JCO Clinical Cancer Informatics最新文献

{"title":"eHealth Literacy Among Patients With Cancer in Central Norway.","authors":"Nina Kongshaug, Emil Aale Hægermark, Eva Hofsli, Bjørn Olav Åsvold, Arild Faxvaag","doi":"10.1200/CCI-26-00010","DOIUrl":"https://doi.org/10.1200/CCI-26-00010","url":null,"abstract":"<p><strong>Purpose: </strong>Empowering and activating patients by means of digital tools is associated with increased utilization of health care services and reduced health care costs. The aim of this study was to assess the ability patients with cancer have to use digital tools and digitally mediated health information by means of the eHealth Literacy Questionnaire (eHLQ) survey tool.</p><p><strong>Methods: </strong>This quantitative cross-sectional study included patients from three outpatient oncology departments in Central Norway between July 2023 and February 2024. Eligible participants were 18 years or older, currently or recently (<60 days) receiving chemotherapy, and Norwegian speaking. Data were collected using the eHLQ and supplementary questions on sociodemographic characteristics and patient portal use. Mean scale scores were calculated for the seven eHLQ scales. Analyses were performed in SPSS using general linear models.</p><p><strong>Results: </strong>A total of 248 patients with cancer were recruited. Mean eHealth literacy (EHL) scores assessed by using eHLQ, ranged from 2.72 to 3.14 across the seven scales (standard deviation [SD], 0.42-0.62). The highest score was observed for scale 4 <i>feel safe and in control</i> (mean 3.14, SD, 0.45). Increasing age showed the strongest association with lower EHL. No statistically significant gender differences were observed, and associations of low education and income with lower EHL were attenuated after adjustment for age.</p><p><strong>Conclusion: </strong>Our data indicate that EHL among patients with cancer does not differ substantially from what has been found in other populations in previous studies. This may indicate that having a severe disease or receiving chemotherapy does not affect overall EHL. Increasing age was the strongest factor associated with lower EHL.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 4","pages":"e2600010"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149450","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":"Comparison of an Artificial Intelligence Nutrition Platform With Dietitians in Providing Nutrition Support to Patients With Cancer.","authors":"Mélanie Guirette, Marissa L Buchan, Ethan M Basch, Nancy Buderer, Keshav P Goel, Susan Bratton, Stacy Loeb","doi":"10.1200/CCI-25-00337","DOIUrl":"https://doi.org/10.1200/CCI-25-00337","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with cancer experience nutrition challenges, but access to registered dietitians (RDs) is limited. The purpose of this study was to determine whether a specialized Artificial Intelligence (AI) Nutrition Platform trained on curated high-quality evidence is equivalent in providing nutrition information to patients with cancer compared with oncology-credentialed RDs.</p><p><strong>Methods: </strong>Twenty nutrition queries were developed by an expert panel, encompassing common cancer types, symptoms, and food allergies/preferences. These queries were administered to the AI Platform and to four nationally recruited human dietitians. Three expert reviewers with >10 years of cancer-specific nutrition experience blindly reviewed all query responses using a modified version of the Quality Assessment of Medical AI (mQAMAI) instrument, which assigns a score between 1 and 5 for accuracy, clarity, relevance, completeness, and usefulness (total quality score between 5 and 25). The primary outcome was mQAMAI total score. Equivalence was evaluated a priori using two one-sided tests (TOST). Secondary outcomes included readability and response time.</p><p><strong>Results: </strong>The AI Nutrition Platform met equivalence criteria with human dietitians at ±3 to ±5 points (mean difference: 0.9 points; 90% CI, -0.27 to 2.15; TOST <i>P</i> < .05), while scoring higher on individual components: clarity (4.2 <i>v</i> 3.9; <i>P</i> = .001), relevance (4.1 <i>v</i> 3.9; <i>P</i> = .04), and usefulness (3.8 <i>v</i> 3.5; <i>P</i> = .02). AI responses were easier to read (Flesch-Kincaid grade level 7.2 <i>v</i> 9.8; <i>P</i> < .001) with faster response time (8.5 <i>v</i> 18.5 minutes per two queries; <i>P</i> < .001).