International Journal of Radiation Oncology*Biology*Physics

PurposeThis study evaluated the safety and effectiveness of an intraoral light-emitting diode (LED)-based photobiomodulation (PBM) device to reduce the incidence and severity of oral mucositis (OM) from intensity modulated radiation therapy (IMRT) for head and neck cancer (HNC). MethodsThis randomized, double-blind, sham-controlled trial enrolled patients with HNC undergoing high-dose IMRT over 6-8 weeks, with or without concurrent chemotherapy. Participants received daily 10-minute PBM or sham treatments immediately before IMRT sessions. Assessments were conducted at baseline, daily and weekly during IMRT, and two weeks post-IMRT. ResultsEighty-five participants (42 PBM; 43 sham) were enrolled across 12 US sites. No device-related adverse events were observed, and 99.5% of initiated sessions were completed. In the intent-to-treat population, severe OM (WHO Grade [≥]3) incidence was significantly lower with PBM across six weeks of IMRT (36.8% vs 57.1%; p = 0.046) and at two weeks post-treatment (10.8% vs 36.4%; p = 0.042). In the per-protocol population, the PBM arm reported significantly greater taste preservation (p = 0.034), lower increases in mouth/throat soreness (p = 0.029) and throat pain (p = 0.028) and needed fewer feeding tube placements (p = 0.073) than the control arm. ConclusionDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed. Trial RegistrationClinicalTrials.gov Registration Number NCT03972527. Registered on June 3, 2019. Concise SummaryDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed.

Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks--hallucination and automation bias--that threaten the fidelity of AI-assisted learning. We present Onco-Shikshak V7, an AI-native adaptive learning platform that addresses both challenges through a unified cognitive architecture grounded in learning science. The system replaces isolated educational modules with four authentic clinical workflows--Morning Report, Tumor Board, Clinic Day, and AI Textbook--each scaffolded by a nine-module pedagogy engine that integrates ACT-R activation dynamics (illness scripts), Item Response Theory (adaptive difficulty), the Free Spaced Repetition Scheduler (FSRS v4), Zone of Proximal Development (scaffolding), and metacognitive calibration training (Brier score). Six specialist AI agents--medical oncology, radiation oncology, surgical oncology, pathology, radiology, and oncology navigation--engage in multi-disciplinary deliberation with per-specialty retrieval-augmented generation (RAG) grounding across nine authoritative guideline sources including NCCN, ESMO, and ASTRO. The platform provides 18 clinical cases with decision trees across six cancer types, maps every interaction to 13 ACGME Hematology-Oncology milestones, and implements four closed-loop feedback mechanisms that connect session errors to targeted flashcards, weak domains to suggested cases, and all interactions to a persistent learner profile. Technical validation confirms algorithmic correctness across eight subsystems. To our knowledge, this is the first system to unify ACT-R, IRT, FSRS, ZPD, and metacognitive calibration in a single medical education platform. Formal learner evaluation via randomized controlled trial is planned.

Importance: Lung cancer mortality in the United States has fallen substantially in recent decades, yet the relative influence of behavioral, environmental, socioeconomic, and therapeutic factors and their sex specific contributions remains unclear. Understanding these drivers is essential to sustain progress and reduce persistent disparities. Objective: To quantify how behavioral, environmental, socioeconomic, and therapeutic determinants collectively shaped US lung cancer mortality from 1994 to 2020, assess sex specific differences, and forecast mortality trajectories through 2030 using an integrated machine learning framework. Design, Setting, and Participants: Ecological time series study using publicly available national data from 1994 to 2020. Sex stratified analyses were conducted integrating lung cancer mortality, smoking prevalence, fine particulate matter PM2.5 exposure, Human Development Index HDI, per capita healthcare expenditure, healthcare inflation, insurance coverage, income inequality, and annual drug approvals. Exposures: Behavioral smoking, environmental PM2.5, socioeconomic HDI health expenditure inflation, uninsurance inequality, and therapeutic drug approval indicators. Main Outcomes and Measures: Age-standardized lung cancer mortality per 100000 population. Temporal changes were modeled using Joinpoint regression. Concurrent associations were assessed using multivariable and elastic net regression, and forecasts were estimated with AutoRegressive Integrated Moving Average models with exogenous variables ARIMAX. Results: From 1994 to 2020, mortality declined by 59 percent in men, from 52.9 to 21.7 per 100000, and by 40 percent in women, from 26.7 to 15.9 per 100000, with faster declines after 2015. Smoking and PM2.5 decreased by more than 45 percent but remained strongly correlated with mortality. In elastic net models, PM2.5 was the strongest predictor for men, while smoking was the strongest predictor for women. Per capita expenditure and HDI ranked higher for men, while uninsurance and income inequality were strong predictors for women. Mortality declines occurred during periods of major approvals of lung cancer drugs. Forecasts suggest continued but slower declines through 2030, with projected rates of 20.2 and 14.9 deaths per 100000 in men and women, respectively. Conclusions and Relevance: Sex specific declines in lung cancer mortality reflect different dominant correlates, with air pollution more important in men and smoking more important in women, while socioeconomic conditions and therapeutic advances also influence trends. Continued tobacco control, improved air quality, and equitable access to screening and modern treatment are essential to sustain further reductions in mortality. Keywords: Lung Neoplasms, Sex Factors, Air Pollution, Smoking, Socioeconomic Factors, Machine Learning

