PURPOSE:NRG/RTOG 1016 was a phase III randomized noninferiority de-escalation trial comparing cetuximab versus cisplatin, concurrent with accelerated radiation 70 Gy/6 weeks, in p16+ oropharyngeal cancer. Quality of life (QOL) was a secondary endpoint. METHODS AND MATERIALS:Eligible/consenting patients among the first 400 entered completed the EORTC QLQ-C30/H&N35 at baseline, end of treatment, 3, 6, and 12 months posttreatment, to provide 90% power to detect an effect size of 0.5 in the between-arm change in QOL scores from baseline to 6 months. We report completion, responsiveness, and patterns over time across domains between arms, considering a difference of >10 points as clinically significant. RESULTS:Consent to the QOL substudy was 91%, with analyzable data in 375 patients. No significant differences in patient/tumor characteristics were found by QOL participation status. Completion at the 5 timepoints did not differ by arm (intensity modulated radiation therapy [IMRT] + cisplatin/cetuximab) and was: 92/94%, 74/77%, 76/81%, 76/81%, and 73/74%. No significant difference was observed between arms for the 6-month change from baseline on any domain. At the end of treatment, all domains showed statistically and clinically significant mean worsening across both arms except emotional functioning, dyspnea, financial difficulties, diarrhea, and teeth. By 6 months, drops >10 points remained for: senses, social eating, opening mouth, dry mouth, sticky saliva; and at 12 months for senses, trouble with social eating, opening mouth, dry mouth, sticky saliva, pain killers, and weight gain. Pain killer reduced at both 6 and 12 months. CONCLUSIONS:Although replacing concurrent IMRT + cisplatin with IMRT + cetuximab did not improve global health status or swallowing at 6 months, this study supports the responsiveness of the EORTC QLQ-C30/H&N35 to the effects of IMRT + systemic therapy for oropharyngeal cancer. Dry mouth, sticky saliva, and senses showed large, significant, and persistent impairment, whereas domains related to eating (swallowing, appetite, nutritional supplements, social eating, and weight loss) did not show sustained significant impairment in this study.

1627 Background: A text-message based artificial intelligence nutrition platform called “Ina” was previously developed and shown to be equivalent to human dietitians in providing nutritional recommendations to patients with cancer. Once registered on the platform, patient users exchange conversational texts with and receive tailored dietary recommendations and recipes from Ina. We investigated whether the emotional sentiment of SMS messages can act as a proactive predictor of program-related Quality of Life (QoL) and program adherence. Methods: QoL was captured via a 1-5 Likert scale (“Has using Ina improved your quality of life?”) survey, and program adherence was captured by a binary response to a question (“Have you used any of Ina’s suggested tips?”). We extracted SMS messages within a 60-day window prior to each survey and question. Sentiment was measured using the Jockers-Rinker lexicon via the validated sentimentr package; scores were calculated as a weighted average ranging from -1 (negative sentiment) to +1 (positive sentiment). Linear mixed-effects models (LMM) were used to quantify the relationship between pre-survey message sentiment and program QoL/adherence outcomes, adjusting for total number of surveys completed and user’s unique baseline sentiment. Results: Between October 2019 and January 2026, 14,700 unique text messages were analyzed from 540 unique patient users of the platform (mean age: 51± 20 years; 91% female). Cancer types included breast (40%), ovarian (24%), bladder (16%), lung (13%), and colon (7%); metastases were present among 34%. For every one-point improvement in digital sentiment, users experienced a 14% improvement in subsequent program-related QoL (𝛽= 0.72, p < 0.001). Furthermore, there was a trend toward an association between higher positive digital sentiment and subsequent reported adherence to nutritional recommendations (𝛽 = 0.12, p = 0.097). Conclusions: Sentiment analysis of unstructured SMS messages successfully identified subtle shifts in digital tone that precede structured assessments. This 'leading indicator' of sentiment could enable proactive supportive care and provider escalation for timely triage and intervention. Given rapid symptomatic deterioration in oncology that often leads to non-adherence, text-based sentiment analysis could enable early detection of declining engagement to support improved quality of life and long-term outcomes.