</p><p><strong>Conclusion: </strong>The AI Platform was equivalent to human dietitians for cancer nutrition information, delivering responses with greater clarity, relevance, usefulness, and readability in less time. This tool could broaden access to nutrition support for patients in settings where access to dietitians is limited.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 4","pages":"e2500337"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197749","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":"Toward Clinically Actionable Prediction of Recurrence and Metastatic Progression in Pancreatic Ductal Adenocarcinoma: A Systematic Review.","authors":"Liyuan Gong, Cadence Chang, Alice Agyekum, Nitish Aswani, Jeong Yun Yang, Peter Shahinnian, Gulam Manji, Stella K Kang, Chin Hur","doi":"10.1200/CCI-26-00200","DOIUrl":"https://doi.org/10.1200/CCI-26-00200","url":null,"abstract":"<p><strong>Purpose: </strong>Pancreatic ductal adenocarcinoma (PDAC) has high rates of early recurrence (ER), occult metastasis, and poor survival despite curative-intent therapy. Predictive factors and models span clinical, biomarker, imaging, radiomics, pathologic, and molecular domains, but clinical readiness remains uncertain.</p><p><strong>Methods: </strong>We performed a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Five databases were searched for studies evaluating predictors or models of PDAC recurrence, metastasis, lymph node metastasis (LNM), occult metastatic disease, disease-free survival (DFS) or recurrence-free survival, or recurrence patterns. Studies were screened and coded for end point, modality, model type, validation, discrimination, calibration, clinical utility, and predictor timing.</p><p><strong>Results: </strong>Of 234 full-text records, 208 studies were included, comprising 144 prediction models and 64 predictor/prognostic factor studies. Computed tomography/radiomics (141/208), clinical/electronic health record variables (104/208), and serum/urine biomarkers (46/208) were the most common modalities. External validation, calibration, and decision-curve analyses were reported in 52, 36, and 38 model studies, respectively. AUCs ranged from 0.70 to 0.92 for ER, 0.75-0.95 for LNM, and 0.71-0.88 for liver or occult metastasis; DFS/relapse-free survival models had C-indices of 0.62-0.82. One externally validated occult-metastasis model had a C-statistic of 0.56. Model maturity was limited by retrospective design, heterogeneous end points, incomplete calibration reporting, and postoperative variables in preoperative models.</p><p><strong>Conclusion: </strong>Prediction models for PDAC recurrence and metastatic progression are rapidly expanding, particularly through radiomics, machine learning, and multimodal modeling. Clinical translation requires decision-aligned design, transparent reporting, external validation, calibration, interpretability, and prospective evaluation of clinical utility.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 4","pages":"e2600200"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185776","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":"Bridging Clinical Observational Research and Data Standards in Head and Neck Cancer Care: Scoping Review and Mapping of Clinical Data Elements.","authors":"Ivy Cerelia Valerie, Farhannah Aly, Ghazaleh Niknam Shirvan, Fahim Irfan Alam, Meredith Johnston, Benjamin Y Kong, Merran Findlay, Lois Holloway, Georgina Kennedy","doi":"10.1200/CCI-26-00177","DOIUrl":"10.1200/CCI-26-00177","url":null,"abstract":"<p><strong>Purpose: </strong>A scoping review and mapping of clinical data elements in observational studies were conducted to assess head and neck cancer (HNC) clinical data, alignment with existing observational oncology data standards, and the demonstrated opportunities for using these data standards in observational studies.</p><p><strong>Methods: </strong>The scoping review was performed to systematically find and extract data elements from real-world studies emphasizing HNC treatment patterns. Text-embedding-assisted and manual mapping was performed to map data elements to their representations in HNC Ontology, Operational Ontology for Oncology, Minimal Common Oncology Data Elements, and the Observational Medical Outcomes Partnership Common Data Model. Study characteristics, data elements and their mapping, and proportion of clinical domains represented were analyzed. Demonstrated opportunities for using standard data elements were reviewed based on the mapping results.