JCO Clinical Cancer Informatics最新文献

{"title":"Early Circulating Tumor DNA Kinetics as a Dynamic Biomarker of Cancer Treatment Response.","authors":"Aaron Li, Emil Lou, Kevin Leder, Jasmine Foo","doi":"10.1200/CCI-24-00160","DOIUrl":"10.1200/CCI-24-00160","url":null,"abstract":"<p><strong>Purpose: </strong>Circulating tumor DNA (ctDNA) assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation-which we hypothesize to hold valuable prognostic information on longer-term treatment response.</p><p><strong>Methods: </strong>We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes and use them to simulate randomized virtual patient cohorts to test candidate biomarkers.</p><p><strong>Results: </strong>Using this approach, we propose specific biomarkers, on the basis of ctDNA longitudinal features, for targeted therapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort data set.</p><p><strong>Conclusion: </strong>This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biologic mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment and generates hypotheses for further clinical testing.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400160"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576049","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":"Advancing AI for Predicting Lung Cancer-Related Immunotherapy Complications.","authors":"Hossam A Zaki, Aaron W P Maxwell, Zhicheng Jiao","doi":"10.1200/CCI-25-00037","DOIUrl":"https://doi.org/10.1200/CCI-25-00037","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500037"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736249","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":"Computational Modeling for Circulating Cell-Free DNA in Clinical Oncology.","authors":"Linh Nguyen Phuong, Sébastien Salas, Sébastien Benzekry","doi":"10.1200/CCI-24-00224","DOIUrl":"https://doi.org/10.1200/CCI-24-00224","url":null,"abstract":"<p><strong>Purpose: </strong>Liquid biopsy, specifically circulating cell-free DNA (cfDNA), has emerged as a powerful tool for cancer early diagnosis, prognosis, and treatment monitoring over a wide range of cancer types. Computational modeling (CM) of cfDNA data is essential to harness its full potential for real-time, noninvasive insights into tumor biology, enhancing clinical decision making.</p><p><strong>Design: </strong>This work reviews CM-cfDNA methods applied to clinical oncology, emphasizing both machine learning (ML) techniques and mechanistic approaches. The latter integrate biological principles, enabling a deeper understanding of cfDNA dynamics and its relationship with tumor evolution.</p><p><strong>Results: </strong>Key findings highlight the effectiveness of CM-cfDNA approaches in improving diagnostic accuracy, identifying prognostic markers, and predicting therapeutic outcomes. ML models integrating cfDNA concentration, fragmentation patterns, and mutation detection achieve high sensitivity and specificity for early cancer detection. Mechanistic models describe cfDNA kinetics, linking them to tumor growth and response to treatment, for example, immune checkpoint inhibitors. Longitudinal data and advanced statistical constructs further refine these models for quantification of interindividual and intraindividual variability.</p><p><strong>Conclusion: </strong>CM-cfDNA represents a pivotal advancement in precision oncology. It bridges the gap between extensive cfDNA data and actionable clinical insights, supporting its integration into routine cancer care. Future efforts should focus on standardizing protocols, validating models across populations, and exploring hybrid approaches combining ML with mechanistic modeling to improve biological understanding.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400224"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527977","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 a Longformer Large Language Model for Segmenting Unstructured Cancer Pathology Reports.","authors":"Damien Fung, Gregory Arbour, Krisha Malik, Kaitlin Muzio, Raymond Ng","doi":"10.1200/CCI-24-00143","DOIUrl":"https://doi.org/10.1200/CCI-24-00143","url":null,"abstract":"<p><strong>Purpose: </strong>Many Natural Language Processing (NLP) methods achieve greater performance when the input text is preprocessed to remove extraneous or unnecessary text. A technique known as text segmentation can facilitate this step by isolating key sections from a document. Give that transformer models-such as Bidirectional Encoder Representations from Transformers (BERT)-have demonstrated state-of-the-art performance on many NLP tasks, it is desirable to leverage such models for segmentation. However, transformer models are typically limited to only 512 input tokens and are not well suited for lengthy documents such as cancer pathology reports. The Longformer is a modified transformer model designed to intake longer documents while retaining the positive characteristics of standard transformers. This study presents a Longformer model fine-tuned for cancer pathology report segmentation.