There is widespread interest among patients, clinicians, regulators and other constituents in post-treatment patient-reported cancer data. Side effect bother is a patient-reported outcome (PRO) that can capture an important aspect of tolerability. In this study, we examined side effect bother at cancer treatment discontinuation and post-discontinuation in commercial cancer trials. We sought to understand completion rates, the extent of bother and its association with other PROs. Data were evaluated from three trials in patients with solid tumours (renal cell carcinoma and breast cancer). Side effect bother was measured with the Functional Assessment of Chronic Illness Therapy (FACIT) GP5 item. Symptom items were drawn from FACIT and function items were drawn from the EQ-5D-3L. FACIT items, including the GP5, are on a 0–4 scale (higher = worse symptoms/bother), and were dichotomised as 0–1 (“low”) vs 2–4 (“moderate”). EQ-5D-3L items were characterised as no problems (1) and some problems (2–3). Descriptive and correlation analyses were conducted separately for each trial. Among patients who received treatment, completion rates at discontinuation for most items were at least 70

Patient-reported outcomes (PROs) offer a non-invasive, low-cost way to capture patients' symptoms, functioning, and quality of life. Yet, their potential as early indicators of tumor size, recurrence/disease progression, and survival remains unclear. We retrospectively analyzed 445,239 longitudinal PRO entries from 2738 patients with breast cancer, pooled from four clinical trials, including both early- and late-stage disease, covering 15 PRO measures. Among patients with radiographically confirmed recurrence or disease progression, 89.2% experienced at least one PRO deterioration prior to relapse detection (85 vs. 706 days), indicating that PROs often worsen before imaging-confirmed relapse. Cox proportional hazards models showed that PRO deterioration was significantly associated with metastatic sites, tumor size, and survival. Appetite loss was most strongly correlated with tumor size, while pain and diarrhea were the most prognostic symptoms for overall survival (OS) and progression-free survival (PFS). Gradient boosting models further showed that combining PRO deterioration times across all subscales with PFS best predicted OS, correctly classifying survival outcomes in over 93% of cases (AUC - ROC = 0.932), outperforming models using PROs alone (AUC - ROC = 0.806) or PFS alone (AUC - ROC = 0.88). This indicates that integrating PROs with PFS enhances the prediction of OS, providing a more powerful approach than using either measure alone. These findings suggest that PROs can serve as early, complementary predictors of recurrence, disease progression, and survival, supporting their use as patient-centered biomarkers in breast cancer management. Our findings align with FDA and EMA efforts to integrate PROs into oncology endpoints, supporting more patient-centered regulatory evaluation.

1571 Background: Remote symptom monitoring with electronic patient-reported outcomes (PROs) during cancer care improves quality of life, reduces acute care events, lengthens time on treatment, and can improve survival. US oncology practices are increasingly implementing PROs, but barriers to implementation remain. To facilitate uptake of PROs, the OncoPRO Initiative was established as a national PRO learning collaborative with funding from PCORI, in partnership with ASCO, the American Cancer Society, the PROTEUS Consortium, federal agencies, practice networks, and the major EHR and PRO software vendors. Oncology practices/health systems may join OncoPRO at no cost if they are committed to implementing PROs and can submit implementation metrics for monitoring progress. Practices are sorted into “affinity groups” by EHR and PRO software systems. Affinity groups meet monthly, led by coaches from the ASCO, PRO experts, and representatives of respective technology companies. Affinity groups follow a curriculum of key topics in PRO implementation, exchange training materials and information about facilitators and experiences, and provide feedback to software vendors. Little is known on how such learning collaboratives can support PRO growth. Methods: Implementation measures and characteristics were identified to quantify progress including the total number of practices joining, the phase of these practices (early implementation, scale-up, maintenance), topic areas for support requested by practices, number of personnel trained for PRO at practices, number of patients enrolled in PRO at practices, and proportion of enrolled patients completing PRO surveys. Data were collected via direct EHR data transfers and surveys. Results: Since 3/1/2024, 23 community and academic oncology practices/health systems have joined OncoPRO, across 23 different states, among which 13 practices were in early implementation, 7 in scale-up, and 5 in maintenance. These practices were sorted into four monthly affinity groups, in which the most commonly requested topics were: personnel roles/responsibilities; workflow; patient/clinician engagement; key performance metrics; technology customization; integration with quality programs; symptom management pathways; and billing/coding. Practice participation in monthly meetings has been 100%. Data are available from 12 initial practices, which collectively enrolled 47,052 patients over their initial 15 months of the program, with 24,566 patients (52%) completing at least one PRO survey. Conclusions: The OncoPRO learning collaborative is successfully supporting practices across the US to implement PROs, with strong practice engagement and high patient participation.

