JCO Clinical Cancer Informatics最新文献

{"title":"Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.","authors":"Hannah Barman, Sriram Venkateswaran, Antonio Del Santo, Unice Yoo, Eli Silvert, Krishna Rao, Bharathwaj Raghunathan, Lisa A Kottschade, Matthew S Block, G Scott Chandler, Joshua Zalis, Tyler E Wagner, Rajat Mohindra","doi":"10.1200/CCI.23.00151","DOIUrl":"10.1200/CCI.23.00151","url":null,"abstract":"<p><strong>Purpose: </strong>Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records.</p><p><strong>Methods: </strong>In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity.</p><p><strong>Results: </strong>For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time.</p><p><strong>Conclusion: </strong>Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11161244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140874915","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":"Service Evaluation of MyChristie-MyHealth, an Electronic Patient-Reported Outcome Measure Integrated Into Clinical Cancer Care.","authors":"Lee A Shipman, James Price, D. Abdulwahid, N. Bayman, Fiona H Blackhall, Raffaele Califano, C. Chan, J. Coote, Marie Eaton, Jacqueline Fenemore, Fabio Gomes, Margaret Harris, E. Halkyard, Colin Lindsay, H. Neal, D. Mcentee, H. Sheikh, Y. Summers, Paul Taylor, David Woolf, Janelle Yorke, Corinne Faivre-Finn","doi":"10.1200/CCI.23.00162","DOIUrl":"https://doi.org/10.1200/CCI.23.00162","url":null,"abstract":"PURPOSE\u0000Electronic patient-reported outcome measures (ePROMs) are digitalized health questionnaires used to gauge patients' subjective experience of health and disease. They are becoming prevalent in cancer care and have been linked to a host of benefits including improved survival. MyChristie-MyHealth is the ePROM established at the Christie NHS Foundation Trust in 2019. We conducted an evaluation of this service to understand user experiences, as well as strategies to improve its functioning.\u0000\u0000\u0000METHODS\u0000Data collection: Patients who had opted never to complete MyChristie-MyHealth (n = 87), and those who had completed at least one (n = 87) were identified. Demographic data included age, sex, ethnicity, postcode, diagnosis, treatment intent, and trial status. Semistructured interviews were held with noncompleters (n = 30) and completers (n = 31) of MyChristie-MyHealth, as well as clinician users (n = 6), covering themes such as accessibility, acceptability and usefulness, and open discourse on ways in which the service could be improved.\u0000\u0000\u0000RESULTS\u0000Noncompleters of MyChristie-MyHealth were older (median age 72 v 66 years, P = .005), receiving treatment with curative rather than palliative intent (odds ratio [OR], 1.45; P = .045), and less likely to be enrolled on a clinical trial (OR, 0.531; P = .011). They were less likely to own a smartphone (33% v 97%) or have reliable Internet access (45% v 100%). Satisfaction with MyChristie-MyHealth was high in both groups: 93% (n = 29) of completers and 87% (n = 26) noncompleters felt generally happy to complete. Completers of MyChristie-MyHealth wanted their results to be acknowledged by their clinicians. Clinicians wanted results to be displayed in a more user-friendly way.\u0000\u0000\u0000CONCLUSION\u0000We have broadly characterized noncompleters of the Christie ePROM to identify those in need of extra support or encouragement in the clinic. An action plan resulting from this review has been compiled and will inform the future development of MyChristie-MyHealth.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789211","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":"Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.","authors":"M. Jung, T. Diallo, Tobias Scheef, Marco Reisert, Alexander Rau, Maximilan F Russe, Fabian Bamberg, Stefan Fichtner-Feigl, M. Quante, Jakob Weiss","doi":"10.1200/CCI.23.00231","DOIUrl":"https://doi.org/10.1200/CCI.23.00231","url":null,"abstract":"PURPOSE\u0000Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC.\u0000\u0000\u0000MATERIALS AND METHODS\u0000We developed and tested a DL model to quantify BC measures defined as subcutaneous and visceral adipose tissue (VAT) and skeletal muscle on routine CT and investigated their prognostic value in a cohort of patients with GEAC using baseline, 3-6-month, and 6-12-month postoperative CTs. Primary outcome was all-cause mortality, and secondary outcome was disease-free survival (DFS). Cox regression assessed the association between (1) BC at baseline and mortality and (2) the decrease in BC between baseline and follow-up scans and mortality/DFS.