Academic Radiology最新文献

{"title":"From Faster Frames to Flawless Focus: Deep Learning HASTE in Postoperative Single Sequence MRI.","authors":"Clarissa Hosse, Uli Fehrenbach, Fabio Pivetta, Thomas Malinka, Moritz Wagner, Thula Walter-Rittel, Bernhard Gebauer, Johannes Kolck, Dominik Geisel","doi":"10.1016/j.acra.2025.05.039","DOIUrl":"https://doi.org/10.1016/j.acra.2025.05.039","url":null,"abstract":"<p><strong>Background: </strong>This study evaluates the feasibility of a novel deep learning-accelerated half-fourier single-shot turbo spin-echo sequence (HASTE-DL) compared to the conventional HASTE sequence (HASTE_S) in postoperative single-sequence MRI for the detection of fluid collections following abdominal surgery. As small fluid collections are difficult to visualize using other techniques, HASTE-DL may offer particular advantages in this clinical context.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted on 76 patients (mean age 65±11.69 years) who underwent abdominal MRI for suspected septic foci following abdominal surgery. Imaging was performed using 3-T MRI scanners, and both sequences were analyzed in terms of image quality, contrast, sharpness, and artifact presence. Quantitative assessments focused on fluid collection detectability, while qualitative assessments evaluated visualization of critical structures. Inter-reader agreement was measured using Cohen's kappa coefficient, and statistical significance was determined with the Mann-Whitney U test.</p><p><strong>Results: </strong>HASTE-DL achieved a 46% reduction in scan time compared to HASTE_S, while significantly improving overall image quality (p<0.001), contrast (p<0.001), and sharpness (p<0.001). The inter-reader agreement for HASTE-DL was excellent (κ=0.960), with perfect agreement on overall image quality and fluid collection detection (κ=1.0). Fluid detectability and characterization scores were higher for HASTE-DL, and visualization of critical structures was significantly enhanced (p<0.001). No relevant artifacts were observed in either sequence.</p><p><strong>Conclusion: </strong>HASTE-DL offers superior image quality, improved visualization of critical structures, such as drainages, vessels, bile and pancreatic ducts, and reduced acquisition time, making it an effective alternative to the standard HASTE sequence, and a promising complementary tool in the postoperative imaging workflow.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499013","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":"MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.","authors":"Yuling Yang, Bowen Zheng, Bin Zou, Renyi Liu, Rongqiang Yang, Qifeng Chen, Yongfei Guo, Shuiquan Yu, Biwei Chen","doi":"10.1016/j.acra.2025.05.059","DOIUrl":"https://doi.org/10.1016/j.acra.2025.05.059","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.</p><p><strong>Methods: </strong>This retrospective study enrolled 214 patients with pathologically diagnosed prostate cancer from May 2013 to January 2025, including 93 cases with bone metastasis and 159 cases with high-grade Gleason scores. Clinical, pathological and MRI data were collected. An nnUNet model automatically segmented the prostate in MRI scans. K-means clustering identified subregions within the entire prostate in T2-FS images. Senior radiologists manually segmented regions of interest (ROIs) in prostate lesions. Radiomics features were extracted from these habitat subregions and lesion ROIs. These features combined with clinical features were utilized to build multiple machine learning classifiers to predict bone metastasis and high-grade Gleason scores while a K-means clustering method was applied to obtain habitat subregions within the whole prostate. Finally, the models underwent interpretable analysis based on feature importance.</p><p><strong>Results: </strong>The nnUNet model achieved a mean Dice coefficient of 0.970 for segmentation. Habitat analysis using 2 clusters yielded the highest average silhouette coefficient (0.57). Machine learning models based on a combination of lesion radiomics, habitat radiomics, and clinical features achieved the best performance in both prediction tasks. The Extra Trees Classifier achieved the highest AUC (0.900) for predicting bone metastasis, while the CatBoost Classifier performed best (AUC 0.895) for predicting high-grade Gleason scores. The interpretability analysis of the optimal models showed that the PSA clinical feature was crucial for predictions, while both habitat radiomics and lesion radiomics also played important roles.