JCO Clinical Cancer Informatics最新文献

{"title":"Machine Learning-Based Prediction of Clinical Outcomes in Patients With Cancer Receiving Systemic Treatment Using Step Count Data Measured With Smartphones.","authors":"Calvin G Brouwer, Branca M Bartelet, Joeri A J Douma, Leni van Doorn, Evelien J M Kuip, Henk M W Verheul, Laurien M Buffart","doi":"10.1200/CCI-25-00023","DOIUrl":"10.1200/CCI-25-00023","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate whether changes in step count, measured using patients' own smartphones, could predict a clinical adverse event in the upcoming week in patients undergoing systemic anticancer treatments using machine learning models.</p><p><strong>Methods: </strong>This prospective observational cohort study included patients with various cancer types receiving systemic anticancer treatment. Physical activity was monitored continuously using patients' own smartphones, measuring daily step count for 90 days during treatment. Clinical adverse events (ie, unplanned hospitalizations and treatment modifications) were extracted from medical records. Models predicting adverse events in the upcoming 7 days were created using physical activity data from the preceding 2 weeks. Machine learning models (elastic net [EN], random forest [RF], and neural network [NN]) were trained and validated on a 70:30 split cohort. Model performance was evaluated using the AUC.</p><p><strong>Results: </strong>Among the 76 patients analyzed (median age 61 [IQR, 53-69] years, 39 [51%] female), 11 (14%) were hospitalized during the study period. The median step count during the first week of systemic treatment was 4,303 [IQR, 1926-7,056]. Unplanned hospitalizations in the upcoming 7 days could be predicted with high accuracy using RF (AUC = 0.88), NN (AUC = 0.84), and EN (AUC = 0.83). The models could not predict treatment modifications (AUC = 0.28-0.51) or the occurrence of any clinically relevant adverse event (AUC = 0.32-0.50).</p><p><strong>Conclusion: </strong>A decline in daily step counts can serve as an early predictor for hospitalizations in the upcoming 7 days, facilitating proactive and preventive toxicity management strategies.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500023"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531079","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":"Where Privacy Meets Partnership in Adolescent Oncology Portal Use.","authors":"Molly S Talman, Nicole M Wood","doi":"10.1200/CCI-25-00155","DOIUrl":"https://doi.org/10.1200/CCI-25-00155","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500155"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585592","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":"Clinical Application of Large Language Models in Generating Pathologic Images.","authors":"Lingxuan Zhu, Yancheng Lai, Na Ta, Weiming Mou, Rodolfo Montironi, Katrina Collins, Kenneth A Iczkowski, Fei Chen, Antonio Lopez-Beltran, Rui Zhou, Huang He, Gyan Pareek, Elias Hyams, Dragan Golijanin, Sari Khaleel, Borivoj Golijanin, Kamil Malshy, Alessia Cimadamore, Xiang Ni, Tao Yang, Liang Cheng, Rui Chen","doi":"10.1200/CCI-24-00267","DOIUrl":"https://doi.org/10.1200/CCI-24-00267","url":null,"abstract":"<p><strong>Purpose: </strong>This study investigates the potential of DALL·E 3, an artificial intelligence (AI) model, to generate synthetic pathologic images of prostate cancer (PCa) at varying Gleason grades. The aim is to enhance medical education and research resources, particularly by providing diverse case studies and valuable teaching tools.</p><p><strong>Methods: </strong>This study uses DALL·E 3 to generate 30 synthetic images of PCa across various Gleason grades, guided by standard Gleason pattern descriptions. Nine uropathologists evaluated these images for realism and accuracy compared with actual hematoxylin and eosin (H&E)-stained slides using a scoring system.</p><p><strong>Results: </strong>The average realism and representativeness scores were 6.04 and 6.17, indicating satisfactory quality. Scores varied significantly among Gleason patterns (<i>P</i> < .05), with Gleason 5 images achieving the highest scores and accurately depicting critical pathologic characteristics. Limitations included a lack of fine nuclear detail, essential for identifying malignancy, which may affect the images' diagnostic utility.</p><p><strong>Conclusion: </strong>DALL·E 3 shows promise in generating customized pathologic images that can aid in education and resource expansion within pathology. However, ethical concerns, such as the potential misuse of AI-generated images for data falsification, highlight the need for responsible oversight. Collaboration between technology firms and pathologists is essential for the ethical integration of AI in pathology practices.