JCO Clinical Cancer Informatics最新文献

{"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}

{"title":"Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review.","authors":"Rachel Woodford, Sally Lord, Frank Lin, Anthony T Papenfuss, Ian Marschner, David Thomas, Michael Friedlander, Clare Scott, John Simes, Chee Khoon Lee","doi":"10.1200/CCI-26-00014","DOIUrl":"10.1200/CCI-26-00014","url":null,"abstract":"<p><p>The phased framework of oncology trials is designed to ensure patient safety and conserve resources by advancing only promising therapies from early- to late-phase testing. Despite decades of refinement, overall trial success rates-defined by the proportion of studies ultimately supporting regulatory approval-remain low, with failures increasingly occurring in late-phase studies. These failures are often contributed to by methodological shortcomings, including suboptimal end point selection, restrictive eligibility criteria, and inefficient trial designs. Although traditional approaches to biomarker discovery, outcome validation, and eligibility refinement have yielded transformational advances, increasing molecular subclassification of tumors into rare subgroups results in the conventional drug development framework being no longer fit for purpose. Artificial intelligence (AI) offers opportunities to enhance the efficiency, precision, and patient-centeredness of oncology trials. Deep learning systems integrate and analyze large data sets to uncover complex patterns often inaccessible to conventional methods. AI has potential applications in patient-trial matching, optimization of eligibility criteria, statistical modeling of survival outcomes, and the identification of novel surrogate end points although these applications remain largely investigational and are not yet established for routine use. This scoping review provides a structured overview of AI applications in oncology trials, with emphasis on outcome selection and surrogate end point evaluation. We also highlight emerging areas with potential for immediate implementation, such as patient selection, biomarker identification and synthetic control arms, to accelerate development and enhance clinical care. However, broader harmonization is needed to ensure reproducibility, transparency, and regulatory confidence before implementation. Ultimately, early and sustained collaboration between trialists, AI developers, and regulators will be essential to ensure that AI delivers meaningful advances in the design, evaluation, and delivery of new medicines.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600014"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426094","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":"Influence of Fractionation and Beam Sequencing on Absorbed Dose to Circulating Lymphocytes During Ultra-High Dose Rate and Conventional Radiotherapy: An In Silico Study.","authors":"François de Kermenguy, Camilla Satragno, Mohammed El-Aichi, Ibrahima Diallo, Cristina Veres, Fereshteh Talebi, Cathyanne Schott, Eric Deutsch, Charlotte Robert","doi":"10.1200/CCI-26-00091","DOIUrl":"10.1200/CCI-26-00091","url":null,"abstract":"<p><strong>Purpose: </strong>Several in silico models concluded that ultra-high dose rate (UHDR) radiotherapy could spare large quantities of circulating lymphocytes. However, preclinical studies failed to show a reduction in radiation-induced lymphopenia after UHDR irradiation compared with conventional dose rates (CONV). This study aims to investigate the influence of fractionation and beam sequencing on absorbed dose to circulating lymphocytes during CONV and UHDR irradiations.</p><p><strong>Materials and methods: </strong>The LymphoDose framework was applied to a cohort of 162 patients treated for brain tumors with 3D conformal radiotherapy. Four scenarios of UHDR treatment were compared: (S1) one fraction with all beams delivered simultaneously, (S2) one fraction with sequentially delivered beams, (S3) three fractions with all beams delivered simultaneously, and (S4) three fractions with sequentially delivered beams.</p><p><strong>Results: </strong>UHDR fractionation and the beam delivery scheme had a significant impact on irradiated blood volume (1.3% ± 0.1% for S1 <i>v</i> 13% ± 3.2% for S4) and lymphocyte pool (12.5% ± 0.1% for S1 <i>v</i> 32.8% ± 0.2% for S4). UHDR scenarios primarily irradiate lymphocytes through exposure of head-and-neck lymph node rather than circulating blood. CONV and UHDR result in distinct temporal dose patterns for lymphocytes, characterized by either numerous low-dose fractions or a few high-dose pulses.</p><p><strong>Conclusion: </strong>The doses delivered to lymphoid organs account for a substantial portion of the total dose received by the lymphocyte pool, showing only a limited difference between UHDR and CONV in terms of lymphocyte exposure. The fractionation strategy of UHDR irradiation beams could play an important role in successfully translating UHDR treatments into clinical practice.