</p><p><strong>Methods: </strong>We fine-tuned a Longformer Question-Answer (QA) model on 504 manually annotated pathology reports to isolate sections such as diagnosis, addenda, and clinical history. We compared baseline methods including regular expressions (regex) and BERT QA. However, those methods may fail to correctly identify section boundaries. Model performance was evaluated using sequence recall, precision, and F1 score.</p><p><strong>Results: </strong>Final test results were obtained on a hold-out test set of 304 cancer pathology reports. We report sequence F1 scores for the following sections: diagnosis (0.77), addenda (0.48), clinical history (0.89), and overall (0.68).</p><p><strong>Conclusion: </strong>We present a fine-tuned Longformer model to isolate key sections from cancer pathology reports for downstream analyses. Our model performs segmentation with greater accuracy.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400143"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558575","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":"Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.","authors":"Takafumi Iguchi, Kensuke Kojima, Daiki Hayashi, Toshiteru Tokunaga, Kyoichi Okishio, Hyungeun Yoon","doi":"10.1200/CCI-24-00194","DOIUrl":"10.1200/CCI-24-00194","url":null,"abstract":"<p><strong>Purpose: </strong>To comprehensively analyze the association between preoperative maximum standardized uptake value (SUV_max) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography and postoperative recurrence in resected non-small cell lung cancer (NSCLC) using machine learning (ML) and statistical approaches.</p><p><strong>Patients and methods: </strong>This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest model were developed to predict postoperative recurrence. Model performance was evaluated using the receiver operating characteristic (ROC) AUC and concordance index (C-index). Shapley additive explanations (SHAP) and partial dependence plots (PDPs) were used to interpret model predictions and quantify feature importance. The relationship between SUV_max and recurrence risk was evaluated by using a multivariable Cox proportional hazards model.</p><p><strong>Results: </strong>The random forest model showed the highest predictive performance (ROC AUC, 0.90; 95% CI, 0.86 to 0.97). The SHAP analysis identified SUV_max as an important predictor. The PDP analysis showed a nonlinear relationship between SUV_max and recurrence risk, with a sharp increase at SUV_max 2-5. The random survival forest model achieved a C-index of 0.82. A permutation importance analysis identified SUV_max as the most important feature. In the Cox model, increased SUV_max was associated with a higher risk of recurrence (adjusted hazard ratio, 1.03 [95% CI, 1.00 to 1.06]).</p><p><strong>Conclusion: </strong>Preoperative SUV_max showed significant predictive value for postoperative recurrence after NSCLC resection. The nonlinear relationship between SUV_max and recurrence risk, with a sharp increase at relatively low SUV_max values, suggests its potential as a sensitive biomarker for early identification of high-risk patients. This may contribute to more precise assessments of the risk of recurrence and personalized treatment strategies for NSCLC.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400194"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143568795","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":"Leveraging Digital Technology to Enhance Mind-Body Approaches in Cancer Treatment.","authors":"Pierre Heudel, Céline Ubelmann","doi":"10.1200/CCI-24-00293","DOIUrl":"https://doi.org/10.1200/CCI-24-00293","url":null,"abstract":"<p><strong>Purpose: </strong>Cancer treatment involves significant psychological and emotional challenges. Conventional therapies often diminish quality of life, exacerbating stress, anxiety, and depression. Mind-body practices, such as sophrology, offer complementary solutions to improve well-being. Digital technology has expanded access to these practices, providing personalized tools to manage stress and emotional health remotely.</p><p><strong>Materials and methods: </strong>This review examines digital applications in cancer care, focusing on sophrology and other mind-body techniques. Studies evaluating the efficacy of digital platforms and artificial intelligence-driven interventions for stress management, fatigue reduction, and psychological support are analyzed, highlighting their impact on patient outcomes.