JCO Clinical Cancer Informatics最新文献

{"title":"Validation of a Composite Real-World Mortality Variable Among Patients With Hematologic Malignancies Treated in the United States.","authors":"Sharlene Dong, Ankit Kansagra, Gurbakhash Kaur, Anna Barcellos, Andrew J Belli, Laura L Fernandes, Eric Hansen, Jacob Ambrose, Claire Bai, Christina M Zettler, Ming He, Ching-Kun Wang","doi":"10.1200/CCI-24-00233","DOIUrl":"https://doi.org/10.1200/CCI-24-00233","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate survival data are critical for high-quality outcomes research. It has been documented that mortality data capture in the real-world setting may be prone to missingness. Our study sought to evaluate the validity of a composite real-world mortality variable compared with the National Death Index (NDI) as the gold standard.</p><p><strong>Methods: </strong>This was a retrospective, observational research study of patients with hematologic malignancies treated in the United States. Adult patients diagnosed with one of the following cancers between January 1, 2012, and December 31, 2020, were included: AML, chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, marginal zone lymphoma, multiple myeloma, and myelodysplastic syndrome. Validation metrics (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) and date concordance (exact, ±7, 15, and 30 days) were assessed.</p><p><strong>Results: </strong>The final study population included N = 21,565 patients across seven cancer types. Validation metrics showed high sensitivity (87.8%), specificity (95.7%), PPV (90.9%), and NPV (94.1%) when comparing the composite real-world mortality variable with the NDI. Exact date concordance was observed in 88.0% of patients, and concordance rates for 7-, 15-, and 30-day intervals were 93.1%, 93.8%, and 94.3%, respectively.</p><p><strong>Conclusion: </strong>Our study found that a composite mortality variable leveraging multiple data sources yields high validity when compared against the gold-standard NDI. Given evidence highlighting the challenges of mortality data documentation in the real-world setting, the use of a composite mortality variable can provide significant benefits in quality of documentation and research results.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400233"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057465","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-Reported Outcomes Program at Scale at a Cancer Center.","authors":"Fernanda C G Polubriaginof, Allison Lipitz-Snyderman, Susan Chimonas, Gilad J Kuperman, Peter D Stetson","doi":"10.1200/CCI-24-00253","DOIUrl":"https://doi.org/10.1200/CCI-24-00253","url":null,"abstract":"<p><strong>Purpose: </strong>Incorporating patient-reported outcomes (PROs) into health care processes can improve engagement with patients; however, adopting PROs at scale is challenging. The aim of this study was to describe the design, development, and adoption at scale of a comprehensive PRO program for standard of care and research at a cancer center.</p><p><strong>Methods: </strong>Requirements for a PRO program were obtained from multiple stakeholders. Components of the program included a governance process to assure a consistent and satisfactory experience for patients completing PRO questionnaires, tools to create and manage questionnaires and related content, methods to send questionnaires to relevant patients at the appropriate time, interactive tools for patients to complete the questionnaires as part of their portal experience, and integration of PRO data into workflows for clinicians. We used descriptive statistics to assess the use of the program from 2016 to 2023.</p><p><strong>Results: </strong>From program launch (on February 1, 2016) until December 31, 2023, 189 unique questionnaires were developed (101 for standard-of-care, 70 for research, and 18 for quality improvement). Of the 432,497 unique patients who were assigned at least one questionnaire, 314,685 (73%) completed at least one. Of 5,948,464 questionnaires sent, 3,098,574 (52%) were completed. The median completion time was 2 minutes.</p><p><strong>Conclusion: </strong>Large-scale adoption of PROs at a cancer center is feasible. Key considerations for success include governance processes, attention to patient experience and clinician workflow, and the ability to manage complex inclusion criteria and timing of delivery of questionnaires. These principles should be disseminated so the full potential of PROs in health care can be realized.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400253"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049277","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":"Development and Evaluation of an Electronic Health Record-Derived Computable Phenotype to Identify Patients Undergoing Prostate Cancer Screening.","authors":"Patrick Lewicki, Yasmin Benhalim, Joshua Bradin, Kim Dryden, Husain Hakim, Benjamin Heasman, Ana Taylor, Jawad Aqeel, Anuush Vejalla, Marisa Conte, Rachel Richesson, Kristian Stensland","doi":"10.1200/CCI-24-00261","DOIUrl":"https://doi.org/10.1200/CCI-24-00261","url":null,"abstract":"<p><strong>Purpose: </strong>Given challenges with randomized trials, tumor registries, and insurance claims, electronic health record data are an appealing resource for studying prostate-specific antigen (PSA) screening for prostate cancer. Transparent, well-evaluated computable phenotypes that observe a stringent definition of screening (<i>v</i> for-cause diagnosis- or symptom-directed testing) are critical for reproducibility and comparison with prospective cohorts.