JMIR Cancer

Barriers to eHealth usage include lack of technological infrastructure, resistance to change, and inequities in access. However, patterns of access and use of eHealth tools in people being treated for cancer have not been fully described in the literature.

Metastatic cancer remains one of the leading causes of cancer-related mortality worldwide. Yet, the prediction of survivability in this population remains limited by heterogeneous clinical presentations and high-dimensional molecular features. Advances in machine learning (ML) provide an opportunity to integrate diverse patient- and tumor-level factors into explainable predictive ML models. Leveraging large real-world datasets and modern ML techniques can enable improved risk stratification and precision oncology.

Online cancer communities provide young adult (YA) cancer survivors with access to informational and emotional support that may not be available in traditional care settings. While these platforms offer vital connection opportunities, the unique pathways YA survivors take to find online communities and the challenges they encounter remain underexplored.

African American caregivers are more likely to be sole unpaid caregivers, spend more hours on caregiving tasks, and receive less external support compared to White caregivers; yet, limited research focuses on their specific needs. Even less attention has been paid to health care provider perspectives on how to better support this population, despite providers’ critical role in connecting caregivers to resources and implementing systems-level changes.

In recent years, the global incidence of thyroid cancer has been increasing.

Cancer patients often face significant financial challenges, known as financial toxicity (FT), which is associated with reduced quality of life. Patients with hematologic malignancies are especially vulnerable due to intensive and prolonged treatments, frequent hospital visits, and a high risk of complications. While FT affects many in the general population, it is particularly severe among racial and ethnic minorities, especially those below the poverty line. To our knowledge, no studies have specifically examined FT in this vulnerable group in the United States.

Adults with brain tumors learn to navigate unpredictable physical and psychological symptoms along with the possibilities of tumor recurrence. As a result, they tend to become resilient to confronting profound uncertainty and actively employ coping strategies. Yet, the impact of resilience on coping strategies among people with brain tumors has not been fully explored.

Pain is common among patients with advanced cancer and is often inadequately controlled. Opioids are central to treatment; yet, self-management is challenging, and clinicians lack scalable tools to monitor and support patients between visits.

Young adult survivors of childhood cancer are at risk for late and long-term effects from their treatment, and less than 1 in 5 obtain risk-based care in adulthood. Transitioning young adult survivors from pediatric, parent-driven care to adult, self-driven care is a challenging process during which young adults face multiple barriers. Intervening during this period may facilitate better transition readiness. For this purpose, we previously developed the Managing Your Health (MYH) web-based intervention, which showed initial feasibility and acceptability; however, young adult participants wanted to access the intervention through a mobile app.

Cancer is a leading cause of death worldwide. Early detection through screening, diagnosis, and effective management can reduce cancer mortality. Risk assessment is crucial for improving outcomes by identifying high-risk individuals based on family history, genetics, lifestyle, and environment. Such targeted screening enhances accuracy and resource efficiency. However, the complex nature of oncology data—which includes clinical observations, lab results, radiology images, treatment regimens, and genetic information—presents significant challenges for data interoperability and exchange.

Personalized nutritional management during cancer remains challenging in clinical practice. The development of an electronic patient-reported outcome platform (ePROM) provides novel opportunities.

Colon cancer is a leading cause of cancer-related deaths worldwide, and survival outcomes are influenced by a variety of factors, including 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 non-linear 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.