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400267"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555617","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":"Assessing the Data Quality Dimensions of Surgical Oncology Cohorts in the <i>All of Us</i> Research Program.","authors":"Matthew Spotnitz, John Giannini, Emily Clark, Yechiam Ostchega, Tamara R Litwin, Stephanie L Goff, Lew Berman","doi":"10.1200/CCI-25-00078","DOIUrl":"10.1200/CCI-25-00078","url":null,"abstract":"<p><strong>Purpose: </strong>Cancer is a leading cause of morbidity and mortality in the United States. Mapping electronic health record (EHR) data to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) may standardize data structure and allow for multiple database oncology studies. However, the number of oncology studies produced with the OMOP CDM has been low. To investigate the discrepancy between the public health impact of cancer and the output of OMOP CDM clinical cancer studies, we evaluated (EHR) data quality of five surgical oncology cohorts in the <i>All of Us</i> Research Program: mastectomy, prostatectomy, colectomy, melanoma excision, and lung cancer resection.</p><p><strong>Methods: </strong>We selected procedure codes that were the basis of each phenotype. We used a data quality checklist to evaluate five domains systematically: conformance, completeness, concordance, plausibility, and temporality.</p><p><strong>Results: </strong>Most phenotype-defining source codes were mapped to Current Procedural Terminology 4, which is an EHR standard. All cohorts had low concept prevalence. Most bivariate correlations between concepts were weak (⍴ ≤ 0.5). The small number of biomarkers available for use limited our plausibility analysis. The median time between biopsy and surgery varied across cohorts.</p><p><strong>Conclusion: </strong>We identified multiple data completeness issues, which limited the fitness for use evaluation. Also, using the OMOP CDM procedure concepts and mappings presented challenges for our study. Variable amounts of missingness in OMOP CDM surgical oncology data may affect the fitness for use of cancer data. Further research is warranted to improve the quality of that data.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500078"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592910","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":"Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.","authors":"Magdalena Fay, Ross S Liao, Zaeem M Lone, Chandana A Reddy, Hassan Muhammad, Chensu Xie, Parag Jain, Wei Huang, Hirak S Basu, Sujit S Nair, Dimple Chakravarty, Sean R Williamson, Shilpa Gupta, Christopher Weight, Rajat Roy, George Wilding, Ashutosh K Tewari, Eric A Klein, Omar Y Mian","doi":"10.1200/CCI-24-00292","DOIUrl":"10.1200/CCI-24-00292","url":null,"abstract":"<p><strong>Purpose: </strong>Artificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluate the clinical validity of an AI-enabled prognostic test, PATHOMIQ_PRAD, using a clinical cohort from the Cleveland Clinic.</p><p><strong>Methods: </strong>We conducted a retrospective analysis of PATHOMIQ_PRAD using CaP WSIs from patients who underwent radical prostatectomy (RP) between 2009 and 2022 and did not receive adjuvant therapy. Patients also had Decipher genomic testing available. WSIs were deidentified, anonymized, and outcomes were blinded. Patients were stratified into high-risk and low-risk categories on the basis of predetermined thresholds for PATHOMIQ_PRAD scores (0.45 for biochemical recurrence [BCR] and 0.55 for distant metastasis [DM]).</p><p><strong>Results: </strong>The study included 344 patients who underwent RP with a median follow-up of 4.3 years. Both PathomIQ and Decipher scores were associated with rates of biochemical recurrence-free survival (BCRFS; PathomIQ score >0.45 <i>v</i> ≤0.45, <i>P</i> <.001; Decipher score >0.6 <i>v</i> ≤0.6, <i>P</i> = .002). There were 16 patients who had DM, and 15 were in the high-risk PathomIQ group (Mets Score >0.55). Both PathomIQ and Decipher scores were associated with rates of metastasis-free survival (PathomIQ score >0.55 <i>v</i> ≤0.55, <i>P</i> <.001; Decipher score >0.6 <i>v</i> ≤0.6, <i>P</i> = .0052). Despite the low event rates for metastasis, multivariable regression demonstrated that high PathomIQ score was significantly associated with DM (>0.55 <i>v</i> ≤0.55, hazard ratio, 10.10 [95% CI, 1.28 to 76.92], <i>P</i> = .0284).</p><p><strong>Conclusion: </strong>These findings independently validate PATHOMIQ_PRAD as a reliable predictor of clinical risk in the postprostatectomy setting. PATHOMIQ_PRAD therefore merits prospective evaluation as a risk stratification tool to select patients for adjuvant or early salvage interventions.