</p><p><strong>Methods: </strong>A cohort of patients who underwent PSA testing in a primary care setting at a large, tertiary health care system was identified. Gold-standard labels for screening versus not screening were created via a combination of clinical note text review and exclusionary diagnosis codes. Ten computable phenotype definitions were created by urology content experts and then evaluated for sensitivity, specificity, and positive predictive value (PPV) and negative predictive value against gold-standard labels.</p><p><strong>Results: </strong>Three hundred fifty-five patients with gold-standard labels were included in the final study cohort. Varying by how missing text data were classified (not applicable <i>v</i> screening), 149 (50.3%) and 208 (58.6%) patients underwent screening. No single phenotype optimized both sensitivity and PPV, although a composite definition that included either (1) absence of symptoms or (2) presence of an encounter for screening code achieved a very high PPV of 0.99 (95% CI, 0.96 to 1.00) with a reasonable sensitivity of 0.82 (95% CI, 0.75 to 0.88).</p><p><strong>Conclusion: </strong>We identify code-based PSA screening phenotypes with a range of performance characteristics. Prevalence of for-cause diagnosis- and symptom-directed testing are significant and may contaminate cohorts not taking related codes into account.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400261"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028106","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":"Preferences of Pediatric Oncology Patients and Caregivers on the Availability of Patient Results in an Online Patient Portal.","authors":"Aryan Patel, Ian Kawpeng, Renee Potashner, Karim Jessa, Adam P Yan","doi":"10.1200/CCI-24-00235","DOIUrl":"https://doi.org/10.1200/CCI-24-00235","url":null,"abstract":"<p><strong>Purpose: </strong>Access to cancer-related data in online patient portals is not uniform. Perspectives of pediatric patients with cancer and caregivers on their desires and experiences accessing cancer-related data via an online patient portal have been poorly described. These perspectives are crucial for informing both hospital-level policies and governmental regulations. This study aims to explore the preferences of pediatric oncology patients and their caregivers regarding the timing of medical test result release into online portals.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted at a tertiary academic pediatric center in Toronto, Canada. English-speaking pediatric patients with cancer age 12 years and older, as well as their caregivers, were invited to participate. A 59-question survey was administered to participants between June and August 2024.</p><p><strong>Results: </strong>A total of 105 participants, including 40 patients and 65 caregivers, completed the survey. Forty-one (53.9%) participants reported that a health care provider had discussed with them the possibility that they might be viewing test results online before their care team had reviewed the result. Immediate release of test results was preferred across most testing domains, with >80% of participants favoring immediate access, even for sensitive oncology-related results. Less than 1% of participants believed that genetic or cancer recurrence results should be withheld until reviewed by an oncology provider. No participants reported increased worry as a result of viewing test results online.</p><p><strong>Conclusion: </strong>This study reveals a strong preference among pediatric oncology patients and their caregivers for immediate access to test results, challenging traditional concerns about the psychological impact of early release. These findings suggest that oncology practices should consider aligning their policies with patient and caregiver preferences.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400235"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048545","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 Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care.","authors":"Richard M Brohet, Elianne C S de Boer, Joram M Mossink, Joni J N van der Eerden, Alexander Oostmeyer, Luuk H W Idzerda, Jan Gerard Maring, Gabriel M R M Paardekooper, Michel Beld, Fiona Lijffijt, Joep Dille, Jan Willem B de Groot","doi":"10.1200/CCI-24-00181","DOIUrl":"10.1200/CCI-24-00181","url":null,"abstract":"<p><strong>Purpose: </strong>The use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite the significant success of targeted therapy and immunotherapy in advanced melanoma, substantial variability in clinical responses to these treatments emphasizes the need for personalized approaches to therapy.