1632 Background: Remote symptom monitoring (RSM) programs are currently being implemented across oncology practices nationwide; however, the impact of RSM on costs by payor has been understudied. Methods: This is a secondary analysis of a hybrid implementation-effectiveness trial of electronic, patient reported outcome-based RSM for patients with cancer initiating systemic therapy (May 2021-May 2024). Differences in costs for healthcare services for RSM-enrolled patients were compared to historical controls. Outcomes included overall, monthly, payor-, and service-specific costs of care received at 3 and 6 months after RSM-enrollment date or initiation of systemic therapy for controls. Adjusted generalized linear models estimated predicted mean costs, mean cost ratios (MR) and 95% confidence intervals (CIs) for RSM-enrolled patients versus controls. Results: Patients receiving RSM (n=968) were 25% Black, 44% privately insured, and 27% living in a highly disadvantaged neighborhood. Historical controls (n=3,488) were demographically similar. Though non-statistically significant, RSM enrolled patients had 5% lower mean costs 3 months post-index date compared to historical controls (MR 0.95, 95% CI 0.78-1.16), translating to an estimated cost savings of $1,347 per patient (95% CI -$3,596, $6,290). Medicare Fee-for-Service (FFS) beneficiaries showed the greatest, though non-statistically significant, cost reductions, with RSM FFS beneficiaries having 10% lower costs than FFS controls (MR 0.90, 95% CI 0.67-1.19) at 3 months post-index date. Though non-statically significant, RSM-enrolled patients had 23% lower costs for radiation, 14% lower inpatient costs, 9% lower costs for labs, scans, or tests, and 2% lower outpatient costs compared to controls at 3 months post-index date. At one-month post-index date, RSM enrolled patients had statistically significantly lower payor costs compared to controls ($11,849 [95% CI $9,364-$14,335] vs. $15,706 [$14,291-$17,121]; p=.004). Costs for RSM enrolled patients and controls were similar two to six-months post-index date. Conclusions: We observed payor cost savings of $1,347 in the 3 months of RSM enrollment compared to controls. As the largest payor cost differences were seen for Medicare FFS beneficiaries, our results suggest RSM may address a gap in the provision of care coordination to patients not offered these services through their insurer. Cost savings were most prominent within the first month of treatment initiation; thus, RSM may aid in reducing acute care needs and optimizing resource utilization for patients with cancer. Our results support future research in potential risk-stratification methods to improve RSM engagement and delivery to reduce costs and improve outcomes for patients with cancer.

PURPOSEThere is growing scientific interest in incorporating patient-reported outcomes (PROs) in early phase dose-finding oncology trials (DFOTs) to assess tolerability, inform dose selection, and guide later stage trial design. However, research indicates that PRO objectives in DFOTs are often unclear. The Incorporating Patient-Reported Outcomes in Dose-Finding Trials-Research Objectives Recommendations (OPTIMISE-ROR) project was established to support trialists to effectively incorporate PROs into DFOTs.METHODSUsing the Enhancing Quality and Transparency of Health Research (EQUATOR) Network's methodological framework, guideline development included the following: (1) a methodological review of published DFOTs incorporating PROs; (2) candidate item generation, refined through expert consultation; (3) a two-round international multistakeholder Delphi survey (N = 109 in Round 1 [October 2024]; N = 96 in Round 2 [December 2024]); and (4) an independently chaired virtual consensus meeting (N = 31; January 2025) where multidisciplinary, international experts reviewed and voted to finalize items for inclusion.RESULTSConsensus was reached on six recommendations emphasizing three core PRO tolerability concepts: overall side effect impact, symptomatic adverse events, and overall health-related quality of life. The integration of PROs to inform final dose recommendations in dose escalation and optimization trials should be considered, regardless of trial design. The recommendations highlight the importance of PRO data analysis over time and across dose levels, defining PRO research objectives as descriptive or statistically powered, and assessing PRO-related end points to guide end point selection for subsequent studies.CONCLUSIONThis foundational guidance outlines key PRO research objectives in DFOTs. By facilitating the systematic integration of PROs, this guidance supports the utilization of patient-centered evidence for the tolerability and efficacy assessment of therapies to inform dose escalation, optimization, and regulatory evaluation-ultimately contributing to the development of safer, more effective therapies.

