Precision Radiation Oncology最新文献

{"title":"Clinical advances and challenges in dose constraints for organs at risk in proton therapy: A systematic review of the last decade's literature.","authors":"Jinghao Duan, Cheng Tao, Shizhang Wu, Chengqiang Li, Jinhu Chen, Tianyuan Dai, Yunyi Fan, Tong Bai, Xiangjuan Meng, Tian Kong, Jian Zhu","doi":"10.1002/pro6.70020","DOIUrl":"10.1002/pro6.70020","url":null,"abstract":"<p><p>Proton beam therapy has demonstrated significant clinical efficacy across multiple malignancies, primarily attributed to its distinct physical dose deposition characteristics. However, clinical implementation remains constrained by resource limitations and accumulated experience, particularly regarding radiation tolerance thresholds for organs at risk (OARs). Unlike photon-based radiotherapy where consensus guidelines like QUANTEC have been established, standardized dose constraints for proton therapy require further validation. This systematic review synthesizes decade-long evidence from peer-reviewed literature and clinical guidelines, critically evaluating current understanding of OARs tolerance in proton therapy. The comprehensive analysis aims to inform clinical decision-making and protocol development for emerging proton therapy.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 3","pages":"210-220"},"PeriodicalIF":3.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402074","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":"Feasibility study of a multi-lesion cyberknife radiotherapy plan verification method using a 2D array with pre-set roll angles.","authors":"Tengxiang Li, Jinhu Chen, Ruimin He, Qingtao Qiu, Quan Tang, Yong Yin","doi":"10.1002/pro6.70022","DOIUrl":"10.1002/pro6.70022","url":null,"abstract":"<p><strong>Background: </strong>When validating intracranial multi-lesion CyberKnife M6/S7 plans with SRSmapcheck, setting the array to a fixed 0° measures only one target dose distribution, leaving the other lesions unmeasured. Moreover, the CyberKnife treatment planning system does not support roll verification tools, and testing confirms that X-sight fiducial marker guidance is incompatible with free array roll. A novel method and workflow are required to validate multi-lesion plans with random positions.</p><p><strong>Methods: </strong>A geometric model was established based on the relationship between SRSmapcheck and the tumor location. For two tumors spaced 77 mm apart (each 20 mm in diameter, or one 40 mm apart and the other infinitesimally small), the corresponding array roll angle interval was approximately 15.05°. The SRSmapcheck and StereoPHAN computed tomography (CT) images were acquired at 15° intervals, starting at 0°, and preprocessed into phantom plans for verification. A total of 101 intracranial multi-lesion plans were verified using the fixed 0° and pre-set roll angle methods to optimize the dose distribution, particularly in high-dose and rapidly varying areas. A two-sample test compared the results of the 0° versus pre-set roll angle verification and assessed the performance under different criteria to determine suitable criteria for pre-set roll angle verification.</p><p><strong>Results: </strong>The equivalent diameter of the 296 tumors ranged from 5 to 45 mm (average: 21.86 mm). Each plan had an average of 2.97 lesions. Using the pre-set roll angle method, 2.34 targets were assessed on average (89.83% of lesions had diameters ranging from 10 to 40 mm), compared to 1.32 targets on average in 0° plans. Statistically significant differences occurred at 2 mm/1% and 2 mm/2% in the γ analysis, showing that plan pass rates were stable between the fixed 0° and pre-set roll angle methods. Relaxing either the distance to agreement or dose deviation significantly increased the pass rates during pre-set roll angle verification, whereas cross-transforming criteria had minimal impact. For pre-set roll angle methods, it is recommended to use 1 mm/1% (action limit: 86.0% ± 13.3%) and 1 mm/2% (action limits: 91.6% ± 7.9%) for γ analysis.</p><p><strong>Conclusion: </strong>SRSmapcheck with the pre-set roll angle method can verify intracranial multi-lesion CyberKnife plans by measuring multiple targets in a single validation and comparing the 1 mm/1% and 1 mm/2% γ analysis criteria.