JMIR Cancer

Digital health portals are online platforms allowing individuals to access their personal information and communicate with health care providers. While digital health portals have been associated with improved health outcomes and more streamlined health care processes, their impact on individuals living with or beyond cancer remains underexplored.

Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the utilization of chemotherapy-targeted immunotherapy from a postoperative adjuvant to a preoperative neoadjuvant approach in select cases, resulting in enhanced survival outcomes. A pathological complete response (pCR) is a critical prognostic marker, with higher pCR rates linked to improved overall and disease-free survival.

Patients with cancer and cancer survivors often suffer from multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predominantly focused on cancers in isolation. There has been growing interest in machine-learning techniques for use in cancer studies. However, these methods have not been applied in the context of supportive care for patients with cancer who have multimorbidity. Moreover, few studies have investigated the associations between these clusters and mortality outcomes.

In the modern era, the use of technology can substantially impact care access. Despite the extent of its influence on several chronic medical conditions related to the heart, lungs, and others, the relationship between one’s access to digital resources and oncologic conditions has been seldom investigated in select pathologies among gastrointestinal and head-neck regions. However, studies into the influence of this “digital inequity” on other cancers pertaining to the nose and paranasal-sinuses (NPSC) have yet to be performed. This remains in stark contrast to the extent of large-data approaches assessing the impact of traditional social determinants/drivers of health (SDoH), such as factors related to one’s socioeconomic status, minoritized race/ethnicity, and housing-transportation status, on prognostic and treatment outcomes.

Immune checkpoint inhibitors represent an effective therapeutic approach for advanced gastric cancer. Their efficacy largely depends on the status of tumor biomarkers including HER2, PD-L1 (CPS ≥1), and MSI-H. To non-invasively evaluate these biomarkers, researchers have developed radiomic models for individual biomarker prediction. However, in clinical practice, holistic prediction of these biomarkers as an integrated system is more efficient. Currently, the feasibility of implementing radiomics-based comprehensive biomarker prediction remains unclear, requiring further investigation.

Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly for small tumors, until diagnostic surgery is performed.

Advances in therapies have significantly improved the outcomes of patients with cancer. However, multidimensional symptoms negatively impact patients’ quality of life. Traditional symptom analysis methods fail to capture the dynamic and interactive nature of these symptoms, limiting progress in supportive care. Network analysis (NA) is a promising method to evaluate complex medical situations.

Breast cancer survivors have increased cardiovascular risk compared to those without cancer history. Cardiovascular disease is the most common cause of death in breast cancer survivors. Cardiovascular risk in breast cancer survivors is impacted by both cancer treatment-associated effects and overlap in risk factors for breast cancer and cardiovascular disease. Strategies to improve screening for and management of cardiovascular disease in breast cancer survivors are needed to improve delivery of survivorship care.

Blood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualised cancer risk identification, as relevant trends may be confined within the normal range.

Home-based hospice care offers terminal cancer patients the comfort of receiving care in a familiar environment while enabling family members to provide personalised support. Despite the critical role families play, the literature remains underexplored in terms of their experiences, needs, and perceptions. A robust qualitative synthesis is needed to inform improvements in palliative care services.

Colorectal cancer (CRC) is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective prevention and treatment, particularly for patients below the recommended screening age.

Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially for disadvantaged families facing limited health literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced for colorectal cancer (CRC) patients, who often experience distressing symptoms and struggle with educational materials due to complex jargon, fatigue, or reading level mismatches. To address these issues, we developed and assessed the feasibility of a digital health platform, CRCWeb, to improve the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers facing limited health literacy or low income. CRCWeb was developed through a stakeholder-centered participatory design approach. Two-phase semi-structured interviews with patients, caregivers, and oncology experts informed the iterative design process. From the interviews, we developed the following five key design principles: user-friendly navigation, multimedia integration, concise and clear content, enhanced accessibility for individuals with vision and reading disabilities, and scalability for future content expansion. Initial feedback from iterative stakeholder engagements confirmed high user satisfaction, with participants rating CRCWeb an average of 3.98 out of 5 on the post-intervention survey. Additionally, using GenAI tools, including large language models (LLMs) like ChatGPT and multimedia generation tools such as Pictory, complex healthcare guidelines were transformed into concise, easily comprehensible multimedia content, and made accessible through CRCWeb. User engagement was notably higher among disadvantaged participants with limited health literacy or low income, who logged into the platform 2.52 times more frequently than non-disadvantaged participants. The structured development approach of CRCWeb demonstrates that GenAI-powered multimedia interventions can effectively address healthcare accessibility barriers faced by disadvantaged CRC patients and caregivers with limited health literacy or low income. This structured approach highlights how digital innovations can enhance healthcare.