PURPOSE:Remote symptom monitoring using patient-reported outcomes (PROs) has been shown to improve symptom control and physical function among patients with advanced cancer. However, it is unclear whether these benefits are similar across demographic groups. This exploratory analysis of the PRO-TECT trial examined whether the effects of electronic symptom monitoring varied by race, age, sex, and educational attainment. METHODS:PRO-TECT was a cluster randomized trial conducted across 52 US community oncology practices comparing weekly electronic symptom monitoring with usual care (UC). Adult patients with metastatic solid tumors receiving systemic therapy were enrolled (N = 1,191). Participants in the PRO arm completed weekly symptom surveys including the PRO-CTCAE, triggering alerts to the care team for severe or worsening symptoms. Patients chose whether to complete weekly PROs online or via telephone-based interactive voice response not requiring Internet access. Outcomes were measured using the European Organisation for Research and Treatment of Cancer QLQ-C30 symptom control and physical function scales. Subgroup analyses were performed by race (Black v White), age (<65 v ≥65 years), sex (male v female), and educational attainment (≤high school v >high school). RESULTS:From baseline to 3 months, PRO participants showed greater improvements in symptom control (+2.37 v -0.20, P = .002) and physical function (+1.54 v -0.93, P = .02) versus UC. Benefits were most pronounced among younger, female, Black, and less educated participants. Black participants in the PRO arm were more likely to report that symptom reporting made them feel more in control of their care compared with White participants. CONCLUSION:Electronic symptom monitoring improved quality-of-life outcomes overall, including among groups who have historically experienced higher symptom burden or barriers to communication. Remote PRO systems may represent an equitable strategy to enhance cancer care delivery.

BACKGROUND:There is growing recognition of the importance of patient-reported tolerability in complementing traditional clinician-reported safety evaluation of cancer therapies. Recent regulatory guidance listed the evaluation of overall side effect impact as a core patient-reported outcome in oncology clinical trials. A single item ('GP5') that asks about side effect bother is included in the Functional Assessment of Chronic Illness Therapy and has been used to capture overall side effect impact. This paper sought to expand the evidence base for GP5 by examining its association with clinician-reported treatment-emergent adverse events and patient-reported global health. METHODS:We examined six commercial cancer clinical trials that collected GP5. The patient population was drawn from the safety population and the analysis focused on the first on-treatment assessment. Clinician-reported adverse events were classified as symptomatic if such adverse events were considered amenable to patient self-reporting (e.g. nausea). Chi-square tests and Pearson's correlation were used to examine associations. We considered adverse event grade and frequency, both for symptomatic adverse events and any type of adverse events. Global health was measured using the visual analogue scale of the EuroQol-5 Dimensions-3 Levels measure. 'Moderate-severe' bother was characterised as scores of 2-4 on a 0-4 point scale for GP5, and 'severe' bother was characterised as scores of 3-4. Analyses were conducted separately for each trial. RESULTS:Data from 3,557 patients were included. Across the trials, most (71.7%-94.2%) patients had an adverse event of some kind, but fewer (17.1%-44.4%) had an adverse event of grade 3 or higher. In general, fewer than 50% of patients (20.6%-44.2%) reported moderate-severe bother and 5.8%-17.% reported severe bother. There were consistent, albeit not always statistically significant, associations between GP5 and adverse events, and GP5/global health correlations ranged from -0.17 to -0.41. DISCUSSION:GP5 is associated with both clinician- and patient-reported symptoms, suggesting its validity and usefulness as part of comprehensive tolerability assessment of cancer trials.

Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic and predictive value remains underutilized due to challenges in interpreting these highly variable and noisy PRO data. Here, we developed a quantitative modeling framework integrating nonlinear mixed-effects (NLME) and item response theory (IRT) to characterize symptom-level PRO trajectories and transform them into clinically actionable predictors. Using longitudinal PRO data from 589 patients with metastatic cancers in the PRO-TECT trial, we modeled 332,920 symptom responses to estimate patient-specific PRO trajectory parameters while accounting for variability and noise. IRT-NLME modeling captured heterogeneous symptom-level PRO dynamics and is more informative than modeling with composite PRO scores. PRO trajectory parameters were strongly associated with overall survival, acute care utilization, and treatment modifications. Machine learning models leveraging these parameters achieved robust prediction of survival (AUC-ROC 0.80) and retained prognostic performance using the first 30-180 days of PRO observations, with AUCs of 0.69-0.78. Similar predictive performance was observed for hospitalization (AUC 0.75), emergency department visit (AUC 0.65), treatment discontinuation (AUC 0.71), and dose reduction (AUC 0.67). These findings demonstrate that longitudinal PRO trajectories can serve as early, patient-centered biomarkers of clinical risk. By converting complex symptom data into interpretable and predictive metrics, this quantitative framework provides a practical pathway to integrate the patient voice into clinical decision-making and advance precision oncology. ClinicalTrials.gov registration: [NCT03249090][1] ### Competing Interest Statement B.M may own stock in Novartis. J.H.H is a current employee of Pfizer and may own stock in Pfizer. L.I.W receives institutional research funding from the National Cancer Institute and previously received personal fees from Celgene/Bristol Myers Squibb as a member of the Scientific Steering Committee for the Connect Multiple Myeloma patient registry. W.A.W. has received institutional research funding from Genentech; receives consulting fees from Teladoc Health and Lantern Health; holds equity in Koneksa Health; and has a leadership position in the American Society of Hematology Research Collaborative. E.B. receives institutional research funding from the National Cancer Institute and from the Patient-Centered Outcomes Research Institute, and personal fees for scientific advising from Research Triangle Institute, Thyme Care, N-Power Medicine, Resilience Health, Canopy Care, Savor Health, and Navigating Cancer. ### Clinical Trial NCT03249090 ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data used in this analysis are fully de-identified individual participant data for secondary analysis. The study was granted an Institutional Review Board (IRB) approval by the University of North Carolina at Chapel Hill. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data generated in this study are available from the authors upon reasonable request. To support reproducibility of the methods, synthetic datasets are provided for readers to use in testing the models. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03249090&atom=%2Fmedrxiv%2Fearly%2F2026%2F05%2F03%2F2026.04.30.26352154.atom