</p><p><strong>Materials and methods: </strong>In this pilot study, 239 patients with melanoma were included to predict the response to both targeted therapies and immunotherapies. We used machine learning (ML) to incorporate RWD and applied explainable artificial intelligence (XAI) to explain the individual predictions.</p><p><strong>Results: </strong>We developed, validated, and compared four ML models to evaluate 2-year survival using RWD. Our research showed encouraging outcomes, achieving an AUC of more than 80% and an estimated accuracy of over 74% across the four ML models. The random forest model exhibited the highest performance in predicting 2-year survival with an AUC of 0.85. Local interpretable model-agnostic explanations was used to explain individual predictions and provide trust and insights into the clinical implications of the ML model.</p><p><strong>Conclusion: </strong>With this proof-of-concept, we integrated RWD into predictive modeling using ML techniques to predict clinical outcomes and explore their potential implications for clinical decision making. The potential of XAI was demonstrated to enhance trust and improve the usability of the model in clinical settings. Further research, including foundation modeling and generative AI, will likely increase the predictive power of prognostic and predictive ML models in advanced melanoma.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400181"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784496","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":"Implementation of Recovery Tracker: A Postdischarge Electronic Remote Symptom-Monitoring Survey Tool After Major Urologic Oncology Surgeries.","authors":"Richard S Matulewicz, Fady Baky, Samuel Gold, Viranda H Jayalath, Rebecca Yu, Nicole Liso, Amy L Tin, Melissa Assel, Andrew J Vickers, Michael Hannon, Sigrid V Carlsson, Jennifer R Cracchiolo, Alvin C Goh","doi":"10.1200/CCI-24-00328","DOIUrl":"https://doi.org/10.1200/CCI-24-00328","url":null,"abstract":"<p><strong>Purpose: </strong>Remote symptom monitoring shows promise in promoting postdischarge contact between patients and clinicians. Unique strategies may be needed to tailor reach and engagement to specific patient populations. We aimed to assess implementation and effectiveness outcomes of a patient-reported symptom assessment tool (Recovery Tracker [RT]) after major urologic operations.</p><p><strong>Materials and methods: </strong>Patients undergoing one of four procedures (2016-2022) at a metropolitan cancer center-radical prostatectomy, nephrectomy, radical cystectomy, and retroperitoneal lymph node dissection for testicular cancer-were included in the study. Electronic delivery of RT was embedded in routine perioperative patient care. Outcomes were assessed according to the reach, effectiveness, adoption, implementation, and maintenance framework. Descriptive statistics was reported for reach, effectiveness, and adoption; a linear mixed-effects model for implementation; and a general additive model and fixed-effects meta-analysis for maintenance.</p><p><strong>Results: </strong>The cohort comprised 8,934 patients. Reach, defined as patients correctly receiving the survey, was 98% overall, with 81% (95% CI, 80 to 82) of patients completing at least one survey and the majority completing >7. The median time to completion was <2 minutes and improved as patients completed more surveys (<i>P</i> < .001), with slight variation among procedure types. The survey was effective, initiating patient-clinician contact when alert thresholds were triggered, with a marginal increase in the need for clinician office phone calls. Patient engagement with RT was maintained over several years, with a slight improvement after the addition of e-mail reminders (between 3% and 8%).</p><p><strong>Conclusion: </strong>Implementing a daily electronic survey after hospital discharge after major urologic surgeries is feasible and used often by patients.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400328"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053307","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":"Challenges in Automating Extraction of Real-World Radiographic Images and Adverse Events: Lessons From the ICAREdata Initiative.","authors":"Steven Piantadosi, Nancy Campbell, Selina Chow, Cassandra Elrahi, Michael V Knopp, Vaibhav Kumar, Catherine C Lerro, Donna R Rivera, Paul G Kluetz, Andre Quina, Michelle Casagni, Nareesa Mohammed-Rajput, Amye Tevaarwerk, Suzanne George","doi":"10.1200/CCI-24-00319","DOIUrl":"https://doi.org/10.1200/CCI-24-00319","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400319"},"PeriodicalIF":3.