JMIR Cancer

Survivors of childhood cancer are at risk of medical, psychological, and social late effects. To screen for their risks, receipt of consistent, cancer-specific follow-up care is crucial. However, <50% of survivors attend their aftercare, and only 35% of them recognize that they could have a serious health problem. The use of mobile health (mHealth) is a promising form of intervention to educate, connect, and empower survivors of childhood cancer on the importance of follow-up care.

Digital interventions have been increasingly applied in multidisciplinary care plans to improve medication adherence to oral systemic anti-cancer therapy (SACTs), the crucial life-saving treatments for many cancers. However, there is still a lack of consensus on the efficacy of those digital interventions.

Background: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence-powered chatbot, has signified a potential paradigm shift in how cancer patients can access vast amounts of medical information, including insights into radiotherapy. However, the quality of the information provided by ChatGPT remains unclear. This is particularly significant given the general public's limited knowledge of this treatment and concerns about its possible side effects. Furthermore, evaluating the quality of responses is crucial, as misinformation can foster a false sense of knowledge and security, lead to noncompliance, and result in delay in receiving appropriate treatment.

Oncology patients often face complex choices between treatment regimens with different risk-benefit ratios. The 4D PICTURE (Producing Improved Cancer Outcomes Through User-Centered Research) project aims to support patients, their families, and clinicians with these complex decisions by developing data-driven decision support tools (DSTs) for patients with breast cancer, prostate cancer, and melanoma as part of care path redesign using a methodology called MetroMapping. There are myriad ethical issues to consider as the project will create data-driven prognostic models and develop conversation tools using artificial intelligence while including patient perspectives by setting up boards of experiential experts in 8 different countries. This paper aims to review the key ethical challenges related to the design and development of DSTs in oncology. To explore the ethics of DSTs in cancer care, the project adopted the Embedded Ethics approach—embedding ethicists into research teams to sensitize team members to ethical aspects and assist in reflecting on those aspects throughout the project. We conducted what we call an embedded review of the project drawing from key literature on topics related to the different work packages of the 4D PICTURE project, whereas the analysis was an iterative process involving discussions with researchers in the project. Our review identified 13 key ethical challenges related to the development of DSTs and the redesigning of care paths for more personalized cancer care. Several ethical aspects were related to general potential issues of data bias and privacy but prompted specific research questions, for instance, about the inclusion of certain demographic variables in models. Design methodology in the 4D PICTURE project can provide insights related to design justice, a novel consideration in health care DSTs. Ethical points of attention related to health care policy, such as cost-effectiveness, financial sustainability, and environmental impact, were also identified, along with challenges in the research process itself, emphasizing the importance of epistemic justice, the role of embedded ethicists, and psychological safety. This viewpoint highlights ethical aspects previously neglected in the digital health ethics literature and zooms in on real-world challenges in an ongoing project. It underscores the need for researchers and leaders in data-driven medical research projects to address ethical challenges beyond the scientific core of the project. More generally, our tailored review approach provides a model for embedding ethics into large data-driven oncology research projects from the start, which helps ensure that technological innovations are designed and developed in an appropriate and patient-centered manner.

Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can improve survival outcomes for gastrointestinal (GI) cancer patients with peritoneal disease (PD). This patient population is at high risk of malnutrition and CRS-HIPEC can further negatively impact patients’ nutritional status. However, there has been limited testing of nutritional interventions for this patient population.

Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing symptom burdens from cancer and its treatments. Large language models (LLMs) offer a promising avenue for generating concise, linguistically appropriate, and accessible educational materials tailored to these populations. However, there is limited research evaluating how effectively LLMs perform in creating targeted content for individuals with diverse literacy and language needs.

In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination.

Natural language processing systems for data extraction from unstructured clinical text requires expert-driven input for labelled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records useful for accelerating cancer clinical research and informing oncology care.

Cancer is the second leading cause of death in the United States. Compelling evidence shows screening detects colorectal cancer (CRC) at earlier stages and prevents the development of CRC through removal of pre-cancerous polyps. Healthy People 2030 goal for CRC screening is 68.3% but only 36.5% of Missouri Federally Qualified Health Center Patients aged 50-75 years are up-to-date on CRC screening. For average risk patients, there are three commonly used screening tests used in the United States—two types of stool tests collected at home (Fecal Immunochemical Test (FIT)/Fecal Occult Blood Test (FOBT) and Cologuard® (FIT-DNA)) and colonoscopy completed at procedural centers.

Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision making. However, current PLS formats often exceed the readability requirements for the general population. Cost-effective, more automated solutions to this problem are still lacking.

Cancers of the bladder, kidney, and prostate are the 3 major genitourinary cancers that significantly contribute to the global burden of disease (GBD) and continue to show increasing rates of morbidity and mortality worldwide. In mainland China, understanding the cancer burden on patients and their families is crucial; however, public awareness and concerns about these cancers, particularly from the patient’s perspective, remain predominantly focused on financial costs. A more comprehensive exploration of their needs and concerns has yet to be fully addressed.

The relationship between assisted reproductive technology (ART) and childhood cancer risk has been widely debated. Previous meta-analyses did not adequately account for the impact of infertility, and this study addresses this gap.