11108 Background: Immune checkpoint inhibitors (ICI) are used to treat several solid tumors but can cause serious toxicities which may lead to early discontinuation of therapy. This study describes the effect of the Canopy electronic patient-reported outcomes (ePROs)-based remote therapeutic monitoring (RTM) platform on time to treatment discontinuation, acute care events, and steroid prescribing in patients receiving ICI therapy. Methods: We studied patients with metastatic cancer from four oncology clinics who started ICI treatment from Jan 1, 2024 to July 20, 2025. An RTM group was defined by submission of two ePRO reports in the first 45 days after first ICI treatment. The control group was never enrolled or did not submit ePRO reports within 45 days. Time to discontinuation (TTD) of ICI, acute care events, and steroid prescribing were assessed using propensity score weighting to account for potential differences in cohort characteristics; costs were taken as rate differences multiplied by average acute care event cost. TTD was assessed by Kaplan-Meier estimation, Cox proportional hazards modeling, and restricted mean survival time at 90 days (RMST). Descriptive statistics were taken about symptoms in the RTM group versus the control group. Results: The study included 363 patients using RTM and 1,199 in the control group. Lung cancer, melanoma, and kidney cancer were the most frequent diagnoses. In the RTM group time to treatment discontinuation was significantly greater and prescriptions for steroids were more frequent (Table 1). Additionally, RTM use was associated with about a 51% risk of hospitalization (95% CI: 0.29, 0.88, p: 0.015) and 82% risk of ER visits (95% CI: 0.49, 1.39, p: 0.458) compared to the control group. Estimated cost savings in hospitalization associated with RTM use were $11,733,933 per 1000 patients treated for one year. Symptoms were detected more frequently in the RTM group compared to the control group, including potentially ICI-related symptoms such as rash (18% vs 3.2%), diarrhea (28% vs 4.7%), and difficulty breathing (28% vs 4.8%). Conclusions: Active ePRO-based RTM with Canopy is associated with increased time to ICI discontinuation and decreased acute care events. Increased use of steroids in patients reporting symptoms suggesting ICI toxicity may explain the beneficial effect of RTM. Weighted analysis outcomes. Outcome RTM group Control group Risk or Hazard RatioRTM / Control (95% CI) Risk or RMST differenceRTM - Control (95% CI) Time to discontinuation Median: 7.5 mos(95% CI: 6.7, 8.8) Median: 4.1 mos(95% CI: 3.6, 4.6) 0.55(95% CI: 0.47 to 0.64, p < 0.001) 19 days (16, 22) Hospitalization 6.9% 14% 0.51 (0.29, 0.88, p: 0.015) -6.66 (-11.02, -2.13, p: 0.008) Emergency room visit 9.9% 12% 0.82 (0.49, 1.39, p: 0.458) -2.46(-8.05, 2.95, p: 0.366) Steroid use 53% 31% 1.69 (1.41, 2.02, p < 0.001) 21.67(13.90, 29.13, p < 0.001)

