JMIR Cancer

Cervical nodal metastasis remains one of the strongest determinants of treatment planning and prognosis in oral squamous cell carcinoma. Histopathology remains the reference standard, but it does not provide immediate information at the point of surgical specimen assessment. Elastic scattering spectroscopy (ESS) offers rapid, label-free optical interrogation of tissue microstructure, and may support ex vivo nodal assessment.

Although effective, current chimeric antigen receptor T-cell (CAR-T) production methods—centralized, manual, and complex—are cost-intensive, time-consuming, and prone to variability. AIDPATH (AI-Powered Decentralized Production for Advanced Therapies in the Hospital) proposes a decentralized, automated alternative that integrates patient-specific data; optimizes resource use; and potentially improves cell viability, manufacturing efficiency, and patient outcomes.

Thyroid cancer is often labeled a “good cancer” due to high survival rates. However, many patients diagnosed with differentiated thyroid cancer (DTC) treated with radioactive iodine (RAI) experience short- and long-term symptoms as a result of treatment. These include salivary dysfunction, dry mouth, dry eyes, and nausea. Despite these challenges, patients report limited knowledge and unmet informational needs regarding RAI symptoms and symptom management. Evidence-based, patient-centered education to support symptom management during DTC survivorship is needed. Our team developed Radioactive Iodine Support (RAI Support), a prototype of a 4-week web-based intervention for RAI symptom management among survivors of DTC.

Preoperative nodal assessment in colorectal cancer (CRC) remains imperfect, and uncertainty regarding pathological lymph node metastasis (LNM) may persist despite conventional imaging. Machine learning using routinely available preoperative information may provide complementary patient-level risk estimates without requiring radiomic feature extraction or molecular testing.

Progression-free survival (PFS) is a critical end point in oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited.

Promotional activities, such as social marketing approaches, are important for raising cancer awareness and increasing breast cancer screening participation. Such interventions are designed to facilitate change and maintain positive health behaviors at both the community and society level. With the growing use of these promotional activities in public health, it is pertinent to map available studies to inform future evaluations and the development of effective interventions.

Informal caregivers play a fundamental role in the diagnosis, treatment, and care received by their family members. Caring for someone with dementia is particularly burdensome and can have negative effects on caregivers’ health. Despite caregivers being widely recognized as vulnerable, the psychosocial needs of caregivers who are managing cancer themselves remain unknown.

Patients with newly diagnosed gynecologic cancers often seek information online, but the quality of available resources may be inconsistent. Although GPT-4 may offer an alternative to traditional internet search engines, its performance remains largely under-studied in gynecologic oncology.

People living with advanced cancer experience more frequent and severe symptoms than those with early-stage disease. Common, distressing symptoms include sleep difficulties, worry and uncertainty, fatigue, and depression. Cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT) effectively manage these symptoms but are often too time intensive for patients with multiple appointments, limited energy, and competing priorities. Brief, mobile health (mHealth) interventions provide an accessible alternative, particularly for rural patients with limited access to palliative or psychosocial oncology services.

Cancer diagnosis depends on data from radiology, digital pathology, molecular profiling, laboratory testing, and longitudinal clinical records. AI performs well in selected tasks, but most systems remain narrow and disconnected from the iterative reasoning required in oncology. This Viewpoint defines an AI agent as a feedback-driven system that maintains task state, selects among governed tools, observes results, and revises its plan under explicit safety constraints. This definition separates agents from multimodal foundation models, retrieval-augmented generation, and fixed workflow automation. We organize the discussion across multimodal data collection, preprocessing, fusion and representation learning, and diagnostic decision support. We distinguished agent-level evidence, component- or infrastructure-level evidence, and prospective propositions throughout. Clinical translation will require resilient failure handling, guideline version control, prospective evaluation, computational and workflow feasibility, and clinician authority over final decisions. The near-term opportunity is therefore transparent and traceable clinical decision support rather than autonomous cancer diagnosis.

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