European Radiology最新文献

{"title":"Synthetic hematocrit from virtual non-contrast images for myocardial extracellular volume evaluation with photon-counting detector CT.","authors":"Victor Mergen, Nicolas Ehrbar, Lukas J Moser, Johannes C Harmes, Robert Manka, Hatem Alkadhi, Matthias Eberhard","doi":"10.1007/s00330-024-10865-7","DOIUrl":"10.1007/s00330-024-10865-7","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the accuracy of a synthetic hematocrit derived from virtual non-contrast (VNC) and virtual non-iodine images (VNI) for myocardial extracellular volume (ECV) computation with photon-counting detector computed tomography (PCD-CT).</p><p><strong>Materials and methods: </strong>Consecutive patients undergoing PCD-CT including a coronary CT angiography (CCTA) and a late enhancement (LE) scan and having a blood hematocrit were retrospectively included. In the first 75 patients (derivation cohort), CCTA and LE scans were reconstructed as VNI at 60, 70, and 80 keV and as VNC with quantum iterative reconstruction (QIR) strengths 2, 3, and 4. Blood pool attenuation (BP_mean) was correlated to blood hematocrit. In the next 50 patients (validation cohort), synthetic hematocrit was calculated using BP_mean. Myocardial ECV was computed using the synthetic hematocrit and compared with the ECV using the blood hematocrit as a reference.</p><p><strong>Results: </strong>In the derivation cohort (49 men, mean age 79 ± 8 years), a correlation between BP_mean and blood hematocrit ranged from poor for VNI of CCTA at 80 keV, QIR2 (R² = 0.12) to moderate for VNI of LE at 60 keV, QIR4; 70 keV, QIR3 and 4; and VNC of LE, QIR3 and 4 (all, R² = 0.58). In the validation cohort (29 men, age 75 ± 14 years), synthetic hematocrit was calculated from VNC of the LE scan, QIR3. Median ECV was 26.9% (interquartile range (IQR), 25.5%, 28.8%) using the blood hematocrit and 26.8% (IQR, 25.4%, 29.7%) using synthetic hematocrit (VNC, QIR3; mean difference, -0.2%; limits of agreement, -2.4%, 2.0%; p = 0.33).</p><p><strong>Conclusion: </strong>Synthetic hematocrit calculated from VNC images enables an accurate computation of myocardial ECV with PCD-CT.</p><p><strong>Clinical relevance statement: </strong>Virtual non-contrast images from cardiac late enhancement scans with photon-counting detector CT allow the calculation of a synthetic hematocrit, which enables accurate computation of myocardial extracellular volume.</p><p><strong>Key points: </strong>Blood hematocrit is mandatory for conventional myocardial extracellular volume computation. Synthetic hematocrit can be calculated from virtual non-iodine and non-contrast photon-counting detector CT images. Synthetic hematocrit from virtual non-contrast images enables computation of the myocardial extracellular volume.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7845-7855"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141456106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic MRI for deep pelvic endometriosis: towards a standardized protocol?","authors":"Isabelle Thomassin-Naggara, Christine Sadjo Zoua, Marc Bazot, Michele Monroc, Horace Roman, Léo Razakamanantsoa, Pascal Rousset","doi":"10.1007/s00330-024-10842-0","DOIUrl":"10.1007/s00330-024-10842-0","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the diagnostic efficacy of an MRI protocol and patient preparation in detecting deep pelvic endometriosis (DPE).</p><p><strong>Material and methods: </strong>The cohort is from the ENDOVALIRM database, a multicentric national retrospective study involving women who underwent MRI followed by pelvic surgery for endometriosis (reference standard). Two senior radiologists independently analyzed MRI findings using the deep pelvic endometriosis index (dPEI) to determine lesion locations. The study evaluated the impact of bowel preparation, vaginal and rectal opacification, MRI unit type (1.5-T or 3-T), additional sequences (thin slice T2W or 3DT2W), and gadolinium injection on reader performance for diagnosing DPE locations. Fisher's exact test assessed differences in diagnostic accuracy based on patient preparation and MRI parameters.</p><p><strong>Results: </strong>The final cohort comprised 571 women with a mean age of 33.3 years (± 6.6 SD). MRI with bowel preparation outperformed MRI without bowel preparation in identifying torus/uterosacral ligament (USL) locations (p < 0.0001) and rectosigmoid nodules (p = 0.01). MRI without vaginal opacification diagnosed 94.1% (301/320) of torus/USL locations, surpassing MR with vaginal opacification, which diagnosed 85% (221/260) (p < 0.001). No significant differences related to bowel preparation or vaginal opacification were observed for other DPE locations. Rectal opacification did not affect diagnostic accuracy in the overall population, except in patients without bowel preparation, where performance improved (p = 0.04). There were no differences in diagnostic accuracy regarding MRI unit type (1.5-T/3-T), presence of additional sequences, or gadolinium injection for any endometriotic locations.</p><p><strong>Conclusion: </strong>Bowel preparation prior to MRI examination is preferable to rectal or vaginal opacification for diagnosing deep endometriosis pelvic lesions.</p><p><strong>Clinical relevance statement: </strong>Accurate diagnosis and staging of DPE are essential for effective treatment planning. Bowel preparation should be prioritized over rectal or vaginal opacification in MRI protocols. Optimizing MRI protocols for diagnostic performance with appropriate opacification techniques will help diagnose deep endometriosis more accurately.