</p><p><strong>Results: </strong>A total of 98 observational studies were included. National databases were the most common data source (59.2%), and disease trajectory outcomes were the most frequently reported study end point (95.9%). Overall, 1,613 data elements were reported and classified into five domains: diagnosis (1.2%), health system (5.7%), tumor (27.2%), person (32.7%), and treatment (33.2%). Mapping coverage varied across domains, with 85%-100% coverage in diagnosis, 83.6%-95.2% in treatment, 85.3%-97.8% in tumor, 50.7%-89% in person, and 43.8%-81.2% in health system domains. Demonstrated opportunities for applying data standardization include facilitating geographical links to personal data elements, streamlining value derivation of longitudinally tracked data elements, and deriving complex treatment constructs.</p><p><strong>Conclusion: </strong>The existing data standards and associated terminology systems covered most of the clinical observational data elements, but gaps remain in structuring complex treatment factors that may affect patient outcomes. Resulting mapping offers generalizable representations across standards, laying the groundwork for further refinement through collaborative data element curation from local conventions under the guidance of data standard experts.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 4","pages":"e2600177"},"PeriodicalIF":3.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139161","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":"Equity and Automation in Clinical Trial Screening: Risks and Responsibilities of Artificial Intelligence‑Enabled Matching and Enrollment Systems.","authors":"Kever A Lewis, Ashlee N Seldomridge, Beth A Helmink, Heather G Lyu, Rebecca A Snyder","doi":"10.1200/CCI-26-00032","DOIUrl":"https://doi.org/10.1200/CCI-26-00032","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600032"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406830","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 Symptom Burden Among Thyroid Cancer Survivors: Retrospective Cohort Study.","authors":"Jason B Liu, Thomas Szabo Yamashita, Johnny L Rollins, Soo-Hyun Lee-Kim, Khavya C Avula, Rachel Chacko, Angela Peek, Loretta A Williams, Mark E Zafereo, Anastasios Maniakas, Jennifer R Wang, Naifa L Busaidy, Maria E Cabanillas, Luz E Castellanos, Aysha Chaudhri, Ramona Dadu, Mimi I Hu, Camilo Jimenez Vasquez, Anita K Ying, Jeena M Varghese, Steven I Sherman, Steven G Waguespack, Steven P Weitzman, Paul H Graham, Nancy D Perrier, Eileen H Shinn, Michael E Roth, Mouhammed Amir Habra, Elizabeth G Grubbs, Sarah B Fisher","doi":"10.1200/CCI-26-00041","DOIUrl":"10.1200/CCI-26-00041","url":null,"abstract":"<p><strong>Purpose: </strong>Survivorship care models that extend beyond recurrence surveillance to ones that also address treatment-related symptoms are needed. Using data obtained in routine clinical care, we aimed to examine patient-reported symptom burden among adult thyroid cancer survivors.</p><p><strong>Methods: </strong>Between September 2019 and September 2022, adults were electronically administered the MDASI-Thy, a patient-reported outcome measure that measures symptom severity and interference, within 7 days before their visit at a dedicated thyroid cancer survivorship clinic. The MDASI-Thy generates (1) core symptom severity, (2) thyroid-specific symptom severity, and (3) symptom interference scores, where lower is better. High alert values (HAVs) were defined for four symptoms: Distress (Upset), Pain, Sad, and Shortness of Breath. Multivariable generalized linear models examined associations of patient, cancer, and treatment factors with scores and any HAV.</p><p><strong>Results: </strong>Among 1,557 thyroid cancer survivors, 864 (55.5%) responded. Respondents were a median of 5 years from diagnosis (IQR, 4-8) and predominantly female (79.1%), and most had papillary thyroid carcinoma (92.5%). Mean (standard deviation) scores were 1.20 (1.34) for core severity, 0.99 (1.26) for thyroid-specific severity, and 1.07 (1.80) for interference. Fatigue (11.5%) and Disturbed Sleep (11.3%) were the most common severe symptoms. HAVs occurred in 72 survivors (8.3%), of whom 54 (75%) had a documented plan addressing the HAV. Higher symptom burden was associated with female sex, Black race, greater comorbidity, active smoking, and total thyroidectomy.