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600091"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474137","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":"Evaluation of an Artificial Intelligence Communication Platform for Racial Biases in Pretest Education About Prostate Cancer Germline Testing.","authors":"Johanna Balas, Lauren Bowling, Nonna Shakhnazaryan, Rahul R Aggarwal, Hala T Borno, Julian C Hong, Franklin W Huang, Barry Tong, Daniel H Kwon","doi":"10.1200/CCI-25-00385","DOIUrl":"10.1200/CCI-25-00385","url":null,"abstract":"<p><strong>Purpose: </strong>Guidelines recommend germline testing in advanced prostate cancer (PCa) to inform treatment and personal/familial cancer risk, yet Black patients are less likely than White patients to complete testing. Artificial intelligence (AI) tools are increasingly used in pretest education to improve access but remain unevaluated for racial bias.</p><p><strong>Materials and methods: </strong>We developed ProGene, a secure, generative AI chatbot for PCa germline testing education in Black patients. We prompted ProGene with seven questions about testing types, personal benefits, family benefits, drawbacks, logistics, costs, and privacy. Each question was asked nine times across three patient vignettes (Black, non-Hispanic White, race-agnostic), in triplicate. Two blinded reviewers assessed responses across five domains: (1) comprehensiveness (0%-100%), (2) accuracy (presence/absence of inaccuracies), (3) readability (grade level via Simple Measure of Gobbledygook [SMOG] and Flesch-Kincaid), (4) actionability (0%-100% via Patient Education Materials Assessment Tool), and (5) quality (1-18 via DISCERN-AI). Outcomes were compared by race and question using two-sample t-tests or Wilcoxon rank-sum tests for continuous measures and chi-square or proportion tests for categorical measures; ANOVA was used for question-level comparisons.</p><p><strong>Results: </strong>The mean comprehensiveness was 67% and did not vary by race, although responses for question-1 (genetic testing types) were less comprehensive for Black versus race-agnostic vignette (60% <i>v</i> 93%, <i>P</i> < .01). Inaccuracies appeared in 32% of responses, primarily related to sample collection and cost/insurance, and did not vary by race. The mean readability were 10th (SMOG) and 13th (Flesch-Kincaid) grades, actionability 92%, and DISCERN score 14/18 (good quality); none varied by race.</p><p><strong>Conclusion: </strong>We identified no statistically significant racial disparities across the five evaluation domains in ProGene. Responses were generally good quality and actionable. Although AI may facilitate equity in PCa genetic education, deficiencies in comprehensiveness, accuracy, and readability highlight the need for refinement and/or human oversight with implementation.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2500385"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic Models for Optimal and Improved Health-Related Quality-of-Life Among Adult Survivors of Childhood Cancer: A Report From the Childhood Cancer Survivor Study.","authors":"Madeline R Horan, Fiona Schulte, Yan Chen, Yutaka Yasui, Wendy Leisenring, Paul C Nathan, Eric J Chow, Kirsten K Ness, Melissa M Hudson, Gregory T Armstrong, Kevin R Krull, Tara M Brinkman, I-Chan Huang","doi":"10.1200/CCI-26-00038","DOIUrl":"10.1200/CCI-26-00038","url":null,"abstract":"<p><strong>Purpose: </strong>Predictors of optimal or improved health-related quality-of-life (HRQOL) in adult survivors of childhood cancer are understudied.</p><p><strong>Methods: </strong>This cohort study used data from 4,755 survivors in the Childhood Cancer Survivor Study who completed baseline (T0) and two follow-up surveys (T1, T2) between 1992 and 2016. HRQOL was assessed using SF-36 Physical and Mental Component Summaries (PCS; MCS), classified as optimal (≥40) or suboptimal (<40), with improvement being suboptimal at T1 to optimal at T2.</p><p><strong>Results: </strong>At T1, 88.4% and 82.5% had optimal physical and mental HRQOL; among those suboptimal, 51.3% and 63.9% improved by T2. Higher education, physical activity, income, mental health, and absence of chronic conditions predicted optimal or improved HRQOL using multivariable logistic regression with backward selection. Model performance was acceptable for optimal PCS (0.81), MCS (0.72), and improved models (0.69 each).</p><p><strong>Conclusion: </strong>Findings may inform targeted interventions addressing education, lifestyle, mental health, and chronic conditions to enhance well-being in childhood cancer survivors.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"10 3","pages":"e2600038"},"PeriodicalIF":3.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801346","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}