IntroductionLearning collaboratives are a widely used implementation strategy for supporting the spread of complex innovations, but little is known about how learning collaboratives develop and sustain over time. The OncoPRO initiative, a PCORI-funded national learning collaborative focused on implementing remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) in oncology, provides a unique opportunity to explore this process. By examining how OncoPRO fosters collaboration, shares strategies, and adapts to diverse sites, this study offers critical insights into both the development of learning collaboratives and their ability to support the long-term success of complex healthcare initiatives.MethodsThis study employed a multi-methods implementation science approach to examine the development and first year of the OncoPRO initiative. From conception through year 1 (March 2023-December 2024), OncoPRO provided support to 12 independent health systems. We identified cross-organizational barriers encountered during the development of a national learning collaborative, and the implementation strategies employed to address them, using field notes generated during all OncoPRO-related meetings, site-level communications, and site presentations during meetings. We systematically identified and categorized barriers and implementation strategies using the Consolidated Framework for Implementation Research (CFIR) 2.0 and the Expert Recommendations for Implementing Change (ERIC) frameworks. Strategies were then categorized into domains based on their alignment with each other and learning collaborative implementation components or processes.ResultsWe identified 29 overarching barriers (e.g., lack of best practices; clinician buy-in) that were addressed through 37 foundational implementation strategies relevant to developing and facilitating the learning collaborative. These implementation strategies were organized into six domains: building a multi-level foundation, engaging and onboarding implementation sites, building shared learning structures, supporting technical rollout, embedding feedback loops and quality monitoring, and stimulating demand for RSM and collaborative participation. Most barriers were addressed using multiple strategies, and individual strategies often targeted several barriers simultaneously. Broad strategies addressing multiple barriers (e.g. build a coalition; identify early adopters) were deployed early to develop a base for the collaborative. As the initiative matured, strategies targeting specific barriers (e.g. develop and implement quality monitoring systems) were added to support site-level operationalization and continuous improvement.ConclusionThis study describes our approach to building a national learning collaborative for ePRO-enabled RSM implementation in oncology, focused on the initial phase of implementation. It offers a case study and potential roadmap for others involved in the initial development of large-scale collaboratives for complex interventions. This descriptive process analysis lays the groundwork for future analyses of implementation variation and strategy effectiveness across participating health systems, and highlights how learning collaboratives can support the implementation of complex quality initiatives like RSM in oncology.

Introduction. The recent expansion of treatment options for older adults with blood cancers has increased the necessity for understanding patient preferences and values to inform treatment decision making. Currently, no values elicitation measures have been validated for clinical use in this population. Values-HM (Values elicitation measure for Hematologic Malignancies) is a novel values elicitation measure based on best–worst scaling. It was developed specifically for older adults with blood cancers involving multiple stakeholders including patients, caregivers, and clinicians. Objective. The objective of this study was to test the preliminary validity, reliability, and acceptability of this measure for clinical use based on US Food and Drug Administration and International Society for Pharmacoeconomics and Outcomes Research guidance. Results. Twenty-nine adults aged ≥60 y with newly diagnosed lymphoma, leukemia, myelodysplastic syndrome, and multiple myeloma were enrolled. Most patients were White (97%). Most patients (88%) felt that the measure was relevant to them, showed their real preferences (73%), and was acceptable to clarify their preferences (68%). The measure demonstrated discriminant validity of importance scores and convergent validity for most patients (77%) with a ranking exercise of the treatment values. Cognitive interviews with 10 participants suggested question comprehension, outcome understanding, and appropriate judgment. Data from 18 participants who completed the measure more than once provide some evidence for the reliability of the measure to capture repeated values over time. Conclusions. These initial data suggest that Values-HM may be acceptable to patients and valid and reliable to capture patient values. Additional data are needed to confirm these findings in a larger and more diverse sample. This trial was registered at www.clinicaltrials.gov as #NCT05061095. Highlights Values-HM is a novel values elicitation measure designed based on best–worst scaling for adults with blood cancers. Values-HM appears to be acceptable to patients and may be valid and reliable to capture patient values in clinical care.