</p><p><strong>Conclusion: </strong>Routine patient-reported symptom screening in thyroid cancer survivorship identified generally low symptom burden but meaningful variations, with a subset reporting severe symptoms, functional interference, and HAVs requiring action.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600041"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377882","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":"Validation of a Text-Mining Tool for Extracting Routine Clinical Care Data in Early-Stage Resectable Non-Small Cell Lung Cancer.","authors":"Hanieh Abedian Kalkhoran, Tobias Martinot, Lydia H N Schonewille, Melek Huyuk, Michiel H J Huigen, Henk-Jan Guchelaar, Danielle Cohen, Egbert F Smit, Juliette Zwaveling","doi":"10.1200/CCI-25-00383","DOIUrl":"10.1200/CCI-25-00383","url":null,"abstract":"<p><strong>Purpose: </strong>Manual chart review (MR) of electronic health records (EHRs) is time-consuming, error-prone, and limits the reproducibility and scalability of real-world data (RWD) research. Automation and standardization using natural language processing (NLP) could improve efficiency and scalability. CTcue is an NLP-based software platform designed to extract structured and unstructured data from EHRs. This study evaluated the accuracy and efficiency of CTcue versus MR in patients with early-stage resectable non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>Included were all patients with stage I to III NSCLC who underwent lung resections between January 2018 and December 2021 at the Leiden University Medical Center, the Netherlands. Demographics, tumor characteristics, treatment, and outcomes were collected. CTcue performance was compared with MR using weighted F1-scores, accuracy, precision, and recall for categorical variables and Bland-Altman analysis for continuous variables.</p><p><strong>Results: </strong>Eighty-five patients (70.2% of patients from the manual cohort) were identified by both methods and included in the comparison. CTcue achieved weighted F1-scores >0.85 for seven of 15 categorical variables, including sex, tumor location, and deceased status, although some scores were based on low number of observations in both cohorts. Lower performance was observed for variables with varying terminology in documentation, such as Eastern Cooperative Oncology Group status and pathological N-stage. Continuous variables showed negligible mean differences, indicating good agreement. Survival outcomes were identical in both data sets.</p><p><strong>Conclusion: </strong>CTcue performance for patient selection was lower than anticipated. However, it enables accurate, efficient extraction of structured and unstructured EHR data in early-stage NSCLC. Manual validation remains necessary for variables with varying terminology. Further development of artificial intelligence-based tools-particularly for free-text data extraction-will be crucial to enhance the accuracy and scalability of future RWD research.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2500383"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622552","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":"Consensus-Based Minimum Requirements for the Adoption of a Computational Pathology Algorithm for Tumor Budding in Colorectal Cancer: An Early Health Technology Assessment.","authors":"Julie E M Swillens, Iris D Nagtegaal, Alessandro Lugli, Jeroen A W M Van der Laak, Marcia Tummers","doi":"10.1200/CCI-26-00034","DOIUrl":"10.1200/CCI-26-00034","url":null,"abstract":"<p><strong>Purpose: </strong>Tumor budding (TB) is an independent prognostic biomarker for colorectal cancer (CRC), yet its clinical adoption is limited by labor-intensive and subjective visual scoring. Computational pathology (CPath) algorithms using deep learning offer potential to improve efficiency and reproducibility. Using an international Delphi study, we established consensus requirements to guide validation and clinical adoption of CPath algorithms, aiming to enhance diagnostic accuracy and patient care.