</p><p><strong>Key points: </strong>Evaluating deep endometriosis in collapsible organs such as the vagina and rectum is difficult. Bowel preparation and an absence of vaginal opacification were found to be diagnostically beneficial. Bowel preparation should be prioritized over rectal or vaginal opacification in MRI protocols.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7705-7715"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491476","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":"Reappraising imaging features of pancreatic acinar cystic transformation: be aware of differential diagnoses.","authors":"Lina Aguilera Munoz, Carina Boros, Fanny Bonvalet, Louis de Mestier, Frédérique Maire, Philippe Lévy, Jérôme Cros, Maxime Ronot, Vinciane Rebours","doi":"10.1007/s00330-024-10823-3","DOIUrl":"10.1007/s00330-024-10823-3","url":null,"abstract":"<p><strong>Objectives: </strong>Imaging features of pancreatic acinar cystic transformation (ACT) have been published. We aimed to describe the clinical and radiological characteristics of patients with a presumed pancreatic ACT diagnosis, reappraising the value of these published imaging criteria.</p><p><strong>Materials and methods: </strong>Single-center retrospective study (2003-2021) of consecutive patients with a presumed diagnosis of ACT as suggested by the local expert multidisciplinary case review board. Patients without available imaging (CT or MRI) for review were excluded. Patients were classified into \"certain\" ACT (if ≥ 2 imaging criteria and no differential diagnosis) or \"uncertain\" ACT (if ≥ 1 imaging criteria and suggested differential diagnoses).</p><p><strong>Results: </strong>Sixty-four patients (35 males, [55%]) were included. ACT was considered \"certain\" for 34 patients (53%) and \"uncertain\" for 30 patients (47%). The number of ACT criteria did not differ between groups, with 91.2% of patients with ≥ 3 ACT imaging criteria in the \"certain\" group vs 93.3% in the \"uncertain\" group (p = 0.88). In the \"uncertain\" group, the main suggested differentials were branch-duct intraductal papillary mucinous neoplasm (18/30 patients, 60%), calcifying chronic pancreatitis (8/30 patients, 27%), both (three patients, 10%) and serous cystadenoma (one patient, 3%). Calcifications were significantly more frequent in the \"uncertain\" group (89% vs 63% in the \"certain\" group, p = 0.02).</p><p><strong>Conclusion: </strong>Published ACT imaging criteria are frequently associated with features suggesting differential diagnoses. They appear insufficient to reach a final diagnosis in a subset of patients.</p><p><strong>Clinical relevance statement: </strong>ACT displays a heterogeneous morphological imaging presentation challenging the non-invasive diagnostic work-up. Physicians' and radiologists' awareness of this entity is important to better understand its natural history and improve non-invasive diagnostic criteria.</p><p><strong>Key points: </strong>The criteria to help diagnose ACT are frequently associated with features suggestive of differentials. The main alternatives suggested when ACT diagnosis was \"uncertain\" were branch-duct intraductal papillary mucinous neoplasm and calcifying chronic pancreatitis. Published ACT diagnostic imaging criteria can be insufficient for a definite non-invasive diagnosis.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7650-7658"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305782","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":"Letter to the Editor: \"Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques\".","authors":"Xiaoxiao Zhang, Zhengyu Jin, Hao Sun","doi":"10.1007/s00330-024-10832-2","DOIUrl":"10.1007/s00330-024-10832-2","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7589-7590"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141295846","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":"From data to decisions: deep learning is shaping prostate cancer diagnostics.","authors":"Bas Israël","doi":"10.1007/s00330-024-10997-w","DOIUrl":"10.1007/s00330-024-10997-w","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7907-7908"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878585","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":"Photon-counting CT for pulmonary embolisms-when radiologists don't have to choose between image quality or motion artifacts.","authors":"Michael Brun Andersen","doi":"10.1007/s00330-024-10992-1","DOIUrl":"10.1007/s00330-024-10992-1","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7829-7830"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901403","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":"Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.","authors":"Sheng Li, Xiaoting Wei, Li Wang, Guizhi Zhang, Linling Jiang, Xuhui Zhou, Qinghua Huang","doi":"10.1007/s00330-024-10854-w","DOIUrl":"10.1007/s00330-024-10854-w","url":null,"abstract":"<p><strong>Objectives: </strong>This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients.</p><p><strong>Methods: </strong>In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports.</p><p><strong>Results: </strong>Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89.</p><p><strong>Conclusions: </strong>The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients.