Canhua Xiao

Background: Young adult childhood cancer survivors (YA CCSs) may experience posttraumatic growth (PTG), yet little is known about how neighborhood-level social determinants influence PTG. Posttraumatic stress may also shape PTG, but its mediating role in the relationship between neighborhood deprivation and PTG remains unclear. Objective: To examine associations between neighborhood deprivation and PTG among YA CCSs and determine whether posttraumatic stress mediates these associations. Methods: This cross-sectional study included 121 YA CCSs. Neighborhood deprivation was measured using the Area Deprivation Index (ADI) and the Environmental Justice Index (EJI). PTG and posttraumatic stress were assessed using the Posttraumatic Growth Inventory and the Impact of Event Scale-Revised. Linear regression examined associations between neighborhood deprivation and PTG. The MEDCURVE macro tested curvilinear mediation. Results: Participants were mostly non-Hispanic White (71%) with a mean age of 19.4 years. Higher ADI scores were associated with greater PTG ( B = 0.36, 95% confidence interval [CI], 0.17–0.55). Total EJI and its Social and Health Vulnerability subscale were positively associated with PTG. Posttraumatic stress curvilinearly mediated the ADI–PTG association, with stronger mediation in less deprived neighborhoods ( B = 0.11, 95% CI, 0.02–0.30) and weaker mediation in more deprived neighborhoods ( B = 0.07, 95% CI, 0.02–0.17). Similar mediation was observed for the EJI Social Vulnerability subscale. Conclusions: Greater neighborhood deprivation was associated with higher PTG in YA CCSs. Posttraumatic stress mediated this association, with its influence varying across neighborhood conditions. Implications for Oncology Nursing Practice: Findings support personalized interventions addressing socioecological environments and psychological determinants.

BACKGROUND Chronic disease management increasingly depends on monitoring symptoms and health behaviors outside clinical settings, yet most mobile health (mHealth) systems treat behavioral data capture and intervention delivery as separate functions. Platforms that collect high-resolution sensor or ecological momentary assessment (EMA) data often provide limited tools for clinician-authored intervention content, whereas intervention applications commonly embed content and decision logic directly in application code. This coupling slows iteration, limits personalization, and creates dependence on software developers. OBJECTIVE This paper presents CanBeWell, a disease-agnostic telehealth platform that integrates multimodal behavioral data collection with a clinician-operated, code-free intervention authoring and delivery workflow. CanBeWell consists of a patient-facing mobile application, a backend application programming interface (API) and data layer, and a clinician-facing dashboard. We describe the platform's system architecture, design rationale, implementation for cancer survivorship care, and formative stakeholder evaluation. METHODS CanBeWell was developed as a platform-oriented system informed by prior work in remote patient monitoring, mHealth sensing, and adaptive intervention theory. The platform was evaluated through formative semi-structured interviews with cancer patients (n=12) and their caregivers (n=9). [CX3.1]All study participants reviewed the mobile application prototype and provided feedback during in-person interviews on its usability, perceived value, and suggested improvements. Participants also rated the platform's overall usability on a 10-point scale. Additionally, clinicians (n = 6) were provided access to both the patient-facing mobile application and the clinician dashboard to evaluate the platform's usability, functionality, clinical relevance, and intervention workflow. Qualitative data were analyzed using thematic analysis by two independent coders RESULTS Twenty-one participants (patients and caregivers) completed the demographic survey (mean age 49.4 years; 61.9% female; 57.1% non-Hispanic White). Four themes emerged based on patient and caregiver interviews: ease of use, interest in the application, valued features, and overall user-friendliness[CX4.1]. The mean usability rating across patients and caregivers was 9 out of 10. Patients and caregivers valued the phased intervention structure, baseline activity tracking, and incremental goal progression. Clinicians responded positively to the no-code authoring workflow, noting that the ability to review behavioral data, draft content, and deploy approved phases without developer involvement could reduce technical barriers and accelerate intervention refinement[CX5.1]. The most common requested improvement by clinicians was stronger notification support to sustain engagement over longer deployments. CONCLUSIONS CanBeWell extends remote patient monitoring infrastructure by adding a clinician-governed intervention authoring and delivery layer that closes the loop between behavioral sensing and patient-facing support. Formative validation in a cancer survivorship context suggests that the platform is usable and clinically meaningful to patients, caregivers, and clinicians. By separating intervention content from application code, the platform enables clinicians to create and update personalized interventions without extensive programming support, reducing engineering bottlenecks and facilitating scalable implementation across chronic disease settings.