</p><p><strong>Methods: </strong>A two-round international Delphi process was conducted, involving international experts, to reach consensus on predefined statements. In round 1, baseline characteristics were collected and participants were asked to rank statements representing the minimal requirements for the implementation of a CPath algorithm for TB assessment. After completion of this round, participants received a personalized feedback report summarizing interim results for items that lacked consensus. In round 2, participants re-evaluated their initial responses to statements without consensus, in the light of anonymized group feedback provided in their personalized report.</p><p><strong>Results: </strong>Fifty-nine pathologists participated in round 1, with a 90% response rate in round 2. Consensus was reached for 21 of 29 (72%) minimal requirements. All reached consensus by agreement. Eight statements (28%) remained without consensus after round 2.</p><p><strong>Conclusion: </strong>Agreement was reached on the technical, organizational, ethical, and legal aspects, although some requirements were not agreed upon. This highlights the importance of evaluations that take specific contexts into account, as well as the need for continuous stakeholder engagement throughout the development and implementation phases.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600034"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13456570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649756","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":"Using Artificial Intelligence to Standardize Naming Conventions in Radiation Therapy.","authors":"Fabio Dennstädt","doi":"10.1200/CCI-26-00169","DOIUrl":"https://doi.org/10.1200/CCI-26-00169","url":null,"abstract":"","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600169"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148622456","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":"Concordance Between Claims-Based and Electronic Health Record-Based Comorbidity Measures: An Application Using Linked Data.","authors":"Eberechukwu Onukwugha, Tsung-Ying Lee, Abree Johnson, Chih Chun Tung, Eun Shim Nahm, Jessica Wimbush, Kunj Patel, Donna Rivera, Bindu Kanapuru, Catherine C Lerro, Jonathon Vallejo, Colleen Reilly, Jessica Dohler, Joanne F Dorgan","doi":"10.1200/CCI-25-00376","DOIUrl":"https://doi.org/10.1200/CCI-25-00376","url":null,"abstract":"<p><strong>Purpose: </strong>The National Cancer Institute Comorbidity Index (NCI-CI) captures patient-level comorbidity burden using diagnosis codes found in administrative claims or electronic health record (EHR) data. However, it is less well understood how claims- and EHR-based comorbidity measures compare. We determined the concordance between the claims-based and EHR-based NCI-CI using a linked data source.</p><p><strong>Materials and methods: </strong>This study used a linkage of tumor registry, EHR, and Medicare claims data. Eligible patients were 65 years and older and were diagnosed and/or received their first course of treatment in 2019-2020. We formed comparison pairs determined by the data source, period relative to the diagnosis date, length of interval for calculating the index, and type of comorbidity index. The primary pair was the 12-month prediagnosis NCI-CI calculated using claims (NCI-CI_claims) and EHR (NCI-CI_EHR) data. We compared pairs using descriptive statistics, correlation coefficients, and histograms. We also identified patient-level factors associated with claims/EHR comorbidity discordance using models of categorical outcomes.</p><p><strong>Results: </strong>The final sample included 1,616 individuals. The median values for NCI-CI_claims and NCI-CI_EHR were 0.6 (range: 0-4.11) and 0.29 (range: 0-3.06), respectively. We identified moderate correlation (0.49-0.52) between the claims-based and EHR-based comorbidity measures across all comparison pairs. Among individuals with claims-based comorbidities, 32 percent did not report any of those comorbidities based on the EHR data. Older age (eg, age 80+ years) was positively associated with the odds of having a discordant pair.</p><p><strong>Conclusion: </strong>In this study, we found moderate correlations between claims-based and EHR-based approaches to NCI comorbidity measures. Mean comorbidity index values were consistently higher using claims data potentially because of more complete capture of health care utilization and diagnoses outside of the hospital system using claims data.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2500376"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148842117","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}