\u0000\u0000\u0000RESULTS\u0000Model performance was high with Dice coefficients ≥0.94 ± 0.06. Among 299 patients with GEAC (age 63.0 ± 10.7 years; 19.4% female), 140 deaths (47%) occurred over a median follow-up of 31.3 months. At baseline, no BC measure was associated with DFS. Only a substantial decrease in VAT >70% after a 6- to 12-month follow-up was associated with mortality (hazard ratio [HR], 1.99 [95% CI, 1.18 to 3.34]; P = .009) and DFS (HR, 1.73 [95% CI, 1.01 to 2.95]; P = .045) independent of age, sex, BMI, Union for International Cancer Control stage, histologic grading, resection status, neoadjuvant therapy, and time between surgery and follow-up CT.\u0000\u0000\u0000CONCLUSION\u0000DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766512","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":"Integration of Telemedicine Consultation Into a Tertiary Radiation Oncology Department: Predictors of Use, Treatment Yield, and Effects on Patient Population.","authors":"Y. Sharifzadeh, William G Breen, W. S. Harmsen, A. Amundson, Allison E Garda, D. Routman, M. Waddle, Kenneth W Merrell, C. Hallemeier, Nadia N. Laack, Anantha Kollengode, Kimberly S Corbin","doi":"10.1200/CCI.23.00239","DOIUrl":"https://doi.org/10.1200/CCI.23.00239","url":null,"abstract":"PURPOSE\u0000The COVID-19 pandemic led to rapid expansion of telemedicine. The implications of telemedicine have not been rigorously studied in radiation oncology, a procedural specialty. This study aimed to evaluate the characteristics of in-person patients (IPPs) and virtual patients (VPs) who presented to a large cancer center before and during the pandemic and to understand variables affecting likelihood of receiving radiotherapy (yield) at our institution.\u0000\u0000\u0000METHODS\u0000A total of 17,915 patients presenting for new consultation between 2019 and 2021 were included, stratified by prepandemic and pandemic periods starting March 24, 2020. Telemedicine visits included video and telephone calls. Area deprivation indices (ADIs) were also compared.\u0000\u0000\u0000RESULTS\u0000The overall population was 56% male and 93% White with mean age of 63 years. During the pandemic, VPs accounted for 21% of visits, were on average younger than their in-person (IP) counterparts (63.3 years IP v 62.4 VP), and lived further away from clinic (215 miles IP v 402 VP). Among treated VPs, living closer to clinic was associated with higher yield (odds ratio [OR], 0.95; P < .001). This was also seen among IPPs who received treatment (OR, 0.96; P < .001); however, the average distance from clinic was significantly lower for IPPs than VPs (205 miles IP v 349 VP). Specialized radiotherapy (proton and brachytherapy) was used more in VPs. IPPs had higher ADI than VPs. Among VPs, those treated had higher ADI (P < .001).\u0000\u0000\u0000CONCLUSION\u0000Patient characteristics and yield were significantly different between IPPs and VPs. Telemedicine increased reach to patients further away from clinic, including from rural or health care-deprived areas, allowing access to specialized radiation oncology care. Telemedicine has the potential to increase the reach of other technical and procedural specialties.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763654","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":"Methodology for Using Real-World Data From Electronic Health Records to Assess Chemotherapy Administration in Women With Breast Cancer.","authors":"Jenna Bhimani, K. O'Connell, I. Ergas, Marilyn J. Foley, Grace B Gallagher, Jennifer J Griggs, Narre Heon, Tatjana Kolevska, Yuriy Kotsurovskyy, Candyce H Kroenke, Cecile A Laurent, Raymond Liu, Kanichi G Nakata, Sonia Persaud, Donna R Rivera, Janise M. Roh, Sara M. Tabatabai, Emily Valice, Erin J A Bowles, Elisa V Bandera, Lawrence H Kushi, Elizabeth D Kantor","doi":"10.1200/CCI.23.00209","DOIUrl":"https://doi.org/10.1200/CCI.23.00209","url":null,"abstract":"PURPOSE\u0000Identification of patients' intended chemotherapy regimens is critical to most research questions conducted in the real-world setting of cancer care. Yet, these data are not routinely available in electronic health records (EHRs) at the specificity required to address these questions. We developed a methodology to identify patients' intended regimens from EHR data in the Optimal Breast Cancer Chemotherapy Dosing (OBCD) study.