Radiology-Artificial Intelligence最新文献

{"title":"Automated Delineation of Couinaud Segments at CT for Future Liver Remnant Volumetry.","authors":"Tejas Sudharshan Mathai, Praveen T S Balamuralikrishna, Vivek Batheja, Xinya Wang, Cathleen Hannah, Michael Kassin, Ifechi Ukeh, Christopher Koh, David E Kleiner, Jonathan M Hernandez, Meghan G Lubner, Perry J Pickhardt, Ronald M Summers","doi":"10.1148/ryai.250808","DOIUrl":"10.1148/ryai.250808","url":null,"abstract":"<p><p>Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen at CT for future liver remnant (FLR) volumetry. Materials and Methods In this retrospective study (January 2001-October 2025), eight liver Couinaud segments and the spleen were manually labeled on CT scans of patients from institution A and the public Medical Segmentation Decathlon dataset. A three-dimensional nnU-Net segmentation model was trained on this dataset and evaluated on three datasets (one internal and two external). Results The training dataset included 498 patients (442 from the public Medical Segmentation Decathlon dataset and 56 from institution A, mean age ± SD, 55 years ± 7; 38 men), and the testing dataset included 64 patients from institution A (50 had liver fibrosis and eight underwent portal vein embolization; PVE), 197 patients from the publicly available colorectal liver metastases (CRLM) dataset (mean age, 59 years ± 12; 117 men), and 50 patients (25 were healthy and 25 had cirrhosis) from an external site (institution B; mean age, 49 years ± 9; 29 men). For the whole liver in institution A and institution B, Dice scores of 0.98 ± 0.02 (95% CI: 0.97, 0.99) and 0.98 ± 0.03 (95% CI: 0.97, 0.99), and 95% percentile Hausdorff distance (HD) errors of 2.5 mm ± 3.8 (95% CI: 1.6, 3.3) and 3.3 mm ± 6.6 (95% CI: 1.4, 5.2) were obtained, respectively. The pre-PVE FLR_% and post-PVE FLR_% volume differences (manual vs automated, eight patients) were 0.03 ± 2.4 and -0.39 ± 3.0, respectively. For the FLR in the CRLM dataset, a Dice score of 0.99 ± 0.01 (95% CI: 0.99, 0.993) and an HD error of 0.9 mm ± 1.8 (95% CI: 0.6, 1.1) were achieved. Conclusion The model accurately estimated preoperative FLR volumetry and generalized well to patients with colorectal liver metastases, fibrosis, and cirrhosis and healthy controls. <b>Keywords:</b> CT, Deep Learning, Couinaud, Spleen, Future Liver Remnant, Portal Vein Embolization, Cirrhosis, Fibrosis <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250808"},"PeriodicalIF":20.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.","authors":"Astha Jaiswal, Miriam Rinneburger, Franziska Meyer, Sita Arjune, Laura Oberlinkels, Philomena Alice Wawer Matos Reimer, Lars Lotter-Becker, Ünal Akünal, Markus Bujotzek, Stefan Denner, Klaus Maier-Hein, Leonard Stepansky, Matthias Stefan May, Markus Habert, Alexander Köhn, Mirjam Schöneck, Robert Peter Wawer Matos Reimer, Roman-Ulrich Müller, Simon Lennartz, Nils Große Hokamp, Andreas Michael Bucher, Thorsten Persigehl, Liliana Lourenco Caldeira","doi":"10.1148/ryai.250200","DOIUrl":"10.1148/ryai.250200","url":null,"abstract":"<p><p>Purpose To develop and systematically evaluate an iterative training approach, termed the <i>expert-guided annotation loop</i>, for efficient reference standard segmentation generation, including assessment of two sample selection strategies and real-world clinical implementation. Materials and Methods This retrospective study included 10 datasets comprising 1941 CT and MRI scans from patients with autosomal dominant polycystic kidney disease, prostate cancer, uveal melanoma, thyroid eye disease, or non-small cell lung cancer. nnU-Net segmentation models were iteratively trained using an expert-guided annotation loop with random or active learning-based sample selection. In each iteration, additional samples were added to the training set, and model-generated presegmentations were corrected by expert radiologists to create reference standard annotations. Expert time required for manual segmentation versus presegmentations correction was measured. Model performance and efficiency were assessed using nonparametric tests, and cost savings were estimated for kidney and tumor segmentation using probabilistic sensitivity analysis. Feasibility of end-to-end no-code implementation was evaluated. Results Fifty-seven segmentation models were trained and evaluated. Final model mean Dice scores ranged from 0.67 to 0.97 for organ segmentation and from 0.64 to 0.69 for lung tumor segmentation across internal and external test sets. Maximum