</p><p><strong>Conclusion: </strong>The study proposed an automated prostate habitat analysis for prostate cancer, enabling a comprehensive analysis of tumor heterogeneity. The machine learning models developed achieved excellent performance in predicting the risk of bone metastasis and high-grade Gleason scores in prostate cancer. This approach overcomes the limitations of manual feature extraction, and the inadequate analysis of heterogeneity often encountered in traditional radiomics, thereby improving model performance.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486855","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":"Insights Into the CEUS-Based Granulomatous Mastitis Recurrence Model in Terms of Clinical Applicability and Methodological Transparency.","authors":"Deniz Esin Tekcan Sanli","doi":"10.1016/j.acra.2025.06.006","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.006","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486850","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":"Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.","authors":"Xia Wang, Xingwei Wang, Jie Lei, Chang Rong, Xiaomin Zheng, Shuai Li, Yankun Gao, Xingwang Wu","doi":"10.1016/j.acra.2025.06.005","DOIUrl":"https://doi.org/10.1016/j.acra.2025.06.005","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the intestinal wall, mesenteric fat, and visceral fat to differentiate between ulcerative colitis (UC) and colonic Crohn's disease (CD).</p><p><strong>Methods: </strong>Clinical and imaging data from 116 patients with inflammatory bowel disease (IBD) (68 with UC and 48 with colonic CD) were retrospectively collected. Radiomic features were extracted from venous-phase CTE images. Feature selection was performed via the intraclass correlation coefficient (ICC), correlation analysis, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. Support vector machine models were constructed using features from individual and combined regions, with model performance evaluated using the area under the ROC curve (AUC).</p><p><strong>Results: </strong>The combined radiomic model, integrating features from all three regions, exhibited superior classification performance (AUC= 0.857, 95% CI, 0.732-0.982), with a sensitivity of 0.762 (95% CI, 0.547-0.903) and specificity of 0.857 (95% CI, 0.601-0.960) in the testing cohort. The models based on features from the intestinal wall, mesenteric fat, and visceral fat achieved AUCs of 0.847 (95% CI, 0.710-0.984), 0.707 (95% CI, 0.526-0.889), and 0.731 (95% CI, 0.553-0.910), respectively, in the testing cohort. The intestinal wall model demonstrated the best calibration.</p><p><strong>Conclusion: </strong>This study demonstrated the feasibility of constructing machine learning models based on radiomic features of the intestinal wall, mesenteric fat, and visceral fat to distinguish between UC and colonic CD.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486854","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":"AI is not Always the Enemy: Please, Disambiguate Before You Hate.","authors":"Teodoro Martín-Noguerol, Pilar López-Úbeda, Antonio Luna","doi":"10.1016/j.acra.2025.06.028","DOIUrl":"10.1016/j.acra.2025.06.028","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340606","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":"Artificial Intelligence Language Models to Translate Professional Radiology Mammography Reports Into Plain Language - Impact on Interpretability and Perception by Patients.","authors":"Dusan Pisarcik, Marc Kissling, Jakob Heimer, Monika Farkas, Cornelia Leo, Rahel A Kubik-Huch, André Euler","doi":"10.1016/j.acra.2025.05.065","DOIUrl":"10.1016/j.acra.2025.05.065","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study aimed to evaluate the interpretability and patient perception of AI-translated mammography and sonography reports, focusing on comprehensibility, follow-up recommendations, and conveyed empathy using a survey.</p><p><strong>Materials and methods: </strong>In this observational study, three fictional mammography and sonography reports with BI-RADS categories 3, 4, and 5 were created. These reports were repeatedly translated to plain language by three different large language models (LLM: ChatGPT-4, ChatGPT-4o, Google Gemini). In a first step, the best of these repeatedly translated reports for each BI-RADS category and LLM was selected by two experts in breast imaging considering factual correctness, completeness, and quality. In a second step, female participants compared and rated the translated reports regarding comprehensibility, follow-up recommendations, conveyed empathy, and additional value of each report using a survey with Likert scales. Statistical analysis included cumulative link mixed models and the Plackett-Luce model for ranking preferences.