\u0000\u0000\u0000METHODS\u0000In women older than 18 years, diagnosed with primary stage I-IIIA breast cancer at Kaiser Permanente Northern California (2006-2019), we categorized participants into 24 drug combinations described in National Comprehensive Cancer Network guidelines for breast cancer treatment. Participants were categorized into 50 guideline chemotherapy administration schedules within these combinations using an iterative algorithm process, followed by chart abstraction where necessary. We also identified patients intended to receive nonguideline administration schedules within guideline drug combinations and nonguideline drug combinations. This process was adapted at Kaiser Permanente Washington using abstracted data (2004-2015).\u0000\u0000\u0000RESULTS\u0000In the OBCD cohort, 13,231 women received adjuvant or neoadjuvant chemotherapy, of whom 10,213 (77%) had their intended regimen identified via the algorithm, 2,416 (18%) had their intended regimen identified via abstraction, and 602 (4.5%) could not be identified. Across guideline drug combinations, 111 nonguideline dosing schedules were used, alongside 61 nonguideline drug combinations. A number of factors were associated with requiring abstraction for regimen determination, including: decreasing neighborhood household income, earlier diagnosis year, later stage, nodal status, and human epidermal growth factor receptor 2 (HER2)+ status.\u0000\u0000\u0000CONCLUSION\u0000We describe the challenges and approaches to operationalize complex, real-world data to identify intended chemotherapy regimens in large, observational studies. This methodology can improve efficiency of use of large-scale clinical data in real-world populations, helping answer critical questions to improve care delivery and patient outcomes.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140787228","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":"Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.","authors":"Zuzanna Wójcik, Vania Dimitrova, Lorraine Warrington, Galina Velikova, Kate Absolom","doi":"10.1200/CCI.23.00264","DOIUrl":"10.1200/CCI.23.00264","url":null,"abstract":"<p><strong>Purpose: </strong>Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy.</p><p><strong>Materials and methods: </strong>Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes.</p><p><strong>Results: </strong>The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only.</p><p><strong>Conclusion: </strong>These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11161248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140871744","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":"Evolving From Discrete Molecular Data Integrations to Actionable Molecular Insights Within the Electronic Health Record.","authors":"James L Chen, M. Stumpe, Ezra Cohen","doi":"10.1200/CCI.24.00011","DOIUrl":"https://doi.org/10.1200/CCI.24.00011","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140772737","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":"Multimodal Approach in the Diagnosis of Urologic Malignancies: Critical Assessment of ChatGPT-4V's Image-Reading Capabilities.","authors":"Lingxuan Zhu, Yancheng Lai, Na Ta, Liang Cheng, Rui Chen","doi":"10.1200/CCI.23.00275","DOIUrl":"https://doi.org/10.1200/CCI.23.00275","url":null,"abstract":"ChatGPT-4V model with image interpretation tested for distinguishing kidney & prostate tumors from normal tissue.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140786438","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":"Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.","authors":"Elizabeth A. Sloss, Jordan P. McPherson, Anna C Beck, Jia-Wen Guo, Carolyn H. Scheese, Naomi R Flake, George Chalkidis, C. Staes","doi":"10.1200/CCI.23.00187","DOIUrl":"https://doi.org/10.1200/CCI.23.00187","url":null,"abstract":"PURPOSE\u0000Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy.\u0000\u0000\u0000METHODS\u0000This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified.\u0000\u0000\u0000RESULTS\u0000We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information.\u0000\u0000\u0000CONCLUSION\u0000This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140785842","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":"Shining a Light: Unveiling Cardiac Risks Using Positron Emission Tomography Imaging in Lung Cancer Radiotherapy.","authors":"Tobias Finazzi, P. Putora","doi":"10.1200/CCI.24.00045","DOIUrl":"https://doi.org/10.1200/CCI.24.00045","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758037","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}