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400292"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327640","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":"Machine-Learning Algorithms and Treatment Response in Advanced Melanoma.","authors":"Hinpetch Daungsupawong, Viroj Wiwanitkit","doi":"10.1200/CCI-25-00099","DOIUrl":"https://doi.org/10.1200/CCI-25-00099","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500099"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509320","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":"Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes.","authors":"Mario Fugal, David Marshall, Alexander V Alekseyenko, Xia Jing, Graham Warren, Jihad Obeid","doi":"10.1200/CCI-24-00268","DOIUrl":"10.1200/CCI-24-00268","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health records is a critical but challenging task. Traditional methods of identifying the primary tumor histology relying on International Classification of Diseases (ICD)9 and ICD10 CM codes often fall short in granularity and completeness, particularly for patients with metastatic cancer.</p><p><strong>Methods: </strong>In this study, we propose an approach leveraging natural language processing (NLP) algorithms to enhance the accuracy of extracting primary tumor histology from the patient's electronic records.</p><p><strong>Results: </strong>Through manual annotation of patient data and subsequent algorithm training, we achieved improvements in accuracy and efficiency in primary tumor type classification and finding histology subtypes not available in ICD10 CM.</p><p><strong>Conclusion: </strong>Our findings underscore the value of NLP in refining research processes, identifying patients' cohorts, and improving efficiencies with the goal of potentially improving patient outcomes in SRS treatment.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400268"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289743","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":"Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia.","authors":"Joseph Aoki, Omar Khalid, Cihan Kaya, Mohamed E Salama","doi":"10.1200/CCI-24-00197","DOIUrl":"10.1200/CCI-24-00197","url":null,"abstract":"<p><strong>Purpose: </strong>The diagnosis of chronic lymphocytic leukemia (CLL) is often delayed several years in advance of disease. Addressing this care gap would aid in identifying at-risk patients who may benefit from targeted evaluation to prevent adverse outcomes. To our knowledge, to date, however, there are no widely utilized machine learning (ML) models that predict development of CLL. Therefore, the objective of this study was to leverage readily available laboratory data to train and test the performance of ML-based risk models for abnormal lymphocytosis associated with CLL.</p><p><strong>Methods: </strong>The observational study population was composed of deidentified laboratory data procured from a large US outpatient network. The 7-year longitudinal data set included 1,090,707 adult patients with the following inclusion criteria: age 50 to 75 years and initial absolute lymphocyte count (ALC) <5 × 10⁹/L. The data set was split into training and held-out test sets, where 80% of the data were used in training and 20% were used for independent testing. ML models were developed using random forest survival methods. The ground truth outcome was abnormal lymphocytosis associated with CLL and monoclonal B-cell lymphocytosis diagnosis: ALC ≥5 × 10⁹/L with ≥40% relative lymphocytosis.</p><p><strong>Results: </strong>The 12-variable risk classifier model accurately predicted ALC ≥5 × 10⁹/L within 5 years and achieved an area under the curve receiver operating characteristic of 0.92. The most important predictors were ALC (initial, slope), WBC (last, max, slope, initial), platelet (last, slope, max, initial), age, and sex.</p><p><strong>Conclusion: </strong>Our ML risk classifier accurately predicts abnormal lymphocytosis associated with CLL using routine laboratory data. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition for patients at a risk of CLL.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400197"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486961","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":"Patient Perspectives on Technological Barriers and Implementation Strategies Leveraged During a Real-World Remote Symptom Monitoring Program.","authors":"Tanvi V Padalkar, Nicole L Henderson, D'Ambra N Dent, Emma Hendrix, Catherine Smith, Chao-Hui Sylvia Huang, Tara Kaufmann, Chelsea McGowan, Jennifer Young Pierce, Stacey A Ingram, Angela M Stover, Ethan M Basch, Doris Howell, Bryan J Weiner, J Nicholas Odom, Gabrielle B Rocque","doi":"10.1200/CCI-24-00232","DOIUrl":"10.1200/CCI-24-00232","url":null,"abstract":"<p><strong>Purpose: </strong>Remote symptom monitoring (RSM) using electronic patient-reported outcomes leverages digital technologies to gather real-time information on patient experiences for symptom management. This study reports a formative evaluation of technology-related barriers encountered by patients participating in RSM and implementation strategies used to address those barriers in real-world, large-scale RSM implementations.