搜索结果：JCO clinical cancer informatics

Access to care is an important component of cancer center catchment area (CA) analytics, where CAs are defined as the geographic scope of cancer center operations. Spatial access to care is one piece of the access to care continuum that is useful for quantifying population travel to health care providers. As no studies have comprehensively calculated CA spatial access to providers, we examined access to oncology, cancer care, and primary care providers for all 65 National Cancer Institute-designated cancer center CAs in the 48 contiguous US states. We used the 2024 end-of-year Centers for Medicare and Medicaid Services National Downloadable File and the enhanced two-step floating CA method to compute spatial accessibility. We stratified analyses by cancer center, census division, 2020 urban/rural status, 2023 area deprivation, and cancer center type, and produced select CA maps. Census tracts in the Montefiore Einstein Comprehensive Cancer Center CA had the highest oncology and cancer care spatial access, while the Masonic Cancer Center had the highest primary care spatial access. New Jersey, New York, and Pennsylvania CAs had the highest oncology and cancer care spatial access (P < .001), while midwestern CAs had the highest primary care spatial access (P < .001). Across area deprivation index quartiles and all provider groupings, urban tracts had higher spatial access than rural tracts (P < .001). Comprehensive cancer centers had higher spatial access to oncology and primary care than noncomprehensive cancer centers (P < .001), while noncomprehensive cancer centers had higher spatial access to cancer care providers (P < .001). We observed significant differences in CA spatial access to oncology, cancer care, and primary care by region, urban/rural status, socioeconomic position, and cancer center type.

Hereditary cancer risk is key to guiding screening and prevention strategies. Cancer risks can vary by individual because of the presence or absence of high- and moderate-risk pathogenic variants (PVs) in cancer-associated genes, in addition to sex, age, and other risk factors. We previously developed Fam3PRO, a flexible multigene, multicancer Mendelian risk prediction model that estimates a patient's risk of carrying a PV in hereditary cancer genes and their future risk of developing several types of cancers. The Fam3PRO R package includes 22 genes with 18 associated cancers, allowing users to build customized submodels from any gene-cancer set. However, the current R package lacks a user interface (UI), limiting its practical use in clinical settings. Therefore, we aim to develop a web-based UI for broader use of the Fam3PRO functionalities. The Fam3PRO UI (F3PI), built using R Shiny, collects and formats inputs including family health history, genetic test results, and other risk factors. Pedigree data are interactively visualized and modified using pedigreejs, whereas the backend Fam3PRO model takes all the inputs to generate carrier probabilities and future cancer risks, presented through an interactive UI. F3PI streamlines the collection of patient and family history data, which is analyzed by the Fam3PRO models to provide personalized cancer risks for each proband across 18 cancers, as well as probabilities that a proband has a PV in up to 22 hereditary cancer genes. These results are returned to the user, within 1 minute on average, and are available in both interactive and downloadable formats. We have developed F3PI, an easy-to-use, interactive web application that makes cancer and genetic risk information more accessible to providers and their patients.

Population-based cancer registries are a key data resource for catchment area informatics, but their utility for quantifying differences in cancer burden by socioeconomic status is limited. Here, we describe an approach that estimates cancer incidence along income gradients, leveraging a newly validated method called weighting by income probabilities (WIP). We estimated income-specific colorectal cancer incidence, stratified by sex and race/ethnicity, in a catchment area (Ohio) as a case study. Income-specific numerator data (number of cancer cases per income bracket) were estimated using WIP, whereas denominators (population at risk by income bracket) were derived from US Census data. In the case study of the 52,257 patients with invasive colorectal cancer diagnosed in the catchment area of Ohio between 2010 and 2019, lower income was generally associated with higher incidence rates, except in non-Hispanic (NH) White female individuals. The highest incidence was observed in NH Black male individuals at 0-149% of the Federal Poverty Level, with 113.7 cases per 100,000 (95% CI, 99.6 to 129.3) in 2010-2012, compared with 57.8 (95% CI, 54.7 to 61.2) in their NH White counterparts. Sensitivity analyses showed that income-specific incidence statistics were robust to sources of error in numerator and denominator estimation, with incidence estimates varying by no more than 1.98% from the reference estimates. The approach described here accurately estimates cancer incidence along income gradients and can be expanded to estimate income-specific survival and mortality. The case study of colorectal cancer in Ohio demonstrates important insights into the burden of cancer by income. These granular income-specific data can enhance our understanding of the relationship between cancer burden and socioeconomic status and inform cancer surveillance, prevention, and control efforts.