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 3","pages":"167-176"},"PeriodicalIF":3.6,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402202","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":"Optimized fiducial marker placement using B-spline surface modeling and graph theory for Cyberknife stereotactic body radiotherapy for superficial tumors.","authors":"Jing Huang, Xianlong Xiong, Cheng Chen, Yuhan Li, Ruijie Wang, Zhitao Dai","doi":"10.1002/pro6.70017","DOIUrl":"10.1002/pro6.70017","url":null,"abstract":"<p><p>CyberKnife, an established noninvasive stereotactic radiotherapy technology, has been extensively utilized to treat various malignancies because of its high precision and conformal dose delivery. The success of CyberKnife treatment is crucially dependent on optimal fiducial marker placement. This study introduces a novel fiducial marker placement planning algorithm tailored for superficial tumors, which are located 20-50 mm beneath the epidermis. A retrospective analysis was performed on the data collected from three patients with thymus, breast, and submandibular gland tumors. This algorithm generated potential implantation sites by constructing and optimizing a B-spline surface around the tumor. Candidate points were filtered using multi-criteria constraints: (1) a minimum of 18-mm inter-marker distance, (2) angular separation >30°, and (3) nonoverlapping visibility in 45° oblique digital reconstructed radiographs. To enhance the computational efficiency, a kd-tree spatial indexing structure was integrated with graph theory, specifically the Bron-Kerbosch algorithm for maximal clique detection. The proposed method achieved a time complexity of <math> <mrow><mrow><mi>O</mi> <mo>(</mo> <mrow><mi>mlogm</mi> <mo>+</mo> <msup><mi>m</mi> <mn>2</mn></msup> <mo>+</mo> <msup><mn>3</mn> <mfrac><mi>n</mi> <mn>3</mn></mfrac> </msup> </mrow> <mo>)</mo></mrow> </mrow> </math> , demonstrating a significant improvement over the brute-force <math> <mrow><mrow><mi>O</mi> <mo>(</mo> <msup><mi>n</mi> <mn>3</mn></msup> <mo>)</mo></mrow> </mrow> </math> approach. The experimental results showed that our algorithm could efficiently plan fiducial marker placement, thereby simplifying the planning process and providing valuable technical support for CyberKnife treatments.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"87-95"},"PeriodicalIF":3.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401828","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":"Dosimetric and hematological toxicity analyses of bone marrow-sparing intensity-modulated radiation therapy for patients with cervical cancer treated with extended-field radiation therapy.","authors":"Jia-Nan Wang, Xi Yu, Li-Na Gu, Dong-Mei Liu, Qiu-Yue Su, Jing-Qi Xia, Wei-Kang Yun, Xin Li, Xue-Yuan Hu, Shan-Shan Yang, De-Yang Yu","doi":"10.1002/pro6.70019","DOIUrl":"10.1002/pro6.70019","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to assess the dosimetric parameters and hematological toxicity (HT) associated with bone marrow-sparing (BMS) intensity-modulated radiation therapy (IMRT) in patients diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage IIIC cervical cancer undergoing extended-field radiation therapy (EFRT).</p><p><strong>Methods: </strong>Patients with cervical cancer presenting with common iliac or para-aortic lymph node metastases require EFRT, which often results in grade 3 HT. Therefore, we retrospectively analyzed data of 84 patients with FIGO stage IIIC cervical cancer who underwent concurrent chemoradiotherapy (EFRT, brachytherapy, and weekly cisplatin 40 mg/m²) at Harbin Medical University Cancer Hospital, including 40 who received BMS-IMRT and 44 who received normal IMRT. Dose-volume histogram (DVH) parameters and estimated treatment times were compared. We also compared acute HT between the normal and BMS groups.</p><p><strong>Results: </strong>Dosimetric analysis demonstrated that BMS-IMRT significantly reduced the mean volume of bone marrow receiving ≥10, ≥20, ≥30, and ≥40 Gy without affecting the target coverage of planning target volume and sparing the organs at risk. Within the BMS-IMRT group, 37.5% of the patients developed grade ≥3 HT, with an increase in HT (HT3+ = 61.4%) in patients receiving normal-IMRT (<i>P</i> = 0.029).</p><p><strong>Conclusions: </strong>For patients with cervical cancer treated with EFRT, BMS-IMRT represents a feasible treatment approach that may mitigate HT and facilitate the uninterrupted administration of concurrent chemoradiotherapy.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"96-107"},"PeriodicalIF":3.