Lower-grade gliomas (World Health Organization [WHO] grades 2-3) exhibit variable treatment responses, yet clinical decisions remain guided by population-level trial results. Standard causal survival forests estimate treatment effects at individual time horizons but lack methodology to synthesize these into interpretable temporal trajectories. Here, we apply the Causal Analysis of Survival Trajectories (CAST) framework, a recently developed extension of causal survival forests that synthesizes horizon-specific causal effect estimates into smooth temporal curves while accounting for between-horizon covariances via bootstrap estimation and Ledoit-Wolf shrinkage. We apply CAST to estimate time-varying, heterogeneous effects of radiotherapy and chemotherapy in 776 patients with lower-grade gliomas from The Cancer Genome Atlas (TCGA; n=512) and the Chinese Glioma Genome Atlas (CGGA; n=264), analyzing six treatment-outcome scenarios and adjusting for age, sex, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, and extent of resection using elastic net propensity scores with overlap weighting. CAST curves reveal that chemotherapy provides consistent, sustained benefits across both cohorts; survival probability gains peak at 0.31 at 72-84 months for TCGA overall survival and 0.46 at 48 months for progression-free survival, with restricted mean survival time gains of 18.4 and 32.5 months at 10 years, respectively. CGGA chemotherapy shows delayed but large positive effects (survival probability peak 0.48 at 108 months). Radiotherapy effects are mixed, with modest E-values indicating sensitivity to residual confounding by indication. Subgroup CAST curves identify age at diagnosis as the dominant driver of treatment effect heterogeneity (46-56% of splits). All findings are robust to placebo permutation, simulated unobserved confounder, and negative control refutation tests. The CAST framework provides a general-purpose tool for temporal treatment effect visualization applicable beyond neuro-oncology.

BackgroundOnce the treatment starts, early prediction of treatment benefit and its correlation with overall survival (OS) remains challenging in metastatic castration-resistant prostate cancer (mCRPC). Existing prognostic models require long-term follow-up, limiting their ability to inform timely treatment decisions. To address this gap, we evaluated tumor growth rate (g-rate)-based survival models across multiple treatment lines to assess their ability to predict OS and support early clinical decision-making. MethodsWe developed GxSurv, a Random Survival Forest (RSF)-based framework that incorporates baseline clinical variables and g-rate calculated from serial on-treatment PSA, to construct line-specific prediction models of OS, a direct measure of treatment outcome. Three variants were developed: G3Surv, using the 3-month g-rate; G6Surv, using the 6-month g-rate; and GfSurv, using the final observed g-rate. Model performance was evaluated using Harrells C-index, Unos C-index, Integrated Brier Score (IBS), time-dependent area under the curve (tAUC). Model interpretability was assessed using permutation importance to quantify predictor contributions within the GxSurv framework. FindingsThe study included 15912 treatment records from 11014 patients with mCPRC across four lines of therapy. We found that incorporation of g-rate consistently improved model performance across all treatment lines, with all GxSurv models outperforming Cox proportional hazards (CoxPH). As the earliest prognostic model, our G3Surv demonstrated strong early predictive performance, with Harrells C-index values ranging from 0{middle dot}700 to 0{middle dot}746 and tAUC values of 0{middle dot}766 to 0{middle dot}822 across all lines, representing 5-8% and 4-5% improvements over CoxPH, respectively. These results indicate that G3Surv accurately predicts individual treatment outcomes at 3 months after treatment initiation. Feature importance analyses consistently identified g-rate as a top predictor, followed by baseline PSA and hemoglobin, with relative variation across treatment lines. InterpretationIntegrating g-rate calculated from on-treatment PSA values enables accurate, line-specific prediction of treatment outcomes in mCRPC, with the 3-month g-rate providing robust early prognostic information to support timely, personalized clinical decision-making. FundingU.S. National Science Foundation, National Institutes of Health, American Cancer Society.