expert time savings were 90.3% for kidney and 48.2% for tumor segmentation (<i>P</i> < .001 and <i>P</i> = .003, respectively), corresponding to estimated per-examination cost savings of $14.30 (95% CI: 5.94, 26.87) and $5.63 (95% CI: -7.26, 26.09), respectively. No-code execution of the expert-guided annotation loop was feasible. Conclusion The expert-guided annotation loop reduced expert annotation time and enabled estimated cost savings while producing high-quality reference standard segmentations. The no-code workflow was implemented in a clinical environment. <b>Keywords:</b> Artificial Intelligence, CT, Human-in-the-Loop Machine Learning, Medical Image Segmentation, MRI <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250200"},"PeriodicalIF":20.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Ischemic Stroke Lesion Segmentation Challenge (ISLES)'24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes.","authors":"Evamaria Olga Riedel, Ezequiel de la Rosa, The Anh Baran, Moritz Hernandez Petzsche, Hakim Baazaoui, Kaiyuan Yang, Fabio Antonio Musio, Houjing Huang, David Robben, Joaquin Oscar Seia, Roland Wiest, Mauricio Reyes, Ruisheng Su, Claus Zimmer, Tobias Boeckh-Behrens, Maria Berndt, Bjoern Menze, Daniel Rueckert, Benedikt Wiestler, Susanne Wegener, Jan Stefan Kirschke","doi":"10.1148/ryai.250603","DOIUrl":"10.1148/ryai.250603","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250603"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiopathomic Graph Deep Learning for Multiscale Spatial-Contextual Modeling of Intratumoral Heterogeneity to Predict Breast Cancer Response to Neoadjuvant Therapy.","authors":"Liang-Qin Zhou, Xin-Yi Wang, Ye Xu, Hong-Xia Zhang, Xin-Xin Yang, Rui-Qi Jin, Xi-Qiao Sang, Yue-Min Zhu, Hong-Xue Meng, Zi-Xiang Kuai","doi":"10.1148/ryai.250760","DOIUrl":"10.1148/ryai.250760","url":null,"abstract":"<p><p>Purpose To develop an explainable radiopathomic graph deep learning (RPGDL) system for multiscale spatial-contextual modeling of intratumoral heterogeneity and evaluate its performance for the prediction of pathologic complete response (pCR) to neoadjuvant therapy in breast cancer. Materials and Methods The RPGDL system was developed from dual-center retrospective analysis of patients with biopsy-proven invasive breast cancer (May 2018-August 2024). For each tumor, individual radiomic and pathomic graphs were generated from pretherapeutic MRI and hematoxylin-eosin-stained biopsy slide images, respectively. These graphs were then processed by three distinct graph neural networks (GNNs): radiomic, pathomic, and radiopathomic. GNN performance was assessed with the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). A multifaceted approach was used to explain the GNNs' predictions. Results The training set included 582 (mean age, 52 years ± 9 [SD]) patients and the external test set 468 (50 years ± 10) patients from centers 1 and 2, respectively. The radiomic GNN achieved AUCs of 0.89 (95% CI: 0.85, 0.93) in the training set and 0.84 (95% CI: 0.80, 0.89) in the external test set; the pathomic GNN achieved AUCs of 0.87 (95% CI: 0.83, 0.91) in the training set and 0.83 (95% CI: 0.78, 0.88) in the external test set, with no significant difference between them (<i>P</i> > .05). The radiopathomic GNN outperformed both single-modality GNNs (training set: AUC, 0.95 [95% CI: 0.92, 0.98]; external test set: AUC, 0.91 [95% CI: 0.87, 0.94]; <i>P</i> < .05; NRI and IDI confirmed). Pathomic graphs dominated probability increases for pCR predictions, while radiomic graphs drove probability decreases for non-pCR predictions. Multifaceted analyses verified GNNs' explainability. Conclusion The developed RPGDL system enabled multiscale spatial-contextual intratumoral heterogeneity modeling for high-performance, explainable prediction of pCR to neoadjuvant therapy in breast cancer. <b>Keywords:</b> Dynamic Contrast-enhanced MRI, Breast, Tumor Response, Radiology-Pathology Integration, Prognosis, Principal Component Analysis, Perception, Supervised Learning, Reconstruction Algorithm <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250760"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-Tuned Large Language Model for Automated Radiology Impression Generation: A Multicenter Evaluation.","authors":"Mingyang Li, Yaning Wang, Zheng Miao, Jiaqi Gong, Simin Yang, Han Xue, Qi Yang, Lijun Duan, Lin Mu, Ying Mu, Kai Zhu, Qi Dai, Munire Aihemaiti, Yunhui Yang, Liang Liu, Yingyan Zheng, Yang Hou, Lei Zhang, Jing Wang, Huimao