</p><p><strong>Results: </strong>40 females participated in the survey. GPT-4 and GPT-4o were rated significantly higher than Gemini across all categories (P<.001). Participants >50 years of age rated the reports significantly higher as compared to participants of 18-29 years of age (P<.05). Higher education predicted lower ratings (P=.02). No prior mammography increased scores (P=.03), and AI-experience had no effect (P=.88). Ranking analysis showed GPT-4o as the most preferred (P=.48), followed by GPT-4 (P=.37), with Gemini ranked last (P=.15).</p><p><strong>Conclusion: </strong>Patient preference differed among AI-translated radiology reports. Compared to a traditional report using radiological language, AI-translated reports add value for patients, enhance comprehensibility and empathy and therefore hold the potential to improve patient communication in breast imaging.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334405","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":"Preoperative Nomogram-Based Assessment to Identify GBM Patients Who Do not Derive Survival Benefit From GTR Compared to STR.","authors":"Lei He, Mingfang Luo, Longlin Yin, Jinzhou Feng, Ruxiang Xu, Fan Fei","doi":"10.1016/j.acra.2025.06.003","DOIUrl":"10.1016/j.acra.2025.06.003","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>Glioblastoma, IDH-wildtype (GBM), the most common primary malignant brain tumor in adults, has a median overall survival of 11-15 months. While gross total resection (GTR) generally improves survival compared to subtotal resection (STR), certain patient subgroups may not benefit from more extensive resection.</p><p><strong>Methods: </strong>This study developed a nomogram-based predictive model using preoperative clinical and imaging data to identify GBM patients who may or may not benefit from GTR compared to STR. Data from the UCSF-PDGM dataset (N=371) were used to construct the model, with external validation performed using the UPENN-GBM dataset (N=457).</p><p><strong>Results: </strong>Multivariate Cox regression identified age, extent of resection (EOR), and volume all (necrotic, enhancing, and peritumoral regions of tumor) as independent prognostic factors. The nomogram stratified patients into low-, medium-, and high-score groups based on age and tumor volume. Results showed that GTR significantly improved survival in patients with scores between 55 and 95, but not in those with scores below 55 or above 95. Younger patients with smaller tumors (usually with score <55) and older patients with larger tumors (usually with score >95) derived limited additional survival benefit from GTR compared to STR. The nomogram-based classification outperformed methods relying solely on age or tumor volume.</p><p><strong>Conclusion: </strong>These findings suggest that preoperative assessment using the nomogram can guide individualized surgical strategies, optimizing the extent of resection for GBM patients. However, prospective studies are warranted to further validate the reliability of the findings in this research.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337200","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 Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.","authors":"Wenbo Li, Qiyu Zhang, Dale Black, Huanjun Ding, Carlos Iribarren, Alireza Shojazadeh, Sabee Molloi","doi":"10.1016/j.acra.2025.05.036","DOIUrl":"10.1016/j.acra.2025.05.036","url":null,"abstract":"<p><strong>Background: </strong>Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.</p><p><strong>Purpose: </strong>To develop and validate a deep-learning model capable of detecting, segmenting, and quantifying BAC in mammograms, improving mammographic screening for cardiovascular risk assessment.</p><p><strong>Materials and methods: </strong>We conducted a retrospective study using mammograms from 369 patients. The study utilized a modified U-Net architecture that incorporates Hausdorff loss, Dice loss, and Binary Cross-Entropy (BCE) loss for segmentation and subsequent quantification. The model's performance was assessed using the Dice score, BCE loss for segmentation accuracy, linear fit, and Bland-Altman analysis for quantification accuracy.</p><p><strong>Results: </strong>Our model achieved high segmentation accuracy with Dice scores of 0.90 for the training set and 0.89 for the validation set. Quantification reliability was validated through Bland-Altman analysis, showing a mean difference of -0.98 mg of calcium in the training set. The model also demonstrated high classification accuracy with F1 scores of 0.97 and 0.93 for validation and training sets, respectively, in BAC detection.</p><p><strong>Conclusion: </strong>The deep-learning framework substantially improves BAC detection, segmentation, and quantification in mammograms, advancing the accuracy and efficiency of cardiovascular risk screening. This study supports the potential for integrated dual-purpose screening in women's healthcare.