Remote symptom monitoring (RSM) improves outcomes during cancer treatment, but sustained program functioning requires coordination across clinical roles to optimize patient experience. Existing implementation tools provide building blocks for patient-reported data systems, but detailed approaches for clinical operation of RSM are lacking in oncology. We conducted a multi-method study at an NCI-designated comprehensive cancer center implementing RSM. Using ethnographic observation, process mapping, and semi-structured interviews with 25 healthcare professionals and 20 patients, we examined RSM workflows and experiences. A process map anchored interviews to elicit descriptions of who does what, when, how, and why. Applying a cultural-models lens, we analyzed responses using freelist logic and constant comparison across four subprocesses: screening, enrollment, survey, and intervention. Five cultural components of the RSM process emerged: ownership, mechanism, timing, auditing, and challenges. Screening placed ownership with non-clinical navigators and exposed communication gaps related to eligible patient identification. Enrollment depended on clinician endorsement, navigator-led logistics, and outreach near treatment start. Survey completion relied on brief weekly instruments aligned to treatment rhythms with navigator oversight. Symptom intervention centered on nurse-led triage and clear alert closure expectations. Multi-disciplinary team discussion yielded 12 suggestions anchored in these components. Pairing process mapping with a cultural-models approach clarifies the shared expectations that sustain RSM in routine care. This helps diagnose coordination needs, guide role-specific training, support local adaptation, and ensure patient experience is optimized.

Background/Objectives: The PRO-CTCAE Average Composite Score (ACS), calculated as the mean of PRO-CTCAE composite scores, summarizes overall symptom and side effect burden. This study evaluated the test-retest reliability, responsiveness to change, known-groups validity, and sensitivity to group differences in the ACS. Methods: Analyses used the original PRO-CTCAE validation dataset, including lung (n = 174), breast (n = 222), and head and neck (n = 139) cancer cohorts, and data from the COMET-2 randomized phase III trial comparing cabozantinib (n = 53) and mitoxantrone-prednisone (n = 54) in men with previously treated prostate cancer. Test-retest reliability was assessed using intraclass correlation coefficients (ICCs) from two-way mixed-effects models for agreement. Responsiveness was evaluated by relating ACS change scores (Visit 1 minus follow-up Visit 2) to patient-reported global ratings of change (GRC; worsened, unchanged, or improved) using standardized response means (SRMs) and Jonckheere-Terpstra trend tests. Known-groups validity was examined by comparing mean ACS values between ECOG performance status groups (0-1 vs. 2-4). Sensitivity to treatment group differences was assessed using model-based area-under-the-curve (AUC) comparisons of longitudinal ACS trajectories. Results: Test-retest reliability was acceptable, with ICCs among GRC-defined stable patients of 0.80 (95% CI: 0.70-0.87) in lung cancer, 0.84 (95% CI: 0.77-0.89) in breast cancer, and 0.77 (95% CI: 0.63-0.86) in head and neck cancer; ICCs based on assessments completed one day apart ranged from 0.88 to 0.90. The ACS demonstrated responsiveness, with SRMs of 0.30/0.15/-0.37 (lung), 0.29/0.14/-0.40 (breast), and -0.01/-0.20/-0.56 (head/neck) for improved/no-change/worsened groups, respectively, and significant monotonic trends across GRC categories (all p < 0.01). Known-groups validity was supported by conceptually expected differences in ACS values across distinct levels of self-reported patient-reported physical functioning and ECOG performance status categories. Mean ACS values were lower among patients with good versus limited physical functioning (0.71 vs. 1.23 in lung cancer, 0.52 vs. 1.19 in breast cancer, and 0.69 vs. 1.29 in head and neck cancer) and among patients with ECOG PS 0-1 versus 2-4 (0.93 vs. 1.31, 0.74 vs. 1.22, and 0.90 vs. 1.06, respectively). In COMET-2, higher symptom burden was detected in the cabozantinib arm compared with the mitoxantrone-prednisone arm (AUC difference = 1.5, 95% CI: 0.2-2.8, p = 0.02). Conclusions: The ACS demonstrated acceptable test-retest reliability, responsiveness, and known-groups validity across multiple cancer populations. These findings support its use as a complementary summary measure of overall symptomatic adverse event burden alongside individual symptom-level analyses.

Gastrointestinal (GI) and respiratory symptoms are common among patients undergoing cancer treatment, yet little is known about the symptom burdens and management by race. Remote Symptom Monitoring (RSM) with electronic Patient-Reported Outcomes (ePROs) enhances symptom tracking and intervention; however, its impact on racial disparities in symptom reporting remains unclear. This study examined racial differences in moderate to severe symptom reporting among patients enrolled in an ePRO-based RSM program, described how the frequency of reported symptoms differed across early and later periods of enrollment, and characterized patient engagement with the program. We conducted a retrospective cohort study of 924 cancer patients enrolled in an RSM program at a large academic medical center. Patients self-reported GI (constipation, diarrhea, decreased appetite, nausea, vomiting) and respiratory (cough, shortness of breath) symptoms weekly for six months. Symptoms were categorized as moderate/severe versus none/mild using the PRO-CTCAE grading tool. A generalized linear mixed-effects Poisson log-link model, adjusted for demographic and clinical factors, compared the frequency of moderate/severe symptom reporting between Black and White patients at 0 to 3 and 4 to 6 months post RSM enrolment. We observed no significant differences in reported moderate/severe GI (Black: 18