Zhang","doi":"10.1148/ryai.250714","DOIUrl":"10.1148/ryai.250714","url":null,"abstract":"<p><p>Purpose To develop a fine-tuned large language model (Medical Imaging Report Assistant [MIRA]) and evaluate its performance in generating radiology impressions from multicenter data with respect to accuracy, reporting efficiency, and clinical applicability. Materials and Methods A retrospective multicenter dataset comprising 1.87 million radiology reports (including CT, MRI, and digital radiography data) from 42 hospitals across 22 provinces in China (January 2019-August 2024) was compiled. The dataset was used to fine-tune a large language model via a prompt-based strategy. The evaluation framework incorporated both automated and human evaluation metrics. Radiologists evaluated internal and external datasets and three open-source datasets to compare impressions generated by the fine-tuned large language model and GPT-4o (Open AI). Twenty-four radiologists from six centers performed blinded comparisons of MIRA-generated and reference impressions to assess interrater consistency and drafting efficiency. Data were analyzed using appropriate parametric and nonparametric tests and χ² tests, with Holm-Bonferroni correction for multiple comparisons. Results The internal test set included data for 78 544 reports (median age, 52 years; IQR, 35-65 years; 39 351 male), and the external test set included data for 27 471 reports (median age, 53 years; IQR, 37-66 years; 13 955 male). Site- and modality-aware prompting improved similarity (internal BERTScore F1 and sentence similarity, 0.92 and 0.92, respectively; external BERTScore F1 and sentence similarity, 0.82 and 0.80, respectively, under optimal settings; <i>P</i> < .001). Human evaluation (<i>n</i> = 2327) showed MIRA beat GPT-4o on both similarity and F1 score (<i>P</i> < .001). MIRA-generated impressions were rated as at least as good as the reference impressions in 69.0% (1657 of 2400) of blinded comparisons, reduced draft time by 0.46 minutes per report, and increased interradiologist agreement (<i>P</i> < .001). Conclusion MIRA, a fine-tuned large language model using a prompt-based strategy, generated clinically aligned radiology impressions in multicenter settings, improving accuracy, efficiency, and reporting consistency. <b>Keywords:</b> Computer-aided Diagnosis, CAD, Supervised Learning, Transfer Learning, Conventional Radiography, MRI, Computer Applications-General Informatics, Statistics <i>Supplemental material is available for this article.</i> © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250714"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM Label Noise and the Established Framework of Imperfect Reference Standard Bias.","authors":"Jeong Hyun Lee, Jaeseung Shin","doi":"10.1148/ryai.260153","DOIUrl":"https://doi.org/10.1148/ryai.260153","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260153"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based Fusion of Longitudinal Multimodal Radiomic Features from Chest Radiography and CT in COVID-19.","authors":"Chunrui Zou, Walter C Mankowski, Lauren Pantalone, Hannah Horng, Shefali Setia Verma, Eduardo J Mortani Barbosa, Tessa S Cook, Peter B Noel, Erica L Carpenter, Jeffrey C Thompson, Russell T Shinohara, Leonid Roshkovan, Sharyn I Katz, Despina Kontos","doi":"10.1148/ryai.240218","DOIUrl":"10.1148/ryai.240218","url":null,"abstract":"<p><p>Purpose To evaluate the feasibility of a transformer structure for fusing longitudinal multimodal radiomic features from chest radiographs (CXRs) and CT images to predict outcomes and identify associated clinical events in patients with COVID-19. Materials and Methods This retrospective study analyzed de-identified longitudinal CXRs and CT images in patients with polymerase chain reaction-confirmed COVID-19. Proprietary patient data (site 1) were collected between July 2020 and May 2021, and open-access patient data (obtained before February 1, 2020) were collected from site 2. Clinical outcomes included mortality, intensive care unit admission, and ventilator use during any follow-up visit. Radiomic features were extracted from lung regions in CXRs and CT images using the Cancer Imaging Phenomics Toolkit and integrated using a transformer-based model. Patient data were partitioned into training, validation, and test sets (ratio, 65:15:20). Subgroup analyses were performed across sex, site, and modality. