Motolani E. Ogunsanya

Introduction: Residents of counties with higher poverty levels experience elevated smoking rates and disproportionate tobacco-related cancer mortality. This study examined differences in sociodemographic characteristics, tobacco use, and service utilization among Oklahoma Tobacco Helpline (OTH)...

Background: Prostate cancer (CaP) disproportionately affects Black men in the United States, leading to significant disparities in incidence, survival, and quality of life (QoL). Treatment-related side effects, including urinary dysfunction, pain, fatigue, and psychological distress, contribute to poor long-term outcomes. There is an urgent need for culturally-tailored, technology-based interventions to support symptom self-management and survivorship care. Objective: This study aims to develop, refine, and evaluate the Survivorship App for Ethnically Diverse Black Prostate Cancer Survivors (SAFE-CaPS), a tailored mobile health intervention designed to enhance QoL, improve symptom self-management, and provide psychosocial support. Methods: This 12-month stratified randomized clinical trial will enroll 248 Black survivors with CaP (including native born, African-born, and Caribbean-born men) into the SAFE-CaPS intervention arm and standard care (SC) control group (1:1 allocation). Eligible participants are self-identified Black men (including native born, African-born, and Caribbean-born) aged 40 to 80 years with a confirmed CaP diagnosis within the past 5 years who own or are willing to use a smartphone. SC will consist solely of routine oncology or primary care follow-up, including clinic visits, referrals, and supportive services as needed, with no digital components. The SAFE-CaPS intervention, developed using the Insight platform, includes daily and weekly ecological momentary assessments of pain, urinary and bowel symptoms, sleep, fatigue, sexual function, mood, physical activity, and diet; adaptive educational modules addressing treatment-related side effects and culturally grounded survivorship concerns; automated symptom alerts prompting nurse follow-up and provider notification; and engagement features such as reminders, goal-setting, and literacy-appropriate content. Intervention content incorporates culturally tailored messaging, patient narratives, and feedback from prior qualitative work with ethnically diverse Black CaP. Stratified randomization will ensure balanced representation of ethnic subgroups. Primary outcomes include overall QoL. Secondary outcomes include specific symptom domains, mental health (depression and anxiety), health care engagement, and app acceptability. Assessments will occur at baseline, 3, 6, 9, and 12 months, and linear mixed models with intent-to-treat principles will evaluate intervention effects over time. Results: The study was funded in 2024, institutional review board approval was received in mid-2024, and the study began recruitment on August 24, 2025. Overall, 6 participants have been enrolled, with 2 in the SAFE-CaPS intervention and 4 in SC. Recruitment is expected to conclude in July 2027. Data analysis will occur in late 2027, with article preparation and publication planned for mid-2028 and early 2029. We hypothesize that SAFE CaPS participants will experience greater improvements in QoL, reduced symptom burden, and higher engagement in care compared to SC. Conclusions: This study will provide critical evidence on the feasibility, acceptability, and preliminary effectiveness of a culturally tailored mobile health intervention designed to enhance survivorship outcomes among Black survivors with CaP, informing future large-scale trials and real-world implementation. Trial Registration: ClinicalTrials.gov NCT06651359; https://clinicaltrials.gov/study/NCT06651359 International Registered Report Identifier (IRRID): PRR1-10.2196/81503

Background: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns. Objective: This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk factors, these analyses aim to improve risk stratification, treatment planning, and prognosis for overall colon cancer survival within subgroups. Methods: We conducted a retrospective analysis of colon cancer cases diagnosed between 2010 and 2022 (n=33,825), using Kentucky Cancer Registry data linked to mortality records, with approval from the University of Kentucky Institutional Review Board (#63067). We compared multiple predictive modeling techniques, including Cox proportional hazards, accelerated failure time models, Extreme Gradient Boosting, random survival forests, least absolute shrinkage and selection operator (LASSO), and elastic net regression, to estimate survival probabilities. The Kaplan-Meier method provided baseline survival estimates, and multivariate models, including ML approaches, evaluated contributions of key risk factors. Model performance was compared across evaluation metrics such as the Brier score, concordance index, out-of-bag error, and Continuous Ranked Probability Score. Missing data were handled via multiple imputation, and leave-one-out cross-validation was applied to reduce overfitting. Results: The ML models identified key covariates influencing survival outcomes, such as age, treatment type, positive nodes, tumor stage, smoking, and comorbidities. In the overall model, patients who refused or received no treatment had a 3.24-fold higher risk of mortality compared to those who underwent surgery at primary and regional sites. Elevated mortality risk was also observed among smokers (24% higher than non-smokers) and Appalachian residents (7% higher than non-Appalachian residents). Our overall model achieved a concordance index of 0.8146, with strong discriminatory performance across subgroups, including early-age diagnosis (0.8175), late-age diagnosis (0.7841), Appalachia (0.8135), non-Appalachia (0.8126), White patients (0.8164), and Black patients (0.7881). The results highlight the strengths and limitations of each ML approach, with the random survival forest and LASSO models outperforming traditional methods such as the Cox model in prediction accuracy and model discrimination. Conclusions: Our study demonstrated the utility of ML in identifying risk factors associated with colon cancer survival, with positive lymph nodes, age at diagnosis, treatment received, clinical tumor size, tumor grade, smoking status, geographic region, and marital status emerging as dominant predictors across all statistical models. This comparative analysis offers valuable insights for clinical decision-making and prognosis, highlighting the potential of ML to identify risk factors specific to different subgroups, ultimately advancing personalized care for patients with colon cancer.