Curating high-quality clinical and genomic data sets from patients with cancer to predict hospital readmission using machine learning (ML) models. We extracted data from electronic health records for patients with cancer in the University of California, San Diego Health System, to curate clinicogenomic data sets for lung, breast, and colon cancers. We constructed ML models to predict the risk of hospital readmission 30, 60, and 90 days postdischarge. Standard ML models (logistic regression, random forest [RF], gradient boosting [GB], neural network) and multitask neural network models were developed to simultaneously predict all three readmission outcomes. Our results revealed that rehospitalization is most frequent in colon cancer within 30 days. For the 30-day hospitalization prediction, GB achieved the highest area under the precision recall curve (PR-AUC) for lung (0.415) and breast (0.470) cancers and RF achieved the overall highest PR-AUC for colon cancer (0.621). Explainability analysis revealed that health care metrics (such as the number of previous admissions and average length of stay), risk scores composed of diagnosis codes, and treatments are significant features in predicting readmission within cancer types. It also identified EGFR mutations as a potential predictor of readmission in colon cancer. The study highlights the potential of integrating clinical and genomic data for predicting adverse outcomes in patients with cancer. The standard ML approaches were able to successfully capture patterns in readmission and outperformed the more complex models. Limitations include the relatively small data set from a single institution. Ultimately, this study highlights the value of curating and maintaining clinicogenomic information at an institution level to streamline data set curation and model development.

The University of Wisconsin Population Health Institute (PHI) Model of Health, grounded in models developed over a decade ago, provides a framework for prioritizing health-related investments including setting agendas, implementing policies, and sharing resources for improving community health and health equity. The model includes multiple determinants of health and two broad health outcomes (length and quality of life). We adapted the PHI Model of Health to cancer outcomes. Using county-level publicly available data, health factor summary measures were derived in three areas: health infrastructure including health promotion and clinical care, physical environment, and social and economic factors. A composite health factor z-score was calculated as the weighted (40%, 15%, and 45%, respectively) average of the summary measures for each county, and k-means clustering was used to create unequally sized county groups with lower (healthier) to higher (less healthy) z-scores. We fit age-adjusted negative binomial regression models to estimate rate ratios and 95% CI for cancer mortality in relation to county health factor cluster. Age-adjusted cancer mortality rates increased across the 10 county health factor clusters for all-cancers as well as for lung, colorectal, breast, and prostate cancers. Rate ratios generally increased across the 10 health factor clusters for all cancers combined and for specific cancer types. Compared with counties with the most favorable health factor conditions, the counties with the least favorable conditions had an all-cancer mortality rate ratio of 1.49 (95% CI, 1.39 to 1.60). The PHI model of health adapted to cancer outcomes provides an approach for linking community-specific conditions to the interventions that hold promise to directly address drivers of the cancer burden.

Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a large-scale, applied bias-probing study of a deep learning-based cancer site classifier to quantify race information encoded in document embeddings. We then evaluated how performance changes when race-correlated embedding dimensions are removed in a post-training sensitivity analysis. The cancer site classifier was trained using 3.5 million electronic cancer pathology reports from six of the National Cancer Institute's SEER registries. We trained a hierarchical self-attention network to generate 400-dimensional document embeddings. These embeddings were used to train two downstream, gradient-boosted decision tree classifiers: one to classify the cancer sites and another to predict racial categories. We identified overlapping features by intersecting the top 50 feature-importance rankings from the site and race models and computed their cumulative feature importance in each model. As a post hoc sensitivity analysis, we progressively pruned these overlapping dimensions, retrained the site model, and compared overall macro-F1 and accuracy, race-stratified macro-F1, and group fairness metrics on the basis of demographic parity and equalized odds before and after pruning. The analysis revealed minimal feature overlap between the cancer site and race prediction models, and the cumulative importance scores indicated a negligible influence of racial information on clinical predictions. Post-training pruning of overlapping features did not compromise the models' diagnostic accuracy, with a 0.07% loss in accuracy. Our findings demonstrate that HiSAN-generated embeddings from SEER data can be used effectively in cancer site classification without significant demographic bias influencing the outcomes. Post-training pruning therefore functions as a practical audit and sensitivity check.