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401849","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":"Fast estimation of patient-specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models.","authors":"Wencheng Shao, Liangyong Qu, Xin Lin, Ying Huang, Weihai Zhuo, Haikuan Liu","doi":"10.1002/pro6.70016","DOIUrl":"10.1002/pro6.70016","url":null,"abstract":"<p><strong>Background: </strong>Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X-ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use.</p><p><strong>Methods: </strong>CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R²).</p><p><strong>Results: </strong>The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R²: 0.9), eyes (MAPE: 5%, R²: 0.84), lens (MAPE: 5%, R²: 0.82), bowel (MAPE: 6%, R²: 0.84), kidneys (MAPE: 7.5%, R²: 0.7), and liver (MAPE: 8%, R²: 0.67). Internal organ disturbances had a minimal impact on accuracy.</p><p><strong>Conclusions: </strong>The SVR models efficiently predicted patient-specific organ doses from CT scans, offering a user-friendly tool for rapid segmentation-free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient-specific organ doses using CT.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"77-86"},"PeriodicalIF":3.6,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401919","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":"Deep learning-based dose prediction for low-energy electron beam superficial radiotherapy.","authors":"Jialin Huang, Zhitao Dai, Shuai Hu, Yuanchun Ye, Yuling Chen, Ming Li, Tianye Niu, Jinfen Zheng, Yongsheng Huang, Yuanjie Bi","doi":"10.1002/pro6.70015","DOIUrl":"10.1002/pro6.70015","url":null,"abstract":"<p><strong>Background: </strong>Accurate surface dose calculation is crucial in superficial low-energy electron beam radiotherapy owing to shallow treatment depths and the risk of skin toxicity. Traditional Monte Carlo (MC) simulations are precise but computationally expensive and time-consuming.</p><p><strong>Methods: </strong>This study combined MC simulations with deep learning to improve both accuracy and speed. DOSXYZnrc was used to simulate low-energy electron beams for six body sites, generating computed tomography phantoms and corresponding dose distributions. A cascaded 3D U-Net (C3D) model was trained on these datasets to predict dose distributions rapidly.</p><p><strong>Results: </strong>The C3D model demonstrated significant improvements over traditional 3D U-Net models, achieving a minimum Gamma pass rate of 92.09% and a minimum dose difference pass rate of 93.58%. The model completed dose predictions in just 0.42 seconds, making predictions approximately 140,000 times faster than MC simulations. In the evaluation of dose distributions across six anatomical regions, C3D consistently outperformed other deep learning models (3D U-Net, Deep Convolutional Neural Network, and HD U-Net) in both accuracy and robustness.</p><p><strong>Conclusion: </strong>The integration of deep learning with MC simulations significantly enhances the efficiency of surface dose calculations in superficial electron beam radiotherapy. The C3D model provides rapid and accurate dose predictions, facilitating efficient treatment planning while maintaining high accuracy.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"108-119"},"PeriodicalIF":3.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401875","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":"Deep learning (DL)-based advancements in prostate cancer imaging: Artificial intelligence (AI)-based segmentation of ⁶⁸Ga-PSMSA PET for tumor volume assessment.","authors":"Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid Al Sukaiti, Maria Ahmed, Nadeem Pervez","doi":"10.1002/pro6.70014","DOIUrl":"10.1002/pro6.70014","url":null,"abstract":"<p><p>Positron emission tomography (PET) with gallium-68 prostate-specific membrane antigen (⁶⁸Ga-PSMA) has emerged as a promising imaging modality for evaluating prostate cancer (PC). Quantification of tumor volume is crucial for staging, radiotherapy treatment planning, response assessment, and prognosis in PC patients. This review provides an overview of the current methods and challenges in the assessment of regional and total tumor volumes using ⁶⁸Ga-PSMA PET. Traditional manual segmentation methods are time-consuming processes that are further challenged by inter-observer variability. Artificial intelligence (AI)-based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning-based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. This review discusses the principles underlying AI-based segmentation algorithms, including convolutional neural networks, and their applications in PC imaging. Furthermore, the advantages of AI-based segmentation are highlighted, such as improved reproducibility, faster processing times, and potential for personalized medicine. Despite these advancements, AI-based segmentation faces significant challenges, including the need for standardized protocols, extensive validation studies, and seamless integration into clinical workflows. Addressing these limitations is essential for the widespread adoption of AI-based segmentation in ⁶⁸Ga-PSMA PET for PC, ultimately advancing the field and improving patient care.