Introduction: Clinical and translational research (CTR) plays a vital role in improving health outcomes, but its success relies heavily on institutional support, infrastructure, and workforce capacity. This study aimed to explore the barriers, needs, and facilitators to conducting CTR in Oklahoma, highlighting both the strengths and gaps within the research ecosystem. Methods: A sequential, descriptive mixed-methods design was employed, combining survey data (n = 164) with four qualitative focus groups (n = 23 total participants). The survey assessed research infrastructure, funding, and workforce needs, while the focus groups explored researchers’ lived experiences and institutional challenges. Mixed-methods meta-inference approaches, such as convergence, complementarity, and explanatory integration, were used to identify overlapping and distinct patterns across data strands. Results: Key barriers included lack of protected research time (23.9%), limited pilot funding (15.3%), and administrative hurdles such as IRB delays. Researchers expressed a strong need for centralized tools to support networking, scientific writing, and data access. Qualitative findings revealed additional needs, such as bridge funding and mentorship, not fully captured in the survey. Facilitators included Oklahoma Shared Clinical and Translational Resources (OSCTR)-supported professional development and mentoring programs, though participants noted a heavy reliance on OSCTR as the primary support source, with few decentralized alternatives. Conclusions: While CTR infrastructure in Oklahoma has expanded, critical gaps remain in mentorship, data access, and institutional support. To build a more resilient and inclusive research environment, stakeholders should consider investing in decentralized systems, bridge funding, structured mentorship, and collaborative tools tailored to the state’s rural, tribal, and academic diversity. These findings may inform policy and strategic planning in Oklahoma and other underserved regions aiming to strengthen CTR capacity.

BACKGROUND Colon cancer is a leading cause of cancer-related deaths worldwide, and survival outcomes are influenced by a variety of factors, including biological traits, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and non-linear relationships between risk factors. Recently, machine learning techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns. OBJECTIVE This study compares several machine learning (ML) models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry (KCR). By identifying key risk factors, these analyses aims to improve risk stratification, treatment planning, and prognosis for overall colon cancer survival and within subgroups. METHODS This retrospective study examines registry data to compare a variety of predictive modeling techniques, including Cox proportional hazards, accelerated failure time (AFT) models, random survival forests, Lasso, and Elastic Net, which were applied to the registry data to predict survival probabilities. The models were evaluated based on their predictive accuracy, feature importance, and ability to handle complex risk factors. RESULTS Key covariates influencing survival outcomes, such as age, treatment type, positive nodes, tumor stage, smoking, and comorbidities, were identified as significant predictors of survival. The results highlight the strengths and limitations of each machine learning approach, with the random forest and Lasso models outperforming traditional methods in terms of prediction accuracy and identifying non-linear relationships. CONCLUSIONS This comparative analysis offers valuable insights for clinical decision-making and prognosis, highlighting the potential of machine learning to identify risk factors specific to different subgroups, ultimately advancing personalized care for colon cancer patients.

Objective: This scoping review explores the multifaceted experience of cancer survivorship in Sub‐Saharan Africa (SSA), with a focus on the post‐treatment phase. The primary objective is to examine the psychosocial, cultural, and economic factors that influence post‐treatment survivorship care and outcomes.Methods: A comprehensive literature review was conducted using databases such as Web of Science Core Collection to identify studies published between 2000 and 2023. Eligible studies focused on post‐treatment cancer survivorship in SSA. Data were extracted, analyzed, and synthesized to identify key themes and research gaps.Results: The review identified substantial psychological distress among survivors, including depression, anxiety, and insomnia, often exacerbated by financial toxicity and limited access to psychosocial support services. Cultural factors, such as spiritual beliefs, reliance on traditional healers, and cancer‐related stigma, influenced healthcare‐seeking behaviors and overall well‐being. Despite these challenges, social support networks, religiosity, and targeted psychosocial interventions improved emotional resilience and quality of life. However, major gaps remain, including insufficient integration of cultural beliefs into survivorship care, inadequate long‐term follow‐up (LTFU) programs, limited oncofertility support, and a lack of regionally diverse and longitudinal data.Conclusions: Cancer survivorship in SSA is shaped by intricate psychosocial, cultural, and economic dynamics that extend beyond clinical care. Addressing these challenges requires culturally sensitive, evidence‐based interventions, including financial counseling, spiritual care integration, and the establishment of structured LTFU programs. Additionally, expanding access to oncofertility support and integrating culturally relevant psychosocial services can further enhance survivorship outcomes. Strengthening collaboration between policymakers, healthcare providers, and researchers—through interdisciplinary task forces, psycho‐oncology workforce development, and community‐driven initiatives—is essential for improving post‐treatment outcomes and advancing cancer survivorship care in SSA.

We present findings from town hall meetings held with ethnically diverse Black prostate cancer (CaP) survivors in 2022 and 2023. Four town halls (total participants, N = 265) focused on diagnosis, treatment, psychosocial support, and overall survivorship. Moderated by a long-term CaP survivor, each session featured a panel that included medical providers (urologic oncologists and clinical psychologists), as well as CaP survivors of American, Caribbean, and African origin and their spouses. Surveys captured demographic data, clinical information, CaP-specific details (for survivors only), and perceptions of the town halls. The meetings highlighted three interrelated themesinfluencing decision-making: access to comprehensive information, the strength of support networks, and the availability of culturally competent care. These town halls revealed important educational and support opportunities for ethnically diverse Black men with CaP, promoting informed decision-making throughout the cancer journey and improving quality of life while reducing decisional regret during survivorship.