Disparities in lung cancer incidence exist in Black populations, and screening criteria underserve Black populations due to disparately elevated risk in the screening-eligible population. Prediction models that integrate clinical and imaging-based features to individualize lung cancer risk are a potential means to mitigate these disparities. This multicenter (National Lung Screening Trial [NLST]) and catchment population-based (University of Illinois Health [UIH], urban and suburban Cook County) cross-sectional study used participants at risk of lung cancer with available lung computed tomography (CT) imaging and follow-up between the years 2015 and 2024. In all, 53,452 in NLST and 11,654 in UIH were included on the basis of age and tobacco use-based risk factors for lung cancer. Cohorts were used for training and testing of deep and machine learning models using clinical features alone or combined with CT image features (hybrid computer vision). An optimized seven-feature clinical model achieved receiver operating characteristic (ROC)-AUC values ranging from 0.64 to 0.67 in NLST and 0.60 to 0.65 in UIH cohorts across multiple years. Incorporation of imaging features to form a hybrid computer vision model significantly improved ROC-AUC values to 0.78-0.91 in NLST but deteriorated in UIH with ROC-AUC values of 0.68-0.80, attributable to Black participants where ROC-AUC values ranged from 0.63 to 0.72 across multiple years. Retraining the hybrid computer vision model by incorporating Black and other participants from the UIH cohort improved performance with ROC-AUC values of 0.70-0.87 in a held-out UIH test set. Hybrid computer vision predicted risk with improved accuracy compared with clinical risk models alone. However, potential biases in image training data reduced model generalizability in Black participants. Performance was improved upon retraining with a subset of the UIH cohort, suggesting that inclusive training and validation data sets can minimize racial disparities. Future studies incorporating vision models trained on representative data sets may demonstrate improved health equity upon clinical use.

Over 90% of people with hereditary cancer syndromes in the United States remain unidentified. The Genetic Cancer Risk Detector (GARDE) is an open-source, electronic health record (EHR)-integrated, digital health platform that can facilitate genetic cancer risk assessment and genetic testing. This study evaluates its budget impact on health care institutions. A budget impact analysis was performed from the perspective of a US health care provider system over a 3-year horizon. Data from the BRIDGE randomized controlled trial data from the University of Utah Health (UHealth) were used, where eligible primary care patients were screened for genetic cancer risk via GARDE. Costs of GARDE were categorized across planning, implementation, and operational phases. Revenue projections were based on Centers for Medicare & Medicaid Services reimbursement rates. Scenario analyses varied uptake of interventions, surveillance intervals, reimbursement rates, and implementation scale. Of 1,444 patients identified by GARDE at UHealth and enrolled in the BRIDGE trial, 205 completed genetic testing, with 15 found to carry pathogenic variants. The total 3-year implementation cost was $29,217 US dollars (USD). Revenue from guideline-recommended procedures totaled $86,563 USD, yielding a net positive budget impact of $57,347 USD. Most revenue (76.4%) was generated by surgical risk-reduction procedures. Scenario analyses revealed high sensitivity to cancer risk-reducing surgery uptake and implementation scale. Modeling 100% uptake of risk-reducing surgeries increased revenue to $128,102 USD, while 20-fold scaling of the implementation population increased revenue to $1.7 million USD. Commercial insurance reimbursement assumptions further amplified revenue. GARDE enables scalable hereditary cancer risk assessment within a health care provider system. Even with modest uptake, it yields a positive financial return, and significantly greater revenue is achievable with broader implementation. These findings support adoption of EHR-integrated tools to enhance clinical outcomes in precision cancer prevention and risk management, in an economically viable manner.

Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.

Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using digitized whole-slide images (WSI) of histopathology. Publicly available WSI of pancreatic ductal adenocarcinoma resections from three cohorts were used for training. The model consisted of a pan-cancer foundation model to generate embeddings, mean-pooling across tissue patches, and then a fully connected neural network. Model predictions were compared with human-labeled histopathologic features and genomic alterations. The model was externally validated in a meta-analysis of a single-center cohort from Princess Margaret Cancer Centre, a multicenter cohort from France, and the PRODIGE 24 trial of adjuvant chemotherapy. The deep learning model was trained on 12,594 tissue patches from 257 patients. High-risk classifications were associated with squamous morphology, reactive stroma, tumor cellularity, and necrosis, whereas low-risk classifications were associated with tubulopapillary and conventional morphologies, as well as deserted stroma. High-risk cancers were enriched for basal-like gene expression profiles and distinct oncogenic pathways. In a meta-analysis of the external cohorts, the hazard ratio (HR) for death comparing high-versus low-risk cancers was 1.49 (95% CI, 1.25 to 1.79, P < .001), whereas the HR for recurrence or death was 1.41 (95% CI, 1.19 to 1.68, P < .001). The classifications remained prognostic among moderately differentiated cancers. An open-source deep learning model using WSI from pancreatic cancer resections generated risk classifications that correlated with histopathologic and genomic features. Classifications were externally validated in a meta-analysis of three cohorts. This model could be applied to WSI to provide individualized prognostic information for patients.

