Radiology-Artificial Intelligence最新文献

{"title":"ROADMAP: An Ontology of Medical AI Models and Datasets.","authors":"Abhinav Suri, Marcelo Straus Takahashi, Tara Retson, Ricardo A Gonzales, Seong Ho Park, Charles E Kahn","doi":"10.1148/ryai.260069","DOIUrl":"10.1148/ryai.260069","url":null,"abstract":"<p><p>Successful development, regulatory review, and clinical implementation of artificial intelligence (AI) systems in medicine require clear, unambiguous communication about AI models and datasets. The Radiology Ontology of AI Datasets, Models and Projects (ROADMAP) was developed to provide a machine-interpretable framework to describe medical AI resources by formally defining the attributes of AI models and datasets and their allowable values. ROADMAP builds upon generalized \"model cards\" and \"datasheets for datasets\" by incorporating features that support multimodal data, including medical images, structured data, and unstructured text. ROADMAP references concepts from widely used ontologies, coding schemes, and common data elements to improve the discoverability, interoperability, and reuse of AI resources. The ontology can facilitate matching of appropriate AI models with relevant datasets and support the detection of potential sources of bias in AI resources; it is available at <i><i>https://bioportal.bioontology.org/ontologies/ROADMAP</i></i>. <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also special report on ROADMAP and metrics.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e260069"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147436159","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":"Accelerated Aging and Aging Velocity from Deep Learning-based Chest Radiograph-derived Age for Predicting Cause-specific Mortality.","authors":"Yoosoo Chang, Hyungjin Kim, Seungho Lee, Hong Seok Lee, Soon Ho Yoon, Seungho Ryu","doi":"10.1148/ryai.250609","DOIUrl":"10.1148/ryai.250609","url":null,"abstract":"<p><p>Purpose To assess the prognostic value of deep learning-derived radiographic age and aging velocity for predicting mortality in an Asian cohort. Materials and Methods This retrospective cohort study included Korean adults who underwent posteroanterior chest radiography between January 2006 and December 2020. Radiographic age was estimated using AgeNet, a deep learning model trained on the data of healthy asymptomatic individuals. Accelerated aging was defined as radiographic age exceeding chronological age by ≥5 years and aging velocity as the annual change in radiographic age from serial radiographs. Multivariable Cox and Fine-Gray models were used to estimate adjusted hazard ratios (HRs) for all-cause and cause-specific mortality. Mortality rate ratios were estimated using Poisson regression. Results A total of 421 894 adults were included (mean age, 47.4 years ± 8.8 [SD]; 227 427 [53.9%] male). During a median follow-up of 8.5 years, 6506 deaths occurred (953 cardiovascular, 3024 cancer, 1043 respiratory). Accelerated aging was associated with increased all-cause and cause-specific mortality, with stronger associations in female participants (<i>P</i> = .008 for interaction); the HRs for all-cause mortality were 1.26 (<i>P</i> < .001) for male participants and 1.52 (<i>P</i> < .001) for female participants. Among 179 667 individuals with three or more scans, aging velocity predicted mortality regardless of baseline status (adjusted cumulative mortality ratio per 1-SD increase: male participants, 1.24; female participants, 1.35; all <i>P</i> < .001). Compared with stable aging (1 year/year ± 0.5), decelerated velocity (<0.5 year/year) was associated with lower mortality risk (mortality rate ratios: male participants, 0.90, <i>P</i> = .18; female participants, 0.50, <i>P</i> < .001), whereas accelerated velocity (≥1.5 years/year) increased mortality risk (mortality rate ratios: male participants, 1.51; female participants, 1.71; both <i>P</i> < .001). Conclusion Radiographic age-based accelerated aging and aging velocity independently predicted all-cause and cause-specific mortality. <b>Keywords:</b> Conventional Radiography, Thorax, Epidemiology, Convolutional Neural Network <i>Supplemental material is available for this article.</i> © RSNA, 2026 See also the commentary by Babyn in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250609"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595266","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":"When Framing Shapes the Answer: Cognitive Bias and Large Language Model Reliability in Radiology.","authors":"Soroosh Tayebi Arasteh, Daniel Truhn","doi":"10.1148/ryai.260346","DOIUrl":"https://doi.org/10.1148/ryai.260346","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260346"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843383","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":"Imaging the Breast Cancer Microenvironment: Toward Interpretable MRI Biomarkers for Treatment Response.","authors":"Tician Schnitzler","doi":"10.1148/ryai.260266","DOIUrl":"https://doi.org/10.1148/ryai.260266","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"8 3","pages":"e260266"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783347","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":"AI Triage of Normal Chest Radiographs: A Silent Trial and Failure Analysis.","authors":"Mathew Storey, Anthony Chung, Jack Packer, Ann-Marie Bartsch, Anita