ESMO Real World Data and Digital Oncology最新文献

{"title":"Automated risk stratification in myelofibrosis","authors":"D.T. Kristensen , T.C. El-Galaly , A.S. Roug","doi":"10.1016/j.esmorw.2025.100654","DOIUrl":"10.1016/j.esmorw.2025.100654","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100654"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617882","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":"Editoiral Board","authors":"","doi":"10.1016/S2949-8201(25)00561-2","DOIUrl":"10.1016/S2949-8201(25)00561-2","url":null,"abstract":"","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100672"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789920","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":"Proof-of-concept: AI-assisted, natural-language-guided survival analysis achieves concordance with human-conducted results in glioblastoma","authors":"E. Omene ,&nbsp;C. Marsters ,&nbsp;J. Easaw ,&nbsp;K. Young ,&nbsp;C. Kolbeck ,&nbsp;O. Abdelsalem ,&nbsp;Y. Yuan","doi":"10.1016/j.esmorw.2025.100653","DOIUrl":"10.1016/j.esmorw.2025.100653","url":null,"abstract":"<div><h3>Background</h3><div>The use of large language models with natural-language prompting may simplify survival analysis but requires validation against standard analyses carried out by trained analysts. We compared a human-conducted SPSS survival analysis to the Replit cloud development platform for conversational statistical analysis using a fully observed cohort of 1265 glioblastoma patients.</div></div><div><h3>Patients and methods</h3><div>Two statistical procedures—Kaplan–Meier estimation and Cox proportional hazards regression—were implemented. The reference analysis was carried out by an experienced clinical researcher using SPSS; the Replit-based analysis by an oncologist with no graduate level statistical or programming training employed the lifelines package through Claude language model integration via conversational chatbox interface. Statistical results were reviewed by a data science professor. Artificial intelligence- (AI) generated code was reviewed by a machine learning engineer. All patients had died at last follow-up, so no censoring occurred. Concordance was defined as exact matches in median survival times and hazard ratios (HR), reflecting the scientific principle that identical datasets processed with identical statistical methods must produce identical results.</div></div><div><h3>Results</h3><div>Initial comparison of median survival across 12 molecular/age subgroups found exact concordance in 7 of 12 subgroups (58.3%). The remaining discrepancies represented preprocessing differences rather than acceptable analytical variation. Natural-language troubleshooting through Replit’s conversational interface identified three sources of discrepancy: patient inclusion differences, age-group boundary definitions, and inconsistent molecular encoding. After harmonizing these factors, median survival times and HRs were identical across both analyses, achieving 100% concordance. The Replit-based approach required 1 h 40 min total time compared with 8.5 h for traditional analysis, representing a 80% time reduction while maintaining statistical rigor.</div></div><div><h3>Conclusions</h3><div>This proof-of-concept demonstrates that the Replit platform can achieve exact replication of standard Kaplan–Meier and Cox analyses carried out by an experienced analyst when subgroup definitions and data preprocessing are aligned. Although our findings are limited to a single dataset and workflow, they suggest conversational AI interfaces could reduce barriers to statistical analysis for clinical researchers. Broader validation across varied analytical scenarios is essential before widespread clinical implementation. Statistical expertise remains essential for data quality assessment and model validation.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100653"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684415","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":"Transportability of overall survival estimates from the USA to France in patients with metastatic triple-negative breast cancer","authors":"C. Esnault ,&nbsp;L. Simomia ,&nbsp;G. Machuron ,&nbsp;M. Robain ,&nbsp;B. Adamson ,&nbsp;V. Machuron","doi":"10.1016/j.esmorw.2025.100648","DOIUrl":"10.1016/j.esmorw.2025.100648","url":null,"abstract":"<div><h3>Background</h3><div>Metastatic triple-negative breast cancer (mTNBC) represents an aggressive breast cancer subtype with limited treatment options. The external validity of transportability for real-world evidence (RWE) applied across health care systems remains uncertain. This study evaluated the transportability of overall survival (OS) from the USA to France.</div></div><div><h3>Patients and methods</h3><div>We conducted a retrospective cohort study using de-identified or aggregated electronic health record-derived data from the USA and France. The study included adult female patients diagnosed with mTNBC initiating first-line systemic therapy before the widespread use of poly(ADP-ribose) polymerase (PARP) and programmed cell death (ligand) protein 1 (PD-(L)1) checkpoint inhibitors. To account for population differences, we reweighted the United States cohort to match the French population characteristics. OS was assessed from the start of first-line therapy using Kaplan–Meier curves.