The oncogenic impact of somatic driver alterations is shaped by tissue context. Classifying alterations by cancer type and evaluating their context-specific properties requires large cohorts of genomically profiled and clinically annotated tumors. Here, we define cancer type-specific patterns of driver alterations, including 164 newly identified hotspots, in 54,331 tumors from 48,179 patients spanning 448 histological cancer subtypes. One-third of all drivers arose in non-canonical contexts and exhibited distinct features, including increased subclonality, later emergence, and divergent biological properties. Within cancer types, gene fusions and other distinct patterns of co-occurring drivers are indicative of earlier age of disease onset. We also identify ancestry-specific differences in human leukocyte antigen (HLA)-restricted driver neoantigens affecting T cell receptor therapy eligibility, and demonstrate cancer-type-specific patterns of intrinsic resistance via somatic HLA loss. Our findings highlight that functional roles of driver alterations depend on the cancer types and clinical contexts in which they arise.

Radiation pneumonitis (RP) is the most common toxicity after thoracic radiotherapy. We develop an artificial intelligence model to predict RP in an institutional cohort of patients undergoing radiotherapy for non-small cell lung cancer. Data were collected from patients diagnosed between 2002 and 2020. Patients were screened for a known survival/RP outcome, as well as treatment and clinical parameters. A transformer, pretrained on an open-source data set, was first trained to predict abnormal versus normal pulmonary function based on computed tomography (CT) scans. Transfer learning was then used to apply this model to the RP data set. Three clinical-plus-dosimetric variable models were trained. Finally, a model that combined the CT-based risk score and clinical/dosimetric variables was also trained, to explore if the CT-based risk score improved risk stratification. All models were cross-validated. 1,023 patients were included in the RP data set, for a total of 2,257 pretreatment scans, with a 15% RP rate. The clinical-plus-dosimetric-only values were 0.70, 0.70, and 0.71, and the CT-only was 0.66. Combining the CT-based risk score and clinical parameters improved the receiver operating characteristic curve to a value of 0.74, averaged across all folds. The combined model also had superior sensitivity for a fixed specificity value of 60%. Precision-recall metrics were comparable across models. Activation mapping of the CT-only model showed prioritization of upper lung and right lung. In a cohort treated heterogeneous radiotherapy techniques and doses, combining CT-based risk scores with clinical values enhances the prediction of RP. This suggests that CT scans contain additional information that has the potential to enhance RP predictions. Activation score mapping shows focus on lung structure, upper lung, and right lung. Model code is available online.

Reviewing pathology reports requires physicians to integrate complex histopathologic, immunohistochemical, and molecular findings from multiple reports and institutions, often under time constraints that increase the risk of error and fatigue. Large language models (LLMs) offer a potential solution by generating concise, coherent summaries from complex pathology data. Patients who underwent initial consultation in a thoracic clinic between January 2019 and July 2023 were included. Original pathology reports and corresponding physician pathology summaries from consultation notes were extracted and anonymized. Six open-source LLMs (Llama 3.0, Llama 3.1, Llama 3.2, Mistral, Gemma, and DeepSeek-R1) generated pathology summaries directly from the original reports. Objective and subjective evaluations were performed using the original reports as the ground truth. LLM-generated summaries were compared with physician summaries for correctness, completeness, and conciseness. Additional subjective assessments with multiple evaluators were conducted for Llama 3.1. Ninety-four cases met the eligibility criteria. Using the original pathology reports as the ground truth, the LLM-generated summaries achieved higher scores across all objective evaluation metrics compared with physician pathology summaries (P < .0001). In the subjective evaluation, DeepSeek, Mistral, Llama 3.1, and Llama 3.2 achieved higher ratings for completeness (P = .017, P < .0001, P < .0001, and P < .0001, respectively) while maintaining comparable correctness relative to physician pathology summaries (P = 1.000). The results remained consistent in additional subjective analyses involving multiple evaluators for Llama 3.1. LLM-generated summaries demonstrated better performance in objective metrics and greater completeness in subjective evaluations compared with physician summaries. These results highlight the potential of LLMs as valuable tools for enhancing clinical documentation and workflow efficiency in oncology practice.

