JCO Clinical Cancer Informatics最新文献

{"title":"Assessment of Functional Status of Human Leukocyte Antigen Class I Genes in Cancer Tissues in the Context of Personalized Neoantigen Peptide Vaccine Immunotherapy.","authors":"Vijay G Padul, Nupur Biswas, Mini Gill, Jesus A Perez, Javier J Lopez, Santosh Kesari, Shashanka Ashili","doi":"10.1200/CCI-24-00174","DOIUrl":"10.1200/CCI-24-00174","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate human leukocyte antigen (HLA) typing is an essential step for designing peptide vaccines used in the personalized neoantigen peptide vaccine immunotherapy (PNPVT) in patients with cancer. The reasons for variation in the patient response to PNPVT are yet unknown. One of the reasons could be the somatic changes in the HLA genes in the cancer cells. The objective of the present research was to analyze the somatic status of HLA class I genes in cancer tissue through integrative genomic analysis and to identify high-confidence subset of potentially functional cancer somatic HLA class I genotype relevant to PNPVT.</p><p><strong>Patients and methods: </strong>Whole-exome (paired tumor-normal) and RNAseq (tumor) paired-end sequencing data from 24 patients with cancer were used for the analysis. The genotyping of HLA class I was performed using four HLA typing software tools. To assess the functional status of HLA class I genes in the cancer tissue, we analyzed somatic mutation, HLA gene loss of heterozygosity, and chromosome 6 copy loss status in cancer exome data.</p><p><strong>Results: </strong>Somatic mutations in HLA genes were detected in the tumor data of five patients, and somatic HLA gene loss of heterozygosity was identified in the tumor data of five patients. Complete or partial chromosome 6 copy loss was detected in eight patient samples.</p><p><strong>Conclusion: </strong>The results indicate that HLA class I genes may get affected by somatic changes in cancer tissue, and assessment of the somatic status of the HLA genotype should be performed in the cancer tissues. The results provide robust rational for removal of mutated or lost HLAs from the personalized neoantigen peptide prediction pipeline to potentially increase the efficacy of the PNPVT. Further functional studies are needed to assess the impact of HLA gene mutations/loss on PNPVT outcomes.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400174"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555616","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":"Consumer Wearable Device Measures of Gait Cadence and Activity Fragmentation as Predictors of Survival Among Patients Undergoing Chemotherapy.","authors":"Carissa A Low, Christianna Bartel, Krina Durica, Heidi S Donovan, Roby Thomas, Jennifer Fedor","doi":"10.1200/CCI-25-00111","DOIUrl":"https://doi.org/10.1200/CCI-25-00111","url":null,"abstract":"<p><strong>Purpose: </strong>Consumer wearable devices provide new opportunities for measuring patterns of objective daily physical activity throughout cancer treatment. In addition to capturing step counts, these devices can also measure gait cadence and activity fragmentation, two metrics that may reflect functional capacity. The goal of the current study was to examine whether step count, gait cadence, and activity fragmentation predicted overall survival in patients with solid tumors.</p><p><strong>Methods: </strong>We enrolled patients (N = 213) receiving outpatient chemotherapy for any solid tumor into an observational cohort study. Patients wore a consumer wearable device to measure continuous physical activity patterns for up to 90 days and were followed for a median of 2.53 years, during which 42% of the sample died. Univariable and multivariable Cox proportional hazards regression analyses were used to evaluate associations between wearable device physical function metrics and survival.</p><p><strong>Results: </strong>In univariable analyses, higher step count (hazard ratio (HR), 0.87; <i>P</i> = .007), less activity fragmentation (HR, 1.03; <i>P</i> < .001), and faster peak gait cadence (HR, 0.81; <i>P</i> < .001) were significantly associated with lower mortality risk. Associations with activity fragmentation and gait cadence persisted after adjustment for age and cancer type and stage and after additional adjustment for clinician-rated performance status and patient-reported physical function.</p><p><strong>Conclusion: </strong>Activity fragmentation and gait cadence metrics derived from consumer wearable devices were associated with overall survival in patients receiving chemotherapy for any solid tumor. These associations remained statistically significant after adjustment for covariates, including clinician-rated performance status and patient-reported physical function. These findings suggest that wearable devices may capture important prognostic information about physical function independent of what clinicians and patients perceive.