11005 Background: Remote therapeutic monitoring (RTM) using electronic patient-reported outcomes (ePROs) may help care teams respond earlier to patient needs and potentially avoid acute care events, but evidence from real-world practice is limited. This study evaluates the impact of using the Canopy ePRO-based RTM platform on acute care events. Methods: We studied 1,549 patients with metastatic solid tumor malignancies receiving systemic treatment at 5 community oncology sites between Jan 1, 2024 until July 20, 2025. Patients were invited to complete weekly ePRO symptom surveys. Symptoms that exceeded pre-specified severity thresholds triggered notifications to a dedicated triage nursing staff for evaluation and consideration of telephonic triage, urgent outpatient office evaluation or referral to the emergency department or hospital. Patients who enrolled and submitted ≥ 2 ePRO surveys within 45 days of first treatment were included in the RTM group, while those not enrolled or who did not submit 2 or more reports were in the control group. Hospitalization events were extracted from the Arkansas health information exchange. Inverse probability of treatment weighting was used to account for potential non-random selection of patients into the RTM group when performing statistical tests of difference, and achieved cohort balance in terms of age at index, time from first cancer diagnosis to treatment, site of cancer, sex, and race. Results: Over 18 months following first anticancer treatment through data cutoff date, 558 patients were in the RTM group compared to 991 patients in the control group. Among patients in the RTM group, 9.0% had a record of hospitalization compared to 13% in the control group, representing a 28% reduction (risk difference: -3.63, 95% CI -7.06 to -0.39, p: 0.032); 12% of patients in the RTM group had record of ED visit compared to 13% in the control group (risk difference: -1.77, 95% CI: -5.24 to 1.63, p: 0.310). The estimated cost savings per 1,000 patients/years utilizing RTM during treatment was $3,192,789 for hospitalizations and $93,330 for emergency department visits. Conclusions: RTM utilization with Canopy is associated with fewer hospitalizations and potentially fewer emergency visits compared to traditional outpatient management of patients with metastatic solid tumors, potentially reducing the cost of care. Prompt notification of patient symptoms resulting in preemptive treatment may help patients avoid acute care events. Unweighted clinical and demographic characteristics. Characteristic Enrolled & ActiveN = 558 Not EnrolledN = 991 Age at index 64 (55, 71) 70 (62, 78) Sex - Female 313 (56%) 515 (52%) Race/Ethnicity - White 484 (87%) 846 (85%) Race/Ethnicity - Hispanic 28 (5.0%) 58 (5.9%) Lung Cancer 131 (23%) 179 (18%) Breast cancer 98 (18%) 219 (22%) Colorectal cancer 88 (16%) 148 (15%)

Background This secondary analysis of a phase I clinical trial illustrates integrated summary and visualization of Common Terminology Criteria for Adverse Events (CTCAE) data and Patient-Reported Outcomes version of the CTCAE (PRO-CTCAE) data at baseline and through the first cycle of treatment for tolerability review during dose-cohort evaluation in a phase I trial. Methods This study uses data from a phase I clinical trial of patients with relapsed or refractory acute myeloid leukemia. Through iterative feedback from clinicians and statisticians, we developed a summary of cycle 1 PRO-CTCAE and CTCAE data for the first dose-level cohort. This summary is intended to demonstrate reporting that would be reviewed to inform a decision about proceeding to the next dose level in a dose escalation trial. Results Cycle 1 CTCAE and PRO-CTCAE data for 4 patients enrolled in the first dose cohort are summarized as narrative and tabular summaries and visualized using swimmer and butterfly plots. In this cohort, 3/4 (75%) patients had at least one CTCAE grade 3 or higher adverse event, and all 4 (100%) patients had at least one PRO-CTCAE composite score of 3 during cycle 1. All 4 (100%) patients had at least one symptom for which PRO-CTCAE suggested greater symptom burden than was reflected in CTCAE. Conclusions In this illustrative summary of a dose cohort, PRO-CTCAE complemented CTCAE reporting by capturing patient-reported symptomatic burden that was not always fully reflected by clinician-graded adverse events. Reviewing PRO-CTCAE and CTCAE data together during dose-cohort evaluation may enrich discussions on safety and tolerability in phase I trials.