This study assessed the feasibility of developing the University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center (UMGCCC)-Medicare-linked database infrastructure by integrating tumor registry, electronic health records (EHRs), and Medicare administrative claims data. The database was designed to support research identifying determinants of differences in cancer outcomes among patient populations commonly under-represented in clinical trials (based on the US population with the disease) including older adults. Patients 65 years and older who were diagnosed and/or received their first course of treatment for a primary tumor at UMGCCC from 2018 to 2021 were included in the database. A two-stage data linkage process was used to merge cancer center tumor registry data with EHR and Medicare claims data. We performed data quality and linkage quality checks. Summary statistics were calculated for patient and tumor characteristics. Of the 3,322 patients identified from the tumor registry, 3,119 patients (94%) were included in the UMGCCC-Medicare database (mean age 73.1 years, 56% male, 31% Black). Lung cancers were the most common (15%) followed by oral cancers (12%) and non-Hodgkin lymphoma (6%). The development of the UMGCCC-Medicare database serves as proof of concept for linking real-world data from different sources. The database is a valuable resource for research requiring detailed patient-level data and follow-up that may generate real-world evidence for older adults living in the United States and treated in routine oncology practice.

Survival discrepancy between male and female patients in lung cancer is a well-known, but still poorly understood phenomenon. Previous studies have used different patient cohorts and clinical covariates and have not included obesity, which is associated with longer lung cancer survival. We evaluated the relationship between survival, obesity, sex and other covariates using comprehensive, harmonized patient cohorts and a federated analysis approach. Initial analyses were done in a retrospective, real-world cohort of 7,327 patients with lung cancer diagnosed at the Helsinki University Hospital from 2015 to 2024. Patients were stratified by BMI, and univariate and multivariate analyses of survival were performed. External validation of univariate analyses was performed on data from four European university hospitals (n = 12,700). Higher BMI was associated with a smaller sex-related survival difference. In the normal BMI cohort (18.5-25 kg/m2), the 2-year overall survival was 46% in females and 29% in males (P < .01). In the high BMI cohort, the difference was 51% versus 41% (P < .01). Similar trends were observed in the validation sites, with some variation. The largest effect of high BMI was observed in squamous cell carcinoma. When full multivariate analysis was performed separately for high and normal BMI patients, the effect of male sex on survival was 32% smaller among high BMI patients. Higher BMI was associated with reduced survival gap between sexes, emphasizing the value of comprehensive covariate reporting in future clinical trials and observational studies.

A core clinical task is to synthesize fragmented patient data into a coherent summary to support decision making. However, electronic health record (EHR) inefficiencies burden clinicians and contribute to their cognitive overload and burnout. This study evaluated the impact of a large language model (LLM)-enabled clinical decision support (LLM-CDS) platform compared with a simulated EHR (SimEPR) on workflow efficiency and user experience in generating accurate clinical summaries during tumor board preparation and explored its applicability to consultation preparation, referrals, treatment planning, and patient communication. In a remote, within-participant simulation, 26 oncologists from the United Kingdom, United States, Spain, and Singapore reviewed synthetic breast cancer cases and created comprehensive summaries for tumor board discussions using both LLM-CDS and SimEPR. LLM-CDS provided editable LLM-generated summaries; SimEPR required manual composition. Time to task completion was recorded. An independent reviewer assessed summary quality based on completeness, correctness, and conciseness. Participants also completed surveys on usability, cognitive load, and feature acceptability. LLM-CDS significantly reduced the summary completion time compared with SimEPR (6:55 v 8:47 minutes; P < .001). Summary completeness was rated higher with LLM-CDS (mean score, 3.93 v 3.13), whereas correctness and conciseness were similar. Overall, 87% of participants would recommend LLM-CDS and 96% would anticipate time savings. The system usability scale score for LLM-CDS was 65.7. Although perceived cognitive load was lower with LLM-CDS, the difference was not statistically significant. The LLM summary was the most valued, with 92% finding it useful for the tumor board and consultation preparation. The LLM-CDS platform improved the efficiency and completeness of clinical summarization. Strong user acceptance and anticipated time savings underscore the potential for streamlining a range of oncology workflows.