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500111"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612408","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":"Disparities in Patient Portal Messaging Among Oncology Patients Enrolled in the Patient Portal.","authors":"Jes Alexander, Alexis L Beatty","doi":"10.1200/CCI-24-00234","DOIUrl":"https://doi.org/10.1200/CCI-24-00234","url":null,"abstract":"<p><strong>Purpose: </strong>Previous studies have consistently reported disparities in electronic health record portal enrollment. Among patients enrolled in a portal, it is less clear whether there are disparities in usage. We investigated whether disparities existed in portal usage among enrolled oncology patients regarding both sending portal messages to and receiving messages from oncology providers.</p><p><strong>Methods: </strong>This retrospective cohort study included patients ≥18 years old with cancer who were seen at an urban academic cancer center between January 2011 and February 2025 and enrolled in the patient portal. We developed Cox proportional hazards models for the outcomes of patients sending portal messages to and receiving messages from oncology providers as the first message in a thread. Time measurement began with the first cancer center visit or portal enrollment, whichever was later. Models were adjusted for demographic, socioeconomic, disease, and administrative visit variables.</p><p><strong>Results: </strong>Among 101,678 patients, the median age was 62 years (IQR, 51-71), and 68,527 sent and 42,242 received messages. After adjustment, age ≥50 versus 18-29 years, Latinx and Pacific Islander versus White, single and widowed versus partnered, non-English preferred language, and Medicaid and Medicare versus private insurance were associated with reduced likelihood of sending and receiving messages. Black and American Indian/Alaska Native were associated with reduced likelihood of sending messages. Female provider was associated with increased likelihood of sending and receiving messages. Women were more likely to send messages.</p><p><strong>Conclusion: </strong>Among oncology patients enrolled in the patient portal, disparities existed in sending and receiving portal messages. Given the association of messaging with better survival among oncology patients in previous studies, future studies should determine how best to minimize messaging disparities beyond just addressing disparities in portal enrollment.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2400234"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602237","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":"Benefits, Problems, and Motivations for Using the Online Patient Portal in Adolescent Oncology: Interviews With Adolescents and Parents.","authors":"Bryan A Sisk, Stephanie Chen, Christine Bereitschaft, Mark A Fiala, Lindsay J Blazin, Maya F Ilowite, Jennifer Mack, James DuBois","doi":"10.1200/CCI-25-00038","DOIUrl":"https://doi.org/10.1200/CCI-25-00038","url":null,"abstract":"<p><strong>Purpose: </strong>Communication is central to optimizing adolescent cancer care. Online patient portals are widely available tools that support communication. However, the perspectives of parents and adolescents on parental portal access has not been well studied.</p><p><strong>Methods: </strong>We performed separate semistructured interviews with adolescents with cancer and their parents, recruited from three academic pediatric cancer centers. We performed thematic analysis of benefits, problems, and motivations for parental portal use.</p><p><strong>Results: </strong>We interviewed 48 parent/adolescent dyads with cancer. Participants described the importance of allowing parents access to their child's portal, related to perceived parental needs and rights. Parental needs related to managing their child's complex medical needs. Parental rights related to their financial support for the child and their obligation to ensure their child's well-being. Although the cancer diagnosis did not change views on parental rights, it did increase parental needs for portal access. Participants described five benefits provided by portals: (1) improving parental knowledge and understanding, (2) supporting care coordination and family self-management, (3) supporting communication, (4) supporting parental roles, and (5) strengthening relationships. Participants described four problems caused by portal access: (1) complexity of portal contents and misunderstanding, (2) emotional distress, (3) loss of privacy, and (4) exacerbating family tensions. Parents described two factors influencing their portal use: (1) user experience, especially onerous enrollment processes, and (2) perceived usefulness of the portal.</p><p><strong>Conclusion: </strong>Adolescents with cancer and their parents believed that parents should be permitted access to nonsensitive clinical data in the adolescent's portal. Limiting portal access could create extra burdens on parents. Electronic health record companies and hospitals must develop technologies to permit parental access to nonsensitive information through the portal, especially in oncology.