</div></div><div><h3>Results</h3><div>After adjustment, the United States (<em>n</em> = 812) and French (<em>n</em> = 2797) cohorts were well-balanced (standardized mean differences close to 0). Median OS was comparable between the unadjusted United States cohort [13.8 months, 95% confidence interval (CI) 12.6-15.0 months], the adjusted United States cohort (13.2 months, 95% CI 11.6-14.6 months) and the French cohort (13.8 months, 95% CI 13.2-14.6 months). OS rates at 1, 2, and 3 years in the French cohort were 55%, 30%, and 18%, respectively, and did not differ by &gt;3% from those in the USA, regardless of adjustment.</div></div><div><h3>Conclusions</h3><div>This study shows evidence that OS estimates for mTNBC derived from United States RWE are transportable to the French setting. Findings support the use of international RWE in health technology assessment contexts, potentially facilitating more efficient and evidence-based decision making for novel therapies.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100648"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571604","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":"Prognostic role of Ki-67 in high-risk estrogen receptor-positive/HER2-negative early breast cancer: a population-based cohort study","authors":"E. Tegnelius ,&nbsp;J. Ahlgren ,&nbsp;A. Valachis","doi":"10.1016/j.esmorw.2025.100647","DOIUrl":"10.1016/j.esmorw.2025.100647","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Abemaciclib is a CDK4/6-inhibitor approved for adjuvant use in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer with high risk of relapse, defined as N2-N3 disease or high-risk N1 disease. The MonarchE study also included high Ki-67 in combination with N1 disease as a separate study cohort. We investigated the prognostic value of MonarchE risk group criteria, with or without the addition of Ki-67, when applied to a large set of real-world data (RWD).</div></div><div><h3>Materials and methods</h3><div>We carried out a retrospective cohort study using Breast Cancer Data Base Sweden (BCBaSe) 3.0, a research database including all Swedish breast cancer patients from 2008 to 2020. The cohort included 21 110 ER-positive/HER2-negative breast cancer patients, whereof 12 983 had available Ki-67 data.</div></div><div><h3>Results</h3><div>Risk groups were defined as N1 low risk, N1 high risk and N2-N3 very high risk, based on MonarchE criteria. Survival analyses showed an increasingly worse prognosis across the three groups for three outcomes including distant disease-free survival (DDFS), breast cancer-specific survival (BCSS), and overall survival (OS). Ki-67 ≥20% was associated with statistically significant worse DDFS, BCSS, and OS in the overall N1 group, but when comparing 5-year survival rates in the N1 group with and without Ki-67 stratification, no clinically meaningful absolute difference in OS was seen.</div></div><div><h3>Conclusions</h3><div>Our nationwide RWD results support the use of MonarchE's risk group criteria for selection of high-risk ER-positive/HER2-negative breast cancer patients who might derive benefit from adjuvant abemaciclib. The role of Ki-67 in risk stratification seems to be limited and without clear clinical significance.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100647"},"PeriodicalIF":0.0,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571605","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":"Recommendations and definitions for time-to-event endpoints using real-world data in oncology","authors":"P.A.J. Vissers ,&nbsp;M.A.G. Elferink ,&nbsp;M.J. Bijlsma ,&nbsp;L.G. van der Geest ,&nbsp;M. Koopman ,&nbsp;H.W.M. van Laarhoven ,&nbsp;G.A.P. Nieuwenhuijzen ,&nbsp;P.S.N. van Rossum ,&nbsp;F.P.C. Sijtsma ,&nbsp;G.R. Vink ,&nbsp;J. de Vos-Geelen ,&nbsp;H.W. Wilmink ,&nbsp;J.H.W. de Wilt ,&nbsp;R.H.A. Verhoeven ,&nbsp;F.N. van Erning","doi":"10.1016/j.esmorw.2025.100649","DOIUrl":"10.1016/j.esmorw.2025.100649","url":null,"abstract":"<div><h3>Background</h3><div>Real-world data is increasingly used to assess effectiveness of treatments in patients treated in everyday clinical practice. The aim of this study is to establish definitions for time-to-event endpoints based on real-world data, and to evaluate their applicability to cancer registry data.</div></div><div><h3>Materials and methods</h3><div>A multidisciplinary panel consisting of seven epidemiologists, one statistician and seven medical specialists with expertise in gastrointestinal oncology was composed to reach consensus on the definitions of time-to-event endpoints. After obtaining the final definitions, the time-to-event endpoints were applied to a population-based cohort of patients with different types of gastrointestinal cancer from the Netherlands Cancer Registry.