Rhodes, Rafal Colta, Simon Rickaby, Christina Malamateniou, Geraldine Dean, Susan Shelmerdine","doi":"10.1148/ryai.250964","DOIUrl":"10.1148/ryai.250964","url":null,"abstract":"<p><p>Chest radiography is the most frequently performed imaging examination worldwide, and increasing demand has contributed to reporting delays in many health systems. This prospective multicenter silent trial evaluated the diagnostic performance of a commercially available artificial intelligence (AI) model for triaging normal chest radiographs across five National Health Service hospital sites in the United Kingdom over a 12-month period. A total of 63 083 adult chest radiographs were analyzed. The AI model classified 50 661 examinations (80%) as abnormal and 12 422 (20%) as normal. The model achieved 97% sensitivity, 35% specificity, 57% positive predictive value, and 94% negative predictive value for detecting abnormal chest radiographs. Expert review of discrepant cases, after exclusion of 412 natural language processing labeling errors, identified 31 clinically significant AI misses, corresponding to an estimated clinically significant miss rate of 0.05%. Most missed findings involved subtle or overlapping lesions. Concordance between the AI model and radiologist reports for normal examinations occurred in 18.5% of chest radiographs, indicating that nearly one-fifth of examinations could potentially be deprioritized for reporting. These findings suggest that AI-assisted triage of chest radiographs may help prioritize reporting workflows while maintaining a low rate of clinically significant missed findings, although further research is warranted to evaluate clinical implementation. <b>Keywords:</b> Artificial Intelligence, Chest Radiography, Computer-aided Diagnosis, Thorax <i>Supplemental material is available for this article.</i> © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250964"},"PeriodicalIF":13.2,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783324","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":"https://doi.org/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 [KPS], extent of resection [EOR]), molecular markers (<i>IDH</i> mutation, <i>MGMT</i> promoter methylation), histopathology, and WHO grade using a random survival forest model. Model performance was evaluated using the concordance index (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 1,883 patients (mean age, 57.7 ± 14.8 [SD] years; 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 EOR 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, KPS, <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. ©RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250675"},"PeriodicalIF":13.2,"publicationDate":"2026-04-29","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":"Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.","authors":"Anahita Fathi Kazerooni, Zhifan Jiang, Deep Gandhi, Nastaran Khalili, Xinyang Liu, Wenxin Tu, Jeffrey B Ware, Ariana M Familiar, Bhavyasri Vunnava, Anna Zapaishchykova, Aaron S McAllister, Mariana Sanchez-Montano, Nakul Sheth, Khanak K Nandolia, Hollie Anne Lai, Julija Pavaine, Sanjay P Prabhu, Debanjan Haldar, Sanaz Varshochi, Sina Bagheri, Hannah Anderson, Shuvanjan Haldar, Neda Khalili, Anurag Gottipati, Ibraheem Salman Shaikh, Ethan Castellino, Avani Mangoli, Harsh Gohil, Nazanin Maleki, Justin Low, Trent Hummel, Roger J Packer, Andrea Franson, Phillip B Storm, Spyridon Bakas, Evan Calabrese, Mariam Aboian, Peter de Blank, Benjamin H Kann, Brian Rood, Adam C Resnick, Ali Nabavizadeh, Arastoo Vossough, Marius George Linguraru","doi":"10.1148/ryai.250902","DOIUrl":"https://doi.org/10.1148/ryai.250902","url":null,"abstract":"<p><p>Pediatric brain tumors are rare and still represent the most common solid tumors in children and the leading cause of cancer-related mortality in the pediatric population. Compared to adult brain tumors, they exhibit distinct biology, anatomy, and clinical behavior, posing unique diagnostic and therapeutic challenges. Artificial intelligence (AI) methods have the potential to improve diagnosis, disease monitoring, and treatment response assessment, but progress in pediatric neuro-oncology has been hampered by the lack of large, standardized, and publicly accessible datasets. We introduce the Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) dataset, the first large-scale open-access benchmark data dedicated to pediatric brain tumor segmentation and analysis. The dataset comprises multiparametric MRI scans from 457 pediatric patients with high-grade gliomas, collected across multiple institutions and international consortia. Each case includes pre- and post-contrast T1-weighted, T2-weighted, and T2-FLAIR MRI sequences. Tumor subregions were annotated following the Response Assessment in Pediatric Neuro-Oncology (RAPNO) recommendations through a semi-automated process combining pediatric-specific auto-segmentation and expert manual refinement by neuroradiologists. The dataset is partitioned into training (n = 257), validation (n = 91), and hidden testing (n = 109) subsets to support reproducible benchmarking. BraTS-PEDs is the first large-scale, standardized resource for developing and evaluating AI algorithms in pediatric neuro-oncology. It provides a foundation for reproducible method comparison, model generalization across institutions, and future integration of imaging with molecular and clinical data for precision medicine applications.