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500038"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585590","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 Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission.","authors":"J Felipe Montano-Campos, Erin Hahn, Eric Haupt, Jerald Radich, Aasthaa Bansal","doi":"10.1200/CCI-25-00003","DOIUrl":"10.1200/CCI-25-00003","url":null,"abstract":"<p><strong>Purpose: </strong>There is little guidance for decision making in chronic myeloid leukemia (CML) after patients achieve molecular remission. Our study addresses this gap by developing a risk prediction model for molecular relapse using early longitudinal factors, such as BCR::ABL1 biomarker-level changes and treatment adherence.</p><p><strong>Methods: </strong>We analyzed electronic health record data of patients with CML diagnosed between 2007 and 2019 from an integrated health system. We used a time-to-event modeling framework using a Cox proportional hazards approach where we evaluated time from molecular remission to molecular relapse. The main predictors were early changes in BCR::ABL1 levels from treatment initiation to the first follow-up measurement (typically around 3 months) and treatment adherence in the first 6 months, categorized as perfect (≥0.98) or less-than-perfect (<0.98). Model performance was assessed through five-fold cross-validation combined with 100 Monte Carlo bootstrapping iterations to ensure robustness and minimize bias.</p><p><strong>Results: </strong>Patients with early improvement in BCR::ABL1 levels had a 70% lower risk relapse (hazard ratio [HR], 0.30 [95% CI, 0.15 to 0.59]) compared with those without early molecular response. Perfect adherence during this critical early phase of treatment was associated with a 56% lower relapse risk (HR, 0.44 [95% CI, 0.22 to 0.85]). Predictive accuracy was high at 6 months (AUC, 0.90; 95% CI, 0.87 to 0.95) and 1-year postremission (AUC, 0.78; 95% CI, 0.74 to 0.81). Relapse risk was significantly higher among Black, Asian, and Hispanic patients compared with non-Hispanic White patients.</p><p><strong>Conclusion: </strong>Early biomarker trends and adherence after treatment initiation are critical for accurately predicting relapse among patients who achieve molecular remission. The proposed model addresses a gap in guidance after molecular remission and has the potential to enable personalized monitoring and optimize surveillance strategies, offering transformative potential for CML care.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500003"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585591","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":"Linear Federated Learning for Outcome Prediction With Application to Hepatocellular Carcinoma Radiotherapy.","authors":"Keyur D Shah, Harald Paganetti, Pablo Yepes, Theodore S Hong, Jennifer Y Wo, J Hannah Roberts, Eugene J Koay, Christian V Guthier, Ibrahim Chamseddine","doi":"10.1200/CCI-25-00074","DOIUrl":"https://doi.org/10.1200/CCI-25-00074","url":null,"abstract":"<p><strong>Purpose: </strong>Federated learning (FL) enables multi-institutional predictive modeling without sharing raw patient data, preserving privacy while leveraging diverse data sets. This study evaluates the use of linear FL (LFL) as an interpretable approach to enhance sample size and generalizability in outcome prediction. As a proof of concept, we applied LFL to patients with hepatocellular carcinoma (HCC) undergoing external beam radiotherapy (EBRT), predicting hepatic toxicity and 1-year survival (SRVy1).</p><p><strong>Methods: </strong>Patient data from Massachusetts General Hospital (MGH) and Brigham and Women's Hospital (BWH) were used to train models, whereas an independent validation data set from MD Anderson Cancer Center assessed generalizability. Logistic regression was developed to predict hepatic toxicity and SRVy1 using key clinical features, including baseline albumin, bilirubin, Child-Pugh score, liver size, and mean liver dose. The LFL approach allowed each institution to train models locally without sharing raw patient data. Model performance was evaluated using the AUC and compared between the LFL model and institution-specific models.</p><p><strong>Results: </strong>For survival prediction, single-institution models were limited, with AUC = 0.55-0.63, with LFL increasing it to 0.67. For toxicity prediction, external validation showed AUC = 0.68 for the MGH model and 0.69 for the BWH model, with LFL maintaining the AUC at 0.7. The model coefficients were moderate in the LFL compared with the single-institution models, indicating an ability to mitigate bias, which was also reflected by better performance on the validation data set.</p><p><strong>Conclusion: </strong>LFL maintained or improved predictive performance over single-institution models for survival and hepatic toxicity in patients with HCC treated with EBRT while preserving model interpretability and patient privacy. These findings support LFL's role in multi-institutional collaborations.</p>","PeriodicalId":51626,"journal":{"name":"JCO Clinical Cancer Informatics","volume":"9 ","pages":"e2500074"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531078","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":"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":"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":"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}