</div></div><div><h3>Results</h3><div>For nine time-to-event endpoints, consensus on the definitions was reached: real-world data based (RW) recurrence-free survival, recurrence rate, local recurrence rate, locoregional recurrence rate, distant recurrence rate, progression-free survival, progression rate, treatment failure rate, and overall survival. For RW disease-free survival, no consensus was reached. All time-to-event endpoints appeared suitable for calculation with Netherlands Cancer Registry data for cohorts with follow-up data based on the established definitions, with the exception of RW-treatment failure rate due to the unavailability of cause of death.</div></div><div><h3>Conclusions</h3><div>This study offers clear definitions for time-to-event endpoints based on RW data which can be applied to cancer registry data. Uniform use of these definitions in future studies will enable better interpretation of results and aid in comparison between studies, but may require additional development when adapted to other countries, cancer types or data sources.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100649"},"PeriodicalIF":0.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571606","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":"Synthetic data for clinical research and innovation: opportunities, challenges and future directions","authors":"Mattia Delleani","doi":"10.1016/j.esmorw.2025.100651","DOIUrl":"10.1016/j.esmorw.2025.100651","url":null,"abstract":"<div><div>Data form the backbone of modern health care, driving improvements in patient care, enabling clinical research and supporting public health initiatives. The demand for high-quality individual-level data is growing, with electronic health records and clinical trial data offering critical opportunities for secondary analyses, hypothesis testing and methodological innovation. However, accessing and sharing this high-quality real-world data to enable personalized care and to respond to rapidly changing conditions is often limited by strict privacy requirements and complex regulations. This perspective highlights the promise of synthetic data in enabling research access, accelerating artificial intelligence development and supporting clinical trials while also discussing key barriers, including legal and ethical uncertainty, re-identification risk and the need for standardized validation frameworks. In this context, synthetic data present a promising path forward by enabling research while addressing privacy concerns, streamlining administrative processes and decreasing costs.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100651"},"PeriodicalIF":0.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571602","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":"JAVEMACS: a real-world study of avelumab maintenance therapy for advanced urothelial carcinoma in Japan","authors":"H. Kitamura ,&nbsp;T. Kobayashi ,&nbsp;Y. Endo ,&nbsp;M. Ikeda ,&nbsp;K. Yonemori ,&nbsp;M. Nakayama ,&nbsp;A. Fujihara ,&nbsp;T. Abe ,&nbsp;F. Shimizu ,&nbsp;K. Fujimoto ,&nbsp;T. Nakagawa ,&nbsp;S. Hatakeyama ,&nbsp;K. Murakami ,&nbsp;K. Nishihara ,&nbsp;D. Ikarashi ,&nbsp;N. Masumori ,&nbsp;S. Naito ,&nbsp;K. Fujita ,&nbsp;N. Hayakawa ,&nbsp;T. Hara ,&nbsp;E. Kikuchi","doi":"10.1016/j.esmorw.2025.100646","DOIUrl":"10.1016/j.esmorw.2025.100646","url":null,"abstract":"<div><h3>Background</h3><div>Avelumab maintenance therapy was approved in Japan in February 2021 for patients with unresectable locally advanced or metastatic urothelial carcinoma (la/mUC) without disease progression after platinum-based chemotherapy (PBC). We report the primary analysis from the JAVEMACS chart review study of avelumab maintenance therapy in Japan.</div></div><div><h3>Materials and methods</h3><div>This multicenter, retrospective study collected data from medical charts of patients with la/mUC who received avelumab maintenance after first-line (1L) PBC.</div></div><div><h3>Results</h3><div>Between February 2021 and December 2023, 350 patients received avelumab maintenance; median age was 73 years and most were male (74.0%). Prior 1L PBC was cisplatin–gemcitabine in 56.0% and carboplatin–gemcitabine in 33.1%; 28.6% were cisplatin eligible and 52.9% were cisplatin ineligible/platinum eligible. At data cut-off (June 2024), 67 patients (19.1%) were still receiving avelumab. Among 283 patients who discontinued avelumab, 200 (70.7%) received second-line (2L) treatment. In the overall population, median overall survival (OS) from avelumab initiation was 31.8 months [95% confidence interval (CI) 24.6 months-not estimable (NE)]. In subgroups who received 2L enfortumab vedotin (EV) (<em>n</em> = 133), PBC (<em>n</em> = 41), or pembrolizumab (<em>n</em> = 17), median OS from the start of 2L treatment was 17.8 months (95% CI 11.9 months-NE), 15.1 months (95% CI 11.6 months-NE), and 15.6 months (95% CI 8.6-21.3 months), respectively. Limitations include the study’s retrospective nature.