Comprehensive genomic profiling (CGP) is a key strategy in precision medicine for lung cancer, yet its clinical implementation remains limited, partly because of the uncertainty in identifying druggable mutations in individual patients. In this study, we investigated the potential of an artificial intelligence (AI)-based tool to predict the probability of identifying druggable mutations before CGP (pretest probability). We developed an eXtreme Gradient Boosting (XGBoost) prediction model trained on pre-CGP clinical variables from 3,470 patients with lung cancer (June 2019-November 2023) to estimate the probability of identifying druggable mutations. The key predictors were identified using explainable artificial intelligence (XAI) analysis. The refined model was deployed as a web application and evaluated in a temporally independent test cohort of 1,307 patients (December 2023-November 2024), with Brier score as the primary end point. The prediction model achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (95% CI, 0.82 to 0.89) in the overall validation cohort and 0.79 (95% CI, 0.74 to 0.84) in patients for whom a driver mutation had not been identified through companion diagnostic testing. The XAI analysis identified sex, smoking history, histology, and metastatic sites as important predictors. Even among patients who underwent tissue CGP, bone (P = .011) and lung (P < .001) metastases were significantly associated with a higher druggable mutation detection rate. The deployed model achieved Brier scores of 0.19 in the overall independent test cohort and 0.16 in patients for whom a driver mutation had not been identified through companion diagnostic testing. These findings indicate that an AI-based tool using pre-CGP clinical data may support broader CGP implementation and improve access to targeted therapies.

Digital patient portals (DPPs) provide patients a direct electronic link with their care teams allowing secure, rapid symptom reporting and the capture of electronic patient-reported outcome questionnaires. A proportion of patients face barriers to DPP adoption. We conducted a prospective observational cohort study to quantify the proportion of DPP nonadopters and identify factors associated with DPP nonadoption. All patients with prostate cancer undergoing radical radiotherapy at a Canadian Provincial Cancer Program were given the opportunity to install a DPP on their mobile device/tablet before starting radiotherapy. Each patient completed a self-reported questionnaire assessing their willingness and ability to use the DPP and quantified variables associated with their decision. A DPP adopter was defined as a patient who logged into the application and accessed their radiotherapy educational materials. Descriptive variables were tabulated by DPP adoption status. Differences in distributions of baseline characteristics were assessed using standard parametric and nonparametric statistics. Univariable and multivariable logistic regression analyses were performed to identify variables associated with DPP nonadoption. Between July 2022 and January 2023, 118 patients with prostate cancer were enrolled with a median age of 71 years (range, 50-86). Most patients (56.8%) had a college diploma, 29.6% lived rurally, and 95.8% owned a smartphone. Twenty-eight patients (23.8%) were DPP nonadopters. On stepwise multivariable logistic regression analysis, poor self-reported ability to use mobile devices was associated with DPP nonadoption (P < .01). Among patients with prostate cancer undergoing radical radiotherapy, poor self-reported ability to use smartphone technology was identified as the primary barrier to DPP adoption. Interventions geared at mitigating this barrier to DPP adoption are needed.

Real-world data (RWD) are increasingly used in oncology research, regulatory decisions, and clinical practice; however, variability in data quality and lack of standardization remain major limitations. This study assessed the readiness of oncology RWD from Saudi health care centers for standardization and evaluated their completeness and accuracy. Deidentified electronic health records for adult patients (18 years and older) diagnosed with breast cancer, thyroid cancer, colorectal cancer, gastric cancer, hepatocellular carcinoma, or renal cell carcinoma were extracted from five health care centers within the Saudi Real-World Evidence Network. Readiness for standardization was evaluated by assessing alignment with data elements in the Minimal Common Oncology Data Elements (mCODE) framework, a standardized and clinically focused oncology data model. Data quality was evaluated using two dimensions: completeness, defined as the proportion of patients with at least one entered value for each element; and accuracy, defined as the proportion of correct entries based on verification checks (including plausibility and consistency). Outcomes were calculated at the element level and weighted to generate domain- and center-level proportions. A total of 20,671 oncology patients were included. Overall weighted alignment with mCODE domains was moderate (62.43%). The patient domain showed the highest alignment (71.43%), whereas the outcome domain exhibited significant gaps. Data completeness was low to moderate (49.02%), with higher levels in common cancers (54.33%) than in rare cancers (51.50%). Data accuracy was high overall (95.03%), with rare cancers showing higher accuracy (98.76%) than common cancers (94.62%). Saudi oncology RWD show moderate alignment with mCODE, with consistently high accuracy across domains. However, gaps in data completeness highlight the need for broader adoption of standardized data frameworks to support interoperability and enable nationwide research and regulatory use.