</p><p><strong>Methods: </strong>Purposive sampling was conducted to recruit patients diagnosed with cancer and participating in RSM at the University of Alabama at Birmingham and USA Health Mitchell Cancer Institute for semi-structured interviews. Interviews were coded to identify technology-related barriers using a constant comparative method. Expert Recommendations for Implementing Change list was used to address the barriers to optimize RSM implementation. Barrier-associated themes from the interviews were mapped to implementation strategies.</p><p><strong>Results: </strong>Forty participants age 24-77 years, half of whom were 60 years or older, were interviewed from December 2021 to February 2024. Three barrier themes relevant to technology utilization in RSM were identified: (1) <i>accessibility concerns</i>, (2) <i>digital health literacy</i>, and (3) <i>user interface challenges</i>. Themes were mapped to the implementation strategies as identified by the implementation team. Eight total implementation strategies were used to address these technology barriers: (1) assess for readiness and identify barriers and facilitators, (2) obtain and use patients/consumers and family/caregiver feedback, (3) involve patients/consumers and family members/caregivers, (4) access new funding, (5) change physical structure and equipment, (6) centralize technical assistance, (7) prepare patients/consumers to be active participants, and (8) intervene with patients/consumers to enhance uptake and adherence.</p><p><strong>Conclusion: </strong>Technology-related barriers may limit the uptake of RSM by patients. Addressing these barriers through multimodel assessment and intervention strategies is crucial to ensuring successful implementation of RSM in real-world settings.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400232"},"PeriodicalIF":2.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337238","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":"Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches.","authors":"Hongyu Chen, Xiaohan Li, Xing He, Aokun Chen, James McGill, Emily C Webber, Hua Xu, Mei Liu, Jiang Bian","doi":"10.1200/CCI-25-00071","DOIUrl":"10.1200/CCI-25-00071","url":null,"abstract":"<p><strong>Purpose: </strong>Patient recruitment remains a major bottleneck in clinical trial execution, with inefficient patient-trial matching often causing delays and failures. Recent advancements in large language models (LLMs) offer a promising avenue for automating and improving this process. This scoping review aims to provide a comprehensive synthesis of the emerging applications of LLMs in patient-trial matching.</p><p><strong>Methods: </strong>A comprehensive search was conducted in PubMed, Web of Science, and OpenAlex for literature published between December 1, 2022, and December 31, 2024. Studies were included if they explicitly integrated LLMs into patient-trial matching systems. Data extraction focused on system architectures, patient data processing, eligibility criteria processing, matching techniques, evaluation metrics, and performance.</p><p><strong>Results: </strong>Of the 2,357 studies initially identified, 24 met the inclusion criteria. The majority (21/24) were published in 2024, highlighting the rapid adoption of LLMs in this domain. Most systems used patient-centric matching (17/24), with OpenAI's generative pretrained transformer models being the most commonly used LLM. Core components of these systems included eligibility criteria processing, patient data processing, and matching, with some incorporating retrieval algorithms to enhance computational efficiency. LLM-integrated approaches demonstrated improved accuracy and scalability in patient-trial matching, although challenges such as performance variability, interpretability, and reliance on synthetic data sets remain significant.</p><p><strong>Conclusion: </strong>LLM-based patient-trial matching systems present a transformative opportunity to enhance the efficiency and accuracy of clinical trial recruitment. Despite current limitations related to model generalizability, explainability, and data constraints, future advancements in hybrid modeling strategies, domain-specific fine-tuning, and real-world data set integration could further optimize LLM-based trial matching. Addressing these challenges will be crucial to realizing the full potential of LLMs in streamlining patient recruitment and accelerating clinical trial execution.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500071"},"PeriodicalIF":3.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12169815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259398","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}