</p><p><strong>Clinical relevance statement: </strong>Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters.</p><p><strong>Key points: </strong>Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7567-7579"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141431712","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":"Iodine-based contrast media in contrast-enhanced mammography and dedicated breast computed tomography: is it necessary to assess renal function in all outpatients to prevent contrast-induced acute kidney injury?","authors":"Marco Parillo, Carlo A Mallio, Aart J van der Molen, Carlo C Quattrocchi, Ilona A Dekkers, Thiemo J A van Nijnatten, Eleonora M C Voormolen","doi":"10.1007/s00330-024-10871-9","DOIUrl":"10.1007/s00330-024-10871-9","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7580-7582"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141436753","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":"Pediatric diffuse intrinsic pontine glioma radiotherapy response prediction: MRI morphology and T2 intensity-based quantitative analyses.","authors":"Xiaojun Yu, Shaoqun Li, Wenfeng Mai, Xiaoyu Hua, Mengnan Sun, Mingyao Lai, Dong Zhang, Zeyu Xiao, Lichao Wang, Changzheng Shi, Liangping Luo, Linbo Cai","doi":"10.1007/s00330-024-10855-9","DOIUrl":"10.1007/s00330-024-10855-9","url":null,"abstract":"<p><strong>Objectives: </strong>An easy-to-implement MRI model for predicting partial response (PR) postradiotherapy for diffuse intrinsic pontine glioma (DIPG) is lacking. Utilizing quantitative T2 signal intensity and introducing a visual evaluation method based on T2 signal intensity heterogeneity, and compared MRI radiomic models for predicting radiotherapy response in pediatric patients with DIPG.</p><p><strong>Methods: </strong>We retrospectively included patients with brainstem gliomas aged ≤ 18 years admitted between July 2011 and March 2023. Applying Response Assessment in Pediatric Neuro-Oncology criteria, we categorized patients into PR and non-PR groups. For qualitative analysis, tumor heterogeneity vision was classified into four grades based on T2-weighted images. Quantitative analysis included the relative T2 signal intensity ratio (rT2SR), extra pons volume ratio, and tumor ring-enhancement volume. Radiomic features were extracted from T2-weighted and T1-enhanced images of volumes of interest. Univariate analysis was used to identify independent variables related to PR. Multivariate logistic regression was performed using significant variables (p < 0.05) from univariate analysis.</p><p><strong>Results: </strong>Of 140 patients (training n = 109, and test n = 31), 64 (45.7%) achieved PR. The AUC of the predictive model with extrapontine volume ratio, rT2SRmax-min (rT2SR_dif), and grade was 0.89. The AUCs of the T2-weighted and T1WI-enhanced models with radiomic signatures were 0.84 and 0.81, respectively. For the 31 DIPG test sets, the AUCs were 0.91, 0.83, and 0.81, for the models incorporating the quantitative features, radiomic model (T2-weighted images, and T1W1-enhanced images), respectively.</p><p><strong>Conclusion: </strong>Combining T2-weighted quantification with qualitative and extrapontine volume ratios reliably predicted pediatric DIPG radiotherapy response.</p><p><strong>Clinical relevance statement: </strong>Combining T2-weighted quantification with qualitative and extrapontine volume ratios can accurately predict diffuse intrinsic pontine glioma (DIPG) radiotherapy response, which may facilitate personalized treatment and prognostic assessment for patients with DIPG.</p><p><strong>Key points: </strong>Early identification is crucial for radiotherapy response and risk stratification in diffuse intrinsic pontine glioma. The model using tumor heterogeneity and quantitative T2 signal metrics achieved an AUC of 0.91. Using a combination of parameters can effectively predict radiotherapy response in this population.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7962-7972"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141436754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and deep learning for classifying the justification of brain CT referrals.","authors":"Jaka Potočnik, Edel Thomas, Aonghus Lawlor, Dearbhla Kearney, Eric J Heffernan, Ronan P Killeen, Shane J Foley","doi":"10.1007/s00330-024-10851-z","DOIUrl":"10.1007/s00330-024-10851-z","url":null,"abstract":"<p><strong>Objectives: </strong>To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.</p><p><strong>Methods: </strong>Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deep learning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set.</p><p><strong>Results: </strong>42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons.</p><p><strong>Conclusion: </strong>Interpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals.</p><p><strong>Clinical relevance statement: </strong>Healthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources.</p><p><strong>Key points: </strong>Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations. Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation. Machine and deep learning can improve retrospective and prospective justification auditing for better implementation of imaging referral guidelines.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"7944-7952"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11557633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141442384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}