</p><p><strong>Results: </strong>Digital platforms integrating sophrology significantly alleviate cancer-related side effects. The ePAL app reduced pain scores by 30% over 8 weeks, and StressProffen improved fatigue scores by 20% and adherence to stress management (75% <i>v</i> 50% in controls). The PINK! app increased physical activity by 35% and reduced psychological distress by 30% over 12 weeks. During the COVID-19 pandemic, internet-based mindfulness programs reduced anxiety by 18% and depression by 22%. These tools enhance autonomy and promote community support through virtual sessions, reducing isolation.</p><p><strong>Conclusion: </strong>Digital technology complements traditional cancer treatments, improving patient access, personalization, and adherence. Challenges such as digital literacy, data privacy, and regulatory oversight must be addressed. These tools provide holistic support and foster resilience, enhancing the cancer care continuum.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400293"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701518","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":"Impact of Tumor Location on Predicting Early-Stage Breast Cancer Patient Survivability Using Explainable Machine Learning Models.","authors":"Nader Abdalnabi, Abdulmateen Adebiyi, Ahmad Alhonainy, Kushal Naha, Christos Papageorgiou, Praveen Rao","doi":"10.1200/CCI-24-00178","DOIUrl":"https://doi.org/10.1200/CCI-24-00178","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to investigate the impact of tumor quadrant location on the 5-year early-stage breast cancer survivability prediction using explainable machine learning (ML) models. By integrating these predictive models with Shapley Additive Explanations (SHAP), feature importance, and coefficient effect size, we aim to provide insights into the significant factors influencing patient outcomes.</p><p><strong>Methods: </strong>Data from 401 early-stage patients with breast cancer at the University of Missouri's Ellis Fischel Cancer Center were used, encompassing 20 variables related to demographics, tumor characteristics, and therapeutics. Six ML models, namely, Xtreme Gradient Boosting, Random Forest classifier, Logistic Regression, Decision Tree classifier (DT), Support Vector Machine classifier, and AdaBoost (ADB), were trained and evaluated using various performance metrics, including accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR). Feature importance, coefficient effect size, and SHAP values were used to interpret and visualize the importance of different features, particularly focusing on tumor quadrant variables.</p><p><strong>Results: </strong>The extreme gradient boosting model outperformed other models, achieving an AUC-ROC score of 0.98 and an AUC-PR score of 0.97. The analysis revealed that tumor quadrant variables, especially the upper outer and miscellaneous or overlapping sites, were among the top predictive features for breast cancer survivability. SHAP analysis further highlighted the significance of these tumor locations in influencing survival outcomes.</p><p><strong>Conclusion: </strong>This study demonstrates the efficacy of explainable ML models in predicting 5-year early-stage breast cancer survivability and identifies tumor quadrant location as an independent prognostic factor. The use of SHAP values provides a clear interpretation of the model's predictions, offering valuable insights for clinicians to refine treatment protocols and improve patient outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400178"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755815","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":"Predictive Model of Acute Rectal Toxicity in Prostate Cancer Treated With Radiotherapy.","authors":"Keyur D Shah, Beow Y Yeap, Hoyeon Lee, Zainab O Soetan, Maryam Moteabbed, Stacey Muise, Jessica Cowan, Kyla Remillard, Brenda Silvia, Nancy P Mendenhall, Edward Soffen, Mark V Mishra, Sophia C Kamran, David T Miyamoto, Harald Paganetti, Jason A Efstathiou, Ibrahim Chamseddine","doi":"10.1200/CCI-24-00252","DOIUrl":"10.1200/CCI-24-00252","url":null,"abstract":"<p><strong>Purpose: </strong>To aid personalized treatment selection, we developed a predictive model for acute rectal toxicity in patients with prostate cancer undergoing radiotherapy with photons and protons.</p><p><strong>Materials and methods: </strong>We analyzed a prospective multi-institutional cohort of 278 patients treated from 2012 to 2023 across 10 centers. Dosimetric and nondosimetric variables were collected, and key predictors were identified using purposeful feature selection. The cohort was split into discovery (n = 227) and validation (n = 51) data sets. The dose along the rectum surface was transformed into a two-dimensional surface, and dose-area histograms (DAHs) were quantified. A convolutional neural network (CNN) was developed to extract dosimetric features from the DAH and integrate them with nondosimetric predictors. Model performance was benchmarked against logistic regression (LR) using the AUC.