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and weighted AUC scores, with statistical significance assessed using Student <i>t</i> tests. Results The study included 2274 patients (946 from site 1, 1328 from site 2; mean age, 59.84 years ± 16.84, 1171 male patients). Weighted testing AUCs for predicting outcomes were 0.86 (95% CI: 0.85, 0.86) for mortality, 0.82 (95% CI: 0.81, 0.82) for intensive care unit admission, and 0.86 (95% CI: 0.86, 0.87) for ventilator usage, outperforming models trained solely on cross-sectional data or single-modal data (<i>P</i> < .05). Conclusion Transformer-based fusion of longitudinal multimodal radiomic data effectively predicted clinical outcomes and events associated with COVID-19. <b>Keywords:</b> COVID-19, Lung, Radiomics, Multi-Head Attention, Multimodal, Longitudinal, CT, Chest Radiography <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240218"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147475349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Distillation for Breast US AI: Improving Performance and Practicality.","authors":"Gary J Whitman, Ethan O Cohen","doi":"10.1148/ryai.260406","DOIUrl":"10.1148/ryai.260406","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260406"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148035927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Survival Prediction in Glioblastoma: Time-dependent Model Interpretability Using MRI, Clinical, and Molecular Data.","authors":"Junhyeok Lee, Young Hun Jeon, Joon Jang, Heeseong Eum, Minchul Kim, Sung Hye Park, Chul-Kee Park, Seung Hong Choi, Sung Soo Ahn, Kyu Sung Choi","doi":"10.1148/ryai.250675","DOIUrl":"10.1148/ryai.250675","url":null,"abstract":"<p><p>Purpose To develop a multimodal model for survival prediction and time-dependent model interpretability in glioblastoma by integrating preoperative MRI with clinical and molecular variables. Materials and Methods This retrospective multicenter study included two institutional cohorts (February 2007-December 2024) and two public external test sets. A deep learning-based prognostic index (DPI) was generated from preoperative multiparametric MRI using a vision transformer. The DPI was integrated with clinical variables (age, sex, Karnofsky performance status, extent of resection), molecular markers (isocitrate dehydrogenase [<i>IDH</i>] mutation, O6-methylguanine-DNA methyltransferase [<i>MGMT</i>] promoter methylation), histopathology, and World Health Organization (WHO) grade using a random survival forest model. Model performance was evaluated using the C index, and time-dependent model interpretability was assessed using survival Shapley Additive Explanations (SurvSHAP[t]). Associations between DPI and clinical and molecular variables were evaluated using correlation and group-wise statistical tests. Results A total of 1883 patients (mean age, 57.7 years ± 14.8 [SD]; 983 female) were included. The multimodal model integrating MRI and clinical and molecular variables achieved C indexes of 0.77, 0.73, and 0.63 for the internal test set and two external test sets, respectively. In comparison, the image-only model achieved C indexes of 0.73, 0.65, and 0.60 across the same cohorts. SurvSHAP(t) analysis showed that prognostic influence peaked at approximately 12 months for extent of resection and 24 months for <i>MGMT</i> promoter methylation, whereas <i>IDH</i> mutation and WHO grade increased in importance over time. The imaging-derived DPI consistently ranked among the strongest predictors of survival and showed moderate correlations with age, Karnofsky performance status, <i>IDH</i> mutation status, and WHO grade. Conclusion The multimodal model showed good performance for glioblastoma survival prediction and enabled time-dependent model interpretability, identifying the imaging-derived prognostic index as a complementary biomarker with sustained prognostic importance over time. <b>Keywords:</b> MRI, Neuro-Oncology, Central Nervous System, Brain, Brain Stem, Primary Neoplasms, Comparative Studies, Prognosis, Random Survival Forest, Feature Detection, Radiology-Pathology Integration <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250675"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetal Brain in a Box: AIRFRAME Model to Classify Brain Abnormalities at US.","authors":"Patricia Piazza Rafful","doi":"10.1148/ryai.260322","DOIUrl":"10.1148/ryai.260322","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260322"},"PeriodicalIF":20.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147933995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}