</div></div><div><h3>Conclusions</h3><div>Results from JAVEMACS confirm the effectiveness of avelumab maintenance in real-world clinical practice in Japan, supporting its recommendation for patients with la/mUC without disease progression following 1L PBC. This treatment sequence followed by 2L EV appeared to be associated with encouraging long-term outcomes in this real-world population.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100646"},"PeriodicalIF":0.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571603","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":"From Lindbergh’s cockpit to predictive, AI-driven health care: a roadmap for high-reliability medicine","authors":"B. Fuchs ,&nbsp;P. Heesen","doi":"10.1016/j.esmorw.2025.100650","DOIUrl":"10.1016/j.esmorw.2025.100650","url":null,"abstract":"<div><div>Modern health care remains largely intuition-driven, reactive, and fragmented, resulting in preventable errors and inefficiencies. In contrast, aviation has evolved into a highly structured, automated, and predictive safety system, achieving near-zero accident rates through standardization, automation, and artificial intelligence (AI)-assisted decision making. While medicine has incorporated certain aviation-inspired practices—such as checklists, crew resource management, and simulation training—it lacks the comprehensive data-driven framework and continuous oversight that underpins aviation’s safety culture. This perspective proposes a four-phase roadmap for integrating AI into health care, drawing on lessons from aviation’s transformation over the past century. Phase I emphasizes the standardization of clinical data and interoperability, mirroring aviation’s shift from handwritten logs to digital flight recorders. Phase II introduces AI-assisted decision support, paralleling the emergence of autopilot and early flight control systems. Phase III highlights real-time predictive monitoring, analogous to continuous flight data monitoring, while phase IV envisions autonomous health care systems, reflecting modern aircraft’s capacity for automated flight. However, aviation’s experience also reveals the perils of over-reliance on technology, including automation bias and deskilling, underscoring the need for regulatory adaptation, transparent error reporting, and robust human–AI collaboration. By integrating these safeguards, medicine can carefully integrate AI’s transformative potential to reduce errors, improve efficiency, and enhance patient outcomes, thereby shifting from an intuition-driven model to one of precision and reliability.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100650"},"PeriodicalIF":0.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571523","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":"Trends in smoking and obesity prevalence at breast cancer diagnosis: results from a 35-year real-world observational study","authors":"R.D.S. Gbessemehlan ,&nbsp;F. Nguyen Van Long ,&nbsp;N.K.D. Adenyo ,&nbsp;O. Haroun ,&nbsp;A. Turgeon ,&nbsp;A. Boubaker ,&nbsp;C. Laflamme ,&nbsp;J. Lemieux ,&nbsp;H. Nabi","doi":"10.1016/j.esmorw.2025.100632","DOIUrl":"10.1016/j.esmorw.2025.100632","url":null,"abstract":"<div><h3>Background</h3><div>Breast cancer (BC) is the most commonly diagnosed cancer among women. Understanding long-term trends in modifiable risk factors at the time of diagnosis is essential to inform and evaluate the effectiveness of BC prevention strategies. This study assessed 35-year trends in the prevalence of two modifiable risk factors—obesity and smoking—among BC women.</div></div><div><h3>Methods</h3><div>We conducted a retrospective cohort study including 14 595 women diagnosed with BC at the Centre des maladies du sein in Quebec City, Canada, between 1987 and 2021. Prevalence ratios (PRs) for obesity and smoking were estimated using multivariable log-binomial regression models adjusted for demographic and clinical characteristics.</div></div><div><h3>Results</h3><div>Over the 35-year period, obesity prevalence at diagnosis significantly increased [PR 1.19; 95% confidence interval (CI) 1.15-1.24; <em>P</em> &lt; 0.0001], while smoking prevalence declined (PR 0.92; 95% CI 0.89-0.95; <em>P</em> &lt; 0.0001). Stratified analyses showed more pronounced increase in obesity (PR 1.40; 95% CI 1.25-1.57; <em>P</em> &lt; 0.0001) and decline in smoking (PR 0.82; 95% CI 0.78-0.86; <em>P</em> &lt; 0.0001) among premenopausal women, compared with postmenopausal women (obesity: PR 1.16; 95% CI 1.11-1.21; <em>P</em> &lt; 0.0001; smoking: PR 0.97; 95% CI 0.93-1.01; <em>P</em> = 0.1555).</div></div><div><h3>Conclusions</h3><div>The rising prevalence of obesity among BC patients at diagnosis underscores the need to strengthen public health efforts targeting obesity prevention. While anti-smoking measures appear to have been effective, additional strategies are warranted to address the growing burden of obesity and its potential impact on BC incidence in Canada and elsewhere.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"10 ","pages":"Article 100632"},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571522","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}