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065287","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":"Risk Stratification for Sentinel Lymph Node Positivity in Older Women With Early-Stage Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2 Neu-Negative Invasive Breast Cancer.","authors":"Ton Wang, Drew Neish, Samantha M Thomas, Astrid Botty van den Bruele, Laura H Rosenberger, Akiko Chiba, Kendra J Modell Parrish, Maggie L DiNome, Lesly A Dossett, Charles D Scales, Leah L Zullig, E Shelley Hwang, Jennifer K Plichta","doi":"10.1200/CCI-24-00186","DOIUrl":"10.1200/CCI-24-00186","url":null,"abstract":"<p><strong>Purpose: </strong>Guidelines recommend omission of sentinel lymph node biopsy (SLNB) for axillary staging in select patients age 70 years and older with early-stage estrogen receptor-positive (ER+), human epidermal growth factor receptor 2 neu-negative (HER2-) invasive breast cancers (BCs). However, many women meeting criteria for SLNB omission continue to receive this procedure. This study aims to stratify patients into risk cohorts for nodal positivity that can be incorporated into deimplementation strategies to reduce low-value SLNB procedures.</p><p><strong>Methods: </strong>A retrospective cohort analysis using the National Cancer Database was performed on patients age 70 years and older with ER+/HER2-, cT1-2, cN0, cM0 BC who underwent breast surgery from 2018 to 2021. Patients who received neoadjuvant therapies were excluded. Recursive partitioning analysis (RPA) was used to develop two models to estimate nodal positivity: (1) a clinical model for preoperative use to decide whether to perform SLNB and (2) a pathologic model for postoperative use to guide adjuvant decisions in cases of SLNB omission.</p><p><strong>Results: </strong>The study included 68,867 patients who received SLNB; 13.4% had a tumor-involved lymph node. RPA on the basis of clinical covariates demonstrated <8% risk of nodal positivity for patients with cT1mi-cT1b and grade 1-2 tumors. RPA on the basis of pathologic covariates found <10% risk of nodal positivity for patients with pT1 tumors without lymphovascular invasion (LVI). Patients with cT2 or pT2 without LVI and nonductal/nonlobular histology had <5% risk of nodal positivity.</p><p><strong>Conclusion: </strong>This study demonstrates a low risk of nodal positivity for patients with cT1 or pT1 tumors. Our RPA-defined subgroups offer a novel approach to predict nodal positivity in patients age 70 years and older with early-stage, ER+/HER2- invasive BC that can be incorporated in deimplementation strategies to reduce low-value axillary surgery.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400186"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732994","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: Real-World Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With First-Line Nivolumab Plus Ipilimumab in the United States.","authors":"Gurjyot K Doshi, Andrew J Osterland, Ping Shi, Annette Yim, Viviana Del Tejo, Sarah B Guttenplan, Samantha Eiffert, Xin Yin, Lisa Rosenblatt, Paul R Conkling","doi":"10.1200/CCI-25-00026","DOIUrl":"https://doi.org/10.1200/CCI-25-00026","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500026"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558573","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":"Novel Computational Pipeline Enables Reliable Diagnosis of Inverted Urothelial Papilloma and Distinguishes It From Urothelial Carcinoma.","authors":"Wei Shao, Michael Cheng, Antonio Lopez-Beltran, Adeboye O Osunkoya, Jie Zhang, Liang Cheng, Kun Huang","doi":"10.1200/CCI.24.00059","DOIUrl":"10.1200/CCI.24.00059","url":null,"abstract":"<p><strong>Purpose: </strong>With the aid of ever-increasing computing resources, many deep learning algorithms have been proposed to aid in diagnostic workup for clinicians. However, existing studies usually selected informative patches from whole-slide images for the training of the deep learning model, requiring labor-intensive labeling efforts. This work aimed to improve diagnostic accuracy through the statistic features extracted from hematoxylin and eosin-stained slides.</p><p><strong>Methods: </strong>We designed a computational pipeline for the diagnosis of inverted urothelial papilloma (IUP) of the bladder from its cancer mimics using statistical features automatically extracted from whole-slide images. Whole-slide images from 225 cases of common and uncommon urothelial lesions (64 IUPs; 69 inverted urothelial carcinomas [UCInvs], and 92 low-grade urothelial carcinoma [UCLG]) were analyzed.</p><p><strong>Results: </strong>We identified 68 image features in total that were significantly different between IUP and UCInv and 42 image features significantly different between IUP and UCLG. Our method integrated multiple types of image features and achieved high AUCs (the AUCs) of 0.913 and 0.920 for classifying IUP from UCInv and conventional UC, respectively. Moreover, we constructed an ensemble classifier to test the prediction accuracy of IUP from an external validation cohort, which provided a new workflow to diagnose rare cancer subtypes and test the models with limited validation samples.</p><p><strong>Conclusion: </strong>Our data suggest that the proposed computational pipeline can robustly and accurately capture histopathologic differences between IUP and other UC subtypes. The proposed workflow and related findings have the potential to expand the clinician's armamentarium for accurate diagnosis of urothelial malignancies and other rare tumors.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400059"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626792","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}