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250902"},"PeriodicalIF":13.2,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783292","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 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":"https://doi.org/10.1148/ryai.250603","url":null,"abstract":"<p><p>Stroke remains a major global health burden (1,2), although outcomes have improved substantially through imaging-guided therapy and endovascular reperfusion (3,4). While CT and MRI are standard for estimating infarct core and penumbra (5), variability in threshold-based deconvolution of perfusion imaging (6) can lead to inconsistent lesion size estimates (7). Accurate modeling of infarct growth is therefore essential for optimizing transfer and treatment decisions (8). Advances in artificial intelligence (AI) have improved automated lesion detection, yet clinical translation requires large, well-annotated datasets. While recent large-scale cohorts including the Ischemic Stroke Lesion Segmentation Challenge (ISLES)'22 (<i>n</i> = 400) (9), Liew et al (<i>n</i> = 1271) (10), Liu et al (<i>n</i> = 2888) (11), and Absher et al (<i>n</i> = 1715) datasets (12) have expanded available imaging data, datasets pairing acute CT with follow-up MRI (13) remain limited. We address this gap by providing a publicly available dataset that combines hyperacute CT (< 24 h post onset) with acute postinterventional MRI (2-9 days after successful reperfusion; modified Treatment in Cerebral Ischemia 2c or 3) and structured clinical follow-up through 3 months. This combination enables analysis of infarct evolution and supports AI model development for postinterventional stroke care. © RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250603"},"PeriodicalIF":13.2,"publicationDate":"2026-04-22","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":"Fine-Tuned Large Language Models 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":"https://doi.org/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 to August 2024) was compiled. The dataset was used to fine-tune an LLM 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 LLM and GPT-4o. 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/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], 39,351 males) and the external test set included data for (27,471 reports, median age, 53 years [IQR, 37-66], 13,955 males). Site/modality-aware prompting improved similarity (<i>P</i> < .001): internal BERTScore-F/Sentence Similarity 0.92/0.92, external 0.82/0.80 under optimal settings; human evaluation (<i>n</i> = 2,327) 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% of blinded comparisons (1,657/2,400), reduced draft time by 0.46 min per report, and increased interradiologist agreement (<i>P</i> < .001). Conclusion MIRA, a fine-tuned LLM using a prompt-based strategy, generated clinically aligned radiology impressions in multicenter settings, improving accuracy, efficiency, and reporting consistency. © The Authors 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":13.2,"publicationDate":"2026-04-15","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":"Clinic-aligned Dual Distillation of Video and Image Foundation Models for Automated Breast Cancer US Diagnosis.","authors":"Chengqian Zhao, Yijie Dong, Xuan Xie, Guoqing Wu, Feiyu Yin, Wenwen Zeng, Pengfei Song, Yonghuang Wu, Lina Fu, Bingqian Nie, Hong Ding, Jianqiao Zhou, Jinhua Yu","doi":"10.1148/ryai.250600","DOIUrl":"https://doi.org/10.1148/ryai.250600","url":null,"abstract":"<p><p>Purpose To evaluate the clinical applicability of the US Dual-Distillation model (USDist) through comparative analysis with state-of-the-art models, ablation analysis of dual-distillation components, and assessment on portable US devices. Materials and Methods This retrospective multicenter study evaluated USDist using US video datasets collected from 16 medical centers (August 2016-December 2024) and two independent public datasets. The model integrates spatiotemporal dual-distillation and dynamic-static feature fusion to transfer feature representations from video and image foundation models. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with DeLong testing and Holm correction, along with evaluation of computational efficiency and qualitative feature visualization. Results A total of 5033 patients were analyzed (mean age, 49 years ± 12 [SD]; 5031 female). USDist achieved an average area under the ROC curve (AUC) of 0.95 (95% CI: 0.93-0.97) for breast cancer diagnosis across datasets. In the main cohort, USDist outperformed foundation models while using 98.3% fewer parameters. Across multicenter datasets, diagnostic performance was comparable to foundation models (all <i>P</i> < .05). On a portable US device, USDist maintained an AUC of 0.92 (95% CI: 0.86-0.95) with 4.1% of the computational cost of fullparameter fine-tuning. Conclusion USDist demonstrated high diagnostic performance for automated breast cancer diagnosis, with substantial parameter reduction compared with foundation models and maintained performance across multicenter and portable US settings. ©RSNA, 2026.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250600"},"PeriodicalIF":13.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634336","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}