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337201","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":"Application of the 2024 International Association of Pancreatology Guidelines for Identifying (Pre)Malignancy Among Presumed Intraductal Papillary Mucinous Neoplasms via CT and MRI.","authors":"Wenyi Deng, Chunhua Yang, Fuze Cong, Feiyang Xie, Xiuli Li, Zhengyu Jin, Huadan Xue","doi":"10.1016/j.acra.2025.05.058","DOIUrl":"10.1016/j.acra.2025.05.058","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To evaluate the diagnostic performance of the 2024 International Association of Pancreatology (IAP) guidelines for presumed intraductal papillary mucinous neoplasms (IPMNs).</p><p><strong>Materials and methods: </strong>We retrospectively analyzed 181 presumed IPMNs with preoperative contrast-enhanced CT and 129 presumed IPMNs with preoperative contrast-enhanced MRI. All high-risk stigmata (HRS) and worrisome features (WF) in the 2024 IAP guidelines were assessed. Multivariable logistic regression analysis developed nomograms for identifying (pre)malignancy among presumed IPMNs via CT and MRI. The diagnostic performance of nomograms was validated and compared with the 2017 IAP guidelines in independent testing cohorts.</p><p><strong>Results: </strong>Elevated serum carbohydrate antigen 19-9, main pancreatic duct (MPD) ≥ 10 mm, thickened enhancing cyst wall, enhanced mural nodule or solid component, and lymphadenopathy were associated with (pre)malignancy via CT and MRI. MPD ≥ 5 mm and abrupt MPD caliber changes with distal atrophy were also related to (pre)malignancy via CT. Both the CT and MRI nomograms demonstrated satisfactory and improved diagnostic performance compared to HRS in the 2017 IAP guidelines (accuracy: 77.9% vs 67.7%, p = 0.039 for CT and 75.5% vs 59.2%, p = 0.021 for MRI) and the six-point scale based on the 2017 version (AUC: 0.808 vs 0.726, p = 0.039 for CT and 0.865 vs 0.631, p < 0.001 for MRI) in the testing cohorts. The intermodality agreement between CT and MRI was moderate to excellent, except for thickened enhancing cyst wall.</p><p><strong>Conclusion: </strong>The nomograms based on the 2024 IAP guidelines effectively identified (pre)malignant lesions among presumed IPMNs and demonstrated improvement over the 2017 version when evaluated via both CT and MRI in the testing cohorts.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337199","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":"Usefulness of [¹⁸F]FAPI-04 and [¹⁸F]FDG PET/CT for the Detection of Peritoneal Carcinomatosis: A Comparative Study.","authors":"Chengzhi Jiang, Kai Zheng, Yanyin Zhang, Xiang Peng, Jian Yang, Hui Ye, Yue Chen","doi":"10.1016/j.acra.2025.05.067","DOIUrl":"10.1016/j.acra.2025.05.067","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>In this study, we aimed to compared the performance of [¹⁸F]FAPI-04 and [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography/computer tomography (PET/CT) in the evaluation of peritoneal carcinomatosis.</p><p><strong>Materials and methods: </strong>71 patients with suspected peritoneal malignancies were enrolled in our study. All the participants underwent both [¹⁸F]FAPI-04 and [¹⁸F]FDG PET/CT imaging within 7 days. The detection rates, diagnostic accuracies, semiquantitative parameters of the tracers, peritoneal cancer index (PCI) scores, and tumor markers were evaluated and compared.</p><p><strong>Results: </strong>Among the 71 patients, 40 patients were diagnosed with peritoneal carcinomatosis, and 31 were true-negative patients. The sensitivity and accuracy of [¹⁸F]FAPI-04 PET/CT were higher than those of [¹⁸F]FDG PET/CT (sensitivity: 92.50% vs. 72.50%, p=0.003; accuracy: 91.55% vs. 80.28%, p<0.001), particularly in patients with gastric cancer. The SUVmax, tumor-to-liver background ratio (TBR-L), tumor-to-descending aorta ratio (TBR-A), and PCI score were significantly higher for [¹⁸F]FAPI-04 PET/CT than [¹⁸F]FDG PET/CT (all p<0.05). In the [¹⁸F]FAPI-04 PET/CT group, the PCI score, TBR-L, TBR-A, TBR-M and SUVmax were higher in the high level group than the low level group (all p<0.05). The carbohydrate antigen 125 (CA 125) levels were strongly correlated with the PCI of both [¹⁸F]FAPI-04 and [¹⁸F]FDG PET/CT.</p><p><strong>Conclusion: </strong>[¹⁸F]FAPI-04 PET/CT outperformed [¹⁸F]FDG PET/CT in the evaluation of peritoneal carcinomatosis, particularly in patients with gastric cancer. Furthermore, [¹⁸F]FAPI-04 PET/CT may be used for the assessment of peritoneal carcinomatosis in patients, especially FAPI-PCI.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337204","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}