Background: Epigenetic age acceleration in head and neck cancer (HNC) patients undergoing radiotherapy has been linked to adverse treatment outcomes. Understanding a patient's likely epigenetic aging response prior to radiotherapy has the potential to inform healthcare planning and clinical decision‐making; however, current methods cannot effectively predict these changes throughout treatment, particularly without expensive assays of epigenetic alterations. Methods: This study developed and validated a machine learning framework to predict EAA following radiotherapy, utilizing pre‐radiotherapy (Time 1) sociodemographic information, symptom reports, clinical measurements, and immune biomarkers. Various machine learning models were explored to forecast EAA across three post‐treatment clinic stages: Immediately post‐radiotherapy (Time 2), six months (Time 3), and 12 months post‐radiotherapy (Time 4). Results: Our results demonstrate that: (1) deep learning methods, particularly TabNet, outperform conventional algorithms with an average RMSE of 4.08 (SD = 0.32); (2) predictions are most accurate immediately post‐treatment (Time 2 RMSE: 4.87); (3) baseline immune markers, including absolute eosinophil count and hemoglobin levels, are consistent predictors across timepoints; and (4) specific patient subgroups show distinct prediction accuracies (RMSE range: 1.70–4.34). Conclusion: These results suggest that pre‐treatment demographic and clinical data effectively predict post‐treatment EAA trajectories without expensive epigenetic assays, enabling cost‐effective early identification of high‐risk patients for potential targeted interventions before adverse effects manifest.

Human papillomavirus (HPV) is a major cause of oropharyngeal and other cancers, occurs more frequently among people with HIV (PWH). Despite antiretroviral

PURPOSE: NRG Oncology/Alliance LU005 (ClinicalTrials.gov identifier: NCT03811002 ) tested the addition of atezolizumab to concurrent chemoradiation (CRT) in this open-label, phase III international trial. METHODS: Patients with limited-stage small cell lung cancer (LS-SCLC), stage Tx-IV, N0-3, and M0 with Eastern Cooperative Group performance status (PS) 0-2 received one cycle of chemotherapy (platinum/etoposide) before study registration and were randomly assigned to CRT alone versus CRT plus concurrent and adjuvant atezolizumab, 1,200 mg once daily, every 3 weeks until investigator-assessed progression or intolerable side effects for a maximum of 17 cycles. Patients were stratified by choice of chemotherapy (cisplatin v carboplatin), radiation fractionation schedule (66 Gy once daily v 45 Gy twice daily), sex, and PS (0/1 v 2). The primary end point was overall survival (OS). Secondary end points included investigator-assessed progression-free survival (PFS), objective response rate, local control, and distant-metastasis-free survival (DMFS). RESULTS: patients were randomly assigned from May 2019 to December 2023. The median OS was 36.1 months (95% CI, 28.1 to 42.5) for the CRT-alone arm and 31.1 months (95% CI, 28.5 to 44.7) for the CRT + atezolizumab arm, respectively (hazard ratio [HR], 1.03 [95% CI, 0.80 to 1.32]). The median PFS was 11.4 months (95% CI, 10.3 to 13.2) for the CRT-alone arm and 12.1 months (95% CI, 10.9 to 15.2) for the CRT + atezolizumab arm, respectively (HR, 0.98 [95% CI, 0.79 to 1.22]). The median DMFS was 13.0 months (95% CI, 11.3 to 18.2) for the CRT-alone arm and 16.8 months (95% CI, 12.1 to 21.6) for the CRT + atezolizumab arm (HR, 0.96 [95% CI, 0.76 to 1.21]). No unexpected safety signals with concurrent atezolizumab were observed. CONCLUSION: Concurrent and adjuvant atezolizumab with chemoradiation did not improve survival in patients with LS-SCLC.