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"120-132"},"PeriodicalIF":3.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401872","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":"Radiotherapy after Neoadjuvant Immunochemotherapy in Unresectable Stage III Non-Small Cell Lung Carcinoma: A Novel Therapeutic Approach?","authors":"Peizhu Wu, Chaozhuo Li, Zhonghui Wei, Xiangjiao Meng","doi":"10.1002/pro6.70012","DOIUrl":"10.1002/pro6.70012","url":null,"abstract":"","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"143-144"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402064","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":"Massive Infiltrative Osteolysis of the Mandible in Oral Squamous Cell Carcinoma: A Case Report with a Review of the Literature.","authors":"Smrithy Sivadas K, Ananya Madiyal, Vidya Ajila, Krishna Sharan, Yashika Jain","doi":"10.1002/pro6.70011","DOIUrl":"10.1002/pro6.70011","url":null,"abstract":"<p><p>Oral Squamous Cell Carcinoma (OSCC) is the most prevalent form of oral cancer, constituting over 90% of reported cases. This malignancy commonly infiltrates bone, making bone invasion a significant clinical issue. OSCC may invade bone via either an infiltrative or erosive pattern, with the pattern of invasion closely correlating with the clinical behavior of the disease and potentially holding prognostic value. Typically, OSCC spreads to the mandibular bone through direct infiltration of the alveolar ridge or lingual cortical plate. Interestingly, only 6% of OSCC cases initially present with a primary tumor, necessitating comprehensive whole-body imaging and clinical examinations to exclude other primary tumors. This report details a rare case of long-standing OSCC of the retromolar pad, which led to infiltrative osteolysis of the mandible, culminating in the near-total disappearance of the bone in a 47-year-old male patient.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"145-151"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145401852","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":"A safety study of ultra-high dose rate FLASH radiotherapy in the treatment of superficial skin tumors: study protocol of a phase I trial (ChiCTR2400080935).","authors":"Chengliang Yang, Hui Luo, Ma Leijie, Ronghu Mao, Hongchang Lei, Yanping Zhang, Meng Xu, Yiwu Wang, Mingxia Wu, Han Liu, Peng Chen, Hong Ge","doi":"10.1002/pro6.70010","DOIUrl":"10.1002/pro6.70010","url":null,"abstract":"<p><strong>Objective: </strong>Ultra-high dose rate FLASH radiotherapy (FLASH-RT) is emerging as a novel technique to improve the normal tissue tolerance by delivering ultra-high dose rate radiation several orders of magnitude higher than convention radiotherapy. It has been shown in preclinical studies to cause less injury to surrounding normal tissues during radiation treatment, while still maintaining local tumor control. The purpose of this protocol is to evaluate the safety of fractionated FLASH-RT in skin cancer.</p><p><strong>Method: </strong>Patients with superficial skin tumors will be enrolled. The eligible patients will undergo electron FLASH-RT (24-40 Gy/3-5 fractions) to the tumor volume. The primary outcome is to evaluate the safety of FLASH-RT by collecting the acute (< 90 days) skin toxicity adverse events of radiation according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Secondary objectives include late (> 90 days) skin toxicity after FLASH-RT according to CTCAE version 5.0 and treatment response.</p><p><strong>Discussion: </strong>If the results show that delivering FLASH-RT is safe and feasible for skin tumors, further investigation will be conduct to evaluate efficacy of FLASH-RT in a phase II trial.</p><p><strong>Trial registration number: </strong>ChiCTR2400080935. https://www.chictr.org.cn/showproj.html?proj=220336.</p>","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":"9 2","pages":"72-76"},"PeriodicalIF":3.6,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12559900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402207","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}