Sangaraju, Sai Lahari

e23274 Background: Treatment-related symptoms substantially impair quality of life (QoL) and drive unplanned acute care use in oncology. Although patient-reported digital symptom monitoring is widely studied, outcomes remain inconsistent. We examined how intervention design influences clinical impact. Methods: We conducted a scoping systematic review of prospective randomized trials and pragmatic real-world studies evaluating patient-reported digital symptom monitoring or management interventions in adult oncology populations. Eligible studies reported symptom burden, QoL and/or acute care utilization. Data were abstracted on cancer type, digital modality, triage approach, and direction of outcomes. Protocols and qualitative studies were reviewed for context but excluded from outcome synthesis. Results: Across studies involving solid tumors and hematologic malignancies, interventions used web-based portals, mobile applications or interactive voice response systems. A consistent pattern emerged across the evidence base: interventions that paired real-time symptom reporting with structured, rule-based triage and proactive clinician response demonstrated improvements in symptom burden and QoL across multiple settings. In contrast, passive monitoring or self-management alone showed limited or no benefit despite similar reporting intensity. Effects on acute care utilization were heterogeneous, with reductions most often seen in pragmatic, workflow-integrated implementations. Conclusions: The clinical value of patient-reported digital symptom monitoring lies not in symptom collection itself, but in how reported data are reviewed and acted upon by clinicians. Monitoring alone is insufficient. Implementation-ready models that embed actionable triage and clinician response workflows are essential to improve patient outcomes and reduce avoidable utilization. Intervention approaches and clinical outcomes of digital symptom monitoring in oncology. Intervention Approach Digital Modality Actionable Triage / Clinician Response Symptom Burden Quality of Life Acute Care Use Monitoring with proactive clinician response Portal / App / IVR Yes (rule-based, clinician-reviewed) Improved Improved Reduced / Mixed Monitoring with automated alerts only Portal / App Partial Mixed Mixed NR Passive monitoring or self-management App / Portal No No benefit No benefit NR Pragmatic, workflow-integrated models Mixed Yes + staffing workflows Improved Improved Reduced Heterogeneous or non-integrated implementations Mixed Variable Mixed Mixed Mixed Abbreviations: IVR = Interactive Voice Response; QoL = Quality of Life; NR = Not Reported.

e13683 Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but are frequently complicated by immune-related adverse events (irAEs), which may result in treatment interruption, hospitalization and mortality. Reliable pre-treatment tools to identify patients at higher risk for irAEs, particularly checkpoint inhibitor pneumonitis (CIP) remain limited. Artificial intelligence (AI)-based prediction models have emerged but their clinical performance and readiness for implementation are unclear. Methods: We systematically searched PubMed, Embase, Scopus and Web of Science (inception-January 2025) for studies developing AI-based models to predict irAEs or CIP in patients receiving ICIs. Eligible studies included radiomics, deep learning, natural language processing and clinical machine-learning approaches. Two reviewers independently extracted data on cancer type, toxicity endpoint, modality, algorithm, validation strategy and model performance. Study design and reporting characteristics were assessed. Due to substantial methodological heterogeneity, results were synthesized descriptively. Results: Fifteen studies met inclusion criteria, including mixed solid tumor cohorts and lung-predominant populations. Predictive targets included any-grade irAEs and organ-specific toxicities, most commonly pneumonitis. Model approaches ranged from clinical and electronic health record–based models to CT- and PET/CT-based radiomics and multimodal imaging-clinical frameworks. Reported discriminatory performance varied widely, with AUC values ranging from 0.64 to > 0.90 across model types. The most consistent performance was observed in deep learning and multimodal radiomics-clinical models for pneumonitis, with AUCs frequently between 0.75 and 0.92, whereas several clinical-only models demonstrated more modest performance. Most studies were retrospective and single-center, used heterogeneous irAE definitions and lacked standardized imaging protocols. External validation and calibration assessment were uncommon. Conclusions: AI-based models for predicting irAEs in ICI-treated patients demonstrate promising performance, particularly for pneumonitis using radiomics and multimodal approaches. However, current evidence is limited by heterogeneity, scarce external validation and uncertain generalizability. Prospective, multi-institutional validation and standardized outcome definitions are needed before AI-driven irAE risk prediction can be integrated into routine oncology practice to guide surveillance and patient counseling.