This cohort study examines the association of neighborhood disadvantage with outcomes in patients with head and neck cancer.

Background: 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. Objective: This study aimed to evaluate the overall performance of LLMs in generating tailored educational content for cancer survivors and their caregivers with limited health literacy or language barriers, compare the performances of 3 Generative Pretrained Transformer (GPT) models (ie, GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo; OpenAI), and examine how different prompting approaches influence the quality of the generated content. Methods: We selected 30 topics from national guidelines on cancer care and education. GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo were used to generate tailored content of up to 250 words at a 6th-grade reading level, with translations into Spanish and Chinese for each topic. Two distinct prompting approaches (textual and bulleted) were applied and evaluated. Nine oncology experts evaluated 360 generated responses based on predetermined criteria: word limit, reading level, and quality assessment (ie, clarity, accuracy, relevance, completeness, and comprehensibility). ANOVA (analysis of variance) or chi-square analyses were used to compare differences among the various GPT models and prompts. Results: Overall, LLMs showed excellent performance in tailoring educational content, with 74.2% (267/360) adhering to the specified word limit and achieving an average quality assessment score of 8.933 out of 10. However, LLMs showed moderate performance in reading level, with 41.1% (148/360) of content failing to meet the sixth-grade reading level. LLMs demonstrated strong translation capabilities, achieving an accuracy of 96.7% (87/90) for Spanish and 81.1% (73/90) for Chinese translations. Common errors included imprecise scopes, inaccuracies in definitions, and content that lacked actionable recommendations. The more advanced GPT-4 family models showed better overall performance compared to GPT-3.5 Turbo. Prompting GPTs to produce bulleted-format content was likely to result in better educational content compared with textual-format content. Conclusions: All 3 LLMs demonstrated high potential for delivering multilingual, concise, and low health literacy educational content for cancer survivors and caregivers who face limited literacy or language barriers. GPT-4 family models were notably more robust. While further refinement is required to ensure simpler reading levels and fully comprehensive information, these findings highlight LLMs as an emerging tool for bridging gaps in cancer education and advancing health equity. Future research should integrate expert feedback, additional prompt engineering strategies, and specialized training data to optimize content accuracy and accessibility.

Cancer patients are commonly affected by fatigue. Herein, we sought to examine epigenetic modifications (i.e., DNA methylation) related to fatigue in peripheral blood among patients during and after treatment for head and neck cancer (HNC). Further, we determined whether these modifications were associated with gene expression and inflammatory protein markers, which we have previously linked to fatigue in HNC. This prospective, longitudinal study enrolled eligible patients with data collected at pre-radiotherapy, end of radiotherapy, and six months and one-year post-radiotherapy. Fatigue data were reported by patients using the Multidimensional Fatigue Inventory (MFI)-20. DNA methylation (Illumina MethylationEPIC) and gene expression (Applied Biosystems Clariom S) arrays and assays for seven inflammatory markers (R&D Systems multiplex) were performed. Mixed models and enrichment analyses were applied to establish the associations. A total of 386 methylation loci were associated with fatigue among 145 patients (False Discovery Rate [FDR] < 0.05). Enrichment analyses showed the involvement of genes related to immune and inflammatory responses, insulin and lipid metabolism, neuropsychological disorders, and tumors. We further identified 16 methylation-gene expression pairs (FDR < 0.05), which were linked to immune and inflammatory responses and lipid metabolism. Ninety-one percent (351) of the 386 methylation loci were also significantly associated with inflammatory markers (e.g., interleukin 6, c-reactive protein; FDR < 0.05), which further mediated the association between methylation and fatigue (FDR < 0.05). These data suggest that epigenetic modifications associated with inflammation and immunometabolism, in conjunction with relevant gene expression and protein markers, are potential targets for treating fatigue in HNC patients. The findings also merit future prospective studies in other cancer populations as well as interventional investigations.