Jiansong Ji, Shuzheng Chen","doi":"10.1016/j.acra.2025.03.010","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.010","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.</p><p><strong>Methods: </strong>This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.</p><p><strong>Results: </strong>Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying \"ADC_wavelet-HLH_glszm_ZoneEntropy\" and \"DCE_wavelet-HLL_gldm_DependenceVariance\" as the most significant features for the model's predictions.</p><p><strong>Conclusion: </strong>The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.</p><p><strong>Critical relevance statement: </strong>An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Synthetic T2*-Weighted Gradient Echo Images of the Knee with an Open-source Deep Learning Model.","authors":"Konstantinos Vrettos, Evangelia E Vassalou, Grigoria Vamvakerou, Apostolos H Karantanas, Michail E Klontzas","doi":"10.1016/j.acra.2025.03.015","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.015","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of hemosiderin), chondrocalcinosis and the evaluation of the physis in pediatric patients. Herein, we aimed to develop an open-source deep learning model that creates synthetic T2*W images of the knee using fat-suppressed intermediate-weighted images.</p><p><strong>Materials and methods: </strong>A cycleGAN model was trained with 12,118 sagittal knee MR images and tested on an independent set of 2996 images. Diagnostic interchangeability of synthetic T2*W images was assessed against a series of findings. Voxel intensity of four tissues was evaluated with Bland-Altman plots. Image quality was assessed with the use of root mean squared error (NRMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Code, model and a standalone executable file are provided on github.</p><p><strong>Results: </strong>The model achieved a median NRMSE, PSNR and SSIM of 0.5, 17.4, and 0.5, respectively. Images were found interchangeable with an intraclass correlation coefficient >0.95 for all findings. Mean voxel intensity was equal between synthetic and conventional images. Four types of artifacts were identified: geometrical distortion (86/163 cases), object insertion/omission (11/163 cases), a wrap-around-like (26/163 cases) and an incomplete fat-suppression artifact (120/163 cases), which had a median 0 impact (no impact) on the diagnosis.</p><p><strong>Conclusion: </strong>In conclusion, the developed open-source GAN model creates synthetic T2*W images of the knee of high diagnostic value and quality. The identified artifacts had no or minor effect on the diagnostic value of the images.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal Ablation for Low-risk Papillary Thyroid Carcinoma: Comparing Outcomes in T1N0M0 and T2N0M0 PTC.","authors":"Yu-Tong Liu, Ying Wei, Zhen-Long Zhao, Jie Wu, Shi-Liang Cao, Na Yu, Yan Li, Li-Li Peng, Ming-An Yu","doi":"10.1016/j.acra.2025.03.019","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.019","url":null,"abstract":"<p><strong>Background: </strong>Thermal ablation (TA) has demonstrated promising treatment efficacy and safety in T1N0M0 papillary thyroid carcinoma (PTC). However, the efficacy and safety of TA for T2N0M0 PTC still lack sufficient evidence.</p><p><strong>Purpose: </strong>To compare the technical effectiveness, disease progression, and complications of TA in the treatment of solitary T1N0M0 versus solitary T2N0M0 PTC.</p><p><strong>Materials and methods: </strong>In this retrospective study, 1159 patients with PTC treated with TA from January 2015 to June 2024 were included and divided into two groups according to tumor stage. Propensity score matching (PSM) was used to control for confounding factors. Kaplan-Meier curves were used to analyze the disease progression.</p><p><strong>Results: </strong>After PSM (1:4), 41 patients (median age 35 years [IQR 30-49]; 30 women) were included in the T2 group, and 164 patients (median age 34 years [IQR 29-43]; 108 women) were included in the T1 group. The median follow-up durations were 26 months (IQR 13-49) for the T2 group and 25 months (IQR 12.3-43) for the T1 group. The technical success rates were 100% in the two groups. Statistical analysis showed no significant differences in disease progression between the T1 and T2 groups (0.6% vs. 4.9%, P=0.103), nor in disease progression-free survival rates (98.2% vs. 88.6%, log-rank P=0.052). The incidence of major complications was higher in the T2 group than that in the T1 group (1.8% vs. 17.1%, P=0.001). No permanent hoarseness was observed in the two groups.</p><p><strong>Conclusion: </strong>TA could be a safe and effective option in the treatment of solitary T2N0M0 PTC. No significant difference was observed in disease progression between T1N0M0 and T2N0M0 PTC.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Deep Learning in Breast MRI: Technical Advances and Clinical Promise.","authors":"Yuki Arita, Noam Nissan","doi":"10.1016/j.acra.2025.03.047","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.047","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 2024 Association of Academic Radiologists and Industry Think Tank: Unmet Clinical Needs and Collaborative Resourcing.","authors":"Kara Gaetke-Udager, Christopher Hess, Mary Mahoney, Jeffrey G Jarvik, Pablo R Ros","doi":"10.1016/j.acra.2025.03.016","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.016","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lateralized Amplitude Low-frequency Fluctuation Alterations in Mild Cognitive Impairment as a Biomarker for Early Alzheimer's Disease Detection.","authors":"Lichang Yao, Zhilin Zhang","doi":"10.1016/j.acra.2025.03.035","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.035","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}