</p><p><strong>Results: </strong>Key predictors included rectum length, race, age, and hydrogel spacer use. The CNN model demonstrated stability in the discovery data set (AUC = 0.81 ± 0.11) and outperformed LR in the validation data set (AUC = 0.81 <i>v</i> 0.54). Separate analysis of photon and proton subsets yielded consistent AUCs of 0.7 and 0.92, respectively. In the photon high-risk group, the model achieved 83% sensitivity, and in proton subsets, it achieved 100% sensitivity and specificity, indicating the potential to be used for treatment selection in these patients.</p><p><strong>Conclusion: </strong>Our novel approach effectively predicts rectal toxicity across photon and proton subsets, demonstrating the utility of integrating dosimetric and nondosimetric features. The model's strong performance across modalities suggests potential for guiding treatment decisions, warranting prospective validation.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400252"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665355","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":"Methodologic Approach to Defining Comorbidities in a Cohort of Patients With Cancer: An Example in the Optimal Breast Cancer Chemotherapy Dosing Study.","authors":"Peng Wang, Kelli O'Connell, Jenna Bhimani, Victoria Blinder, Rachael Burganowski, Isaac J Ergas, 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 Tabatabai, Emily Valice, Elisa V Bandera, Lawrence H Kushi, Erin J Aiello Bowles, Elizabeth D Kantor","doi":"10.1200/CCI-24-00231","DOIUrl":"10.1200/CCI-24-00231","url":null,"abstract":"<p><strong>Purpose: </strong>We evaluated the definitions of five comorbidities (renal and hepatic impairments, anemia, neutropenia, and thrombocytopenia) in women with breast cancer using data from electronic health records.</p><p><strong>Methods: </strong>In 11,097 women receiving adjuvant chemotherapy for stage I-IIIA breast cancer at Kaiser Permanente Northern California or Kaiser Permanente Washington, we assessed comorbidity definitions in two commonly used lookback windows (1 year before diagnosis, T1; and extending until chemotherapy initiation, T1-2). Within each, we assessed data availability and agreement between International Classification of Diseases (ICD)-defined and lab-defined comorbidities of varying severity. To assess how different pieces of information may affect providers in making treatment decisions, we used multivariable logistic regression to evaluate four-category (with comorbidity indicated by both ICD and lab, ICD-only, lab-only, or neither) and collapsed binary (comorbidity indicated by either ICD or lab <i>v</i> neither) definitions in relation to first cycle chemotherapy dose reduction (FCDR).</p><p><strong>Results: </strong>Extending the lookback period to chemotherapy initiation increased laboratory data availability (missingness ≤4.1% in T1-2 <i>v</i> >40% in T1). Assessment of agreement guided selection of laboratory cutpoints. In both time periods, the four-category and binary comorbidity variables were associated with use of FCDR, but binary variables had larger cell sizes and more stability of regression models. Ultimately, the comorbidity variables in T1 were defined by a combination of either ICD/qualifying laboratory values. Results were similar in T1-2, except laboratory data alone outperformed the combination variable for renal and hepatic comorbidity.</p><p><strong>Conclusion: </strong>Leveraging both ICD and lab data and extending the lookback period to include postdiagnosis but prechemotherapy initiation may provide better capture of comorbidity. Future studies may consider validating our findings with a gold-standard comorbidity status and in other community health care settings.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400231"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143417054","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":"Erratum: Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.","authors":"Tamara P Miller, Kelly D Getz, Edward Krause, Yun Gun Jo, Sandhya Charapala, M Monica Gramatges, Karen Rabin, Michael E Scheurer, Jennifer J Wilkes, Brian T Fisher, Richard Aplenc","doi":"10.1200/CCI-25-00013","DOIUrl":"https://doi.org/10.1200/CCI-25-00013","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500013"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442954","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}