BackgroundGlioblastoma (GBM), Isocitrate dehydrogenase-wildtype (IDH-wt) is characterised by diffuse infiltration, with progression often arising from perilesional tissue and occult white-matter damage. We investigated whether radiomics from the T2/FLAIR-defined oedema and the structural disconnectome improve prediction of progression-free survival (PFS). MethodsWe retrospectively analysed 387 adults with newly diagnosed GBM, IDH-wt treated at a single tertiary centre (2005-2020). A deep-learning pipeline segmented enhancing tumour, non-enhancing tumour, and oedema on pre-operative MRI; lesion masks were propagated to normative tractography to derive disconnectome maps. 3-D shape radiomic features extracted for each segmented region underwent appropriate feature selection. Finally, 10 tumour and 9 oedema radiomics were combined with 6 clinical features to train 3 survival models (Random Survival Forest (RSF), XGBoost, Cox proportional hazards (CPH)) that were evaluated on a held-out 20% test set using Harrells C-index, Kaplan-Meier risk stratification and time-dependent ROC curves. ResultsThe best performance was achieved by RSF using all clinical and radiomic features (C-index 0.665 vs 0.595 for clinical features only, p=0.088). Models including oedema radiomics outperformed those using tumour radiomics alone, and disconnectome features, derived from both tumour and oedema regions, were repeatedly selected among the top predictors across algorithms. Combining radiomic and clinical features improved risk stratification and 12-month early-versus-late recurrence classification (AUC 0.704 vs 0.582 for clinical features alone). ConclusionsIntegrating perilesional oedema and white-matter disconnectome MR features with clinical and molecular data enhances prediction of PFS in GBM, IDH-wt. These network-aware, multimodal survival models may support personalised risk-adapted treatment strategies pending external validation. Key Points- GBM IDH-wt exhibits a high recurrence rate despite aggressive treatment. - Addition of high-dimensional oedema and disconnectome radiomic features to clinical features showed consistent improvement in the test performance of 3 ML models. - This can support informed clinical decision-making. Importance of the StudyPrediction of progression free survival (PFS) for a patient with highly recurrent glioblastoma IDH-wt traditionally relies on clinical history, demographics, and molecular markers of the tumour. Recent literature reveals the tumours disruptive nature through its invasion of white-matter tracts and identifies its microenvironment, particularly the perilesional oedema, as a harbour of treatment resistant tumour cells. This study is the first to combine high-dimensional radiomic features of the tumour, the oedema, and their disconnectome with clinical and treatment factors to predict PFS. Using 3 model architectures (XGBoost, RSF, and CoxPH), we demonstrate consistent directional improvements in performance, on addition of radiomic features to clinical baseline models. Furthermore, oedema and disconnectome radiomics are identified as top predictor features across algorithms. This proof-of-concept study provides a reproducible multimodal pipeline, reaffirms the usability of MR radiomics, and identifies features of the oedema and the structural connectome as promising biomarkers, demanding large-scale external validation.

BackgroundGlyphosate-based herbicides are among the most widely used agricultural chemicals globally. Concerns regarding their carcinogenic potential, particularly in relation to non-Hodgkins lymphoma (NHL), persist despite multiple prior systematic reviews and meta-analyses. However, these reviews have demonstrated important methodological limitations and inconsistent analytic decisions, limiting confidence in their conclusions. ObjectiveTo conduct a rigorous, up-to-date systematic review and meta-analysis of observational studies examining the association between glyphosate-based herbicide exposure and risk of NHL and its subtypes, while addressing methodological and analytic shortcomings of prior syntheses. MethodsWe searched MEDLINE (1970-February 26, 2026) and EMBASE (inception-February 26, 2026), supplemented by reference list review. Eligible studies included cohort, case-control, and pooled analyses reporting effect estimates (or sufficient data) for glyphosate exposure and NHL incidence. Two reviewers independently assessed risk of bias using the Newcastle-Ottawa Scale (for primary studies) and structured criteria for pooled analyses. Random- and fixed-effects meta-analyses were conducted using inverse-variance methods. Heterogeneity was evaluated using Cochrans Q and I{superscript 2} statistics. Publication bias was assessed using standard and contour-enhanced funnel plots. Sensitivity analyses addressed overlapping cohorts, hazard ratio inclusion, exposure definitions, and model overfitting (events-per-variable considerations). Certainty of evidence was graded using GRADE. ResultsSeventeen publications were identified, representing 20 unique study populations; after accounting for overlap, 10 primary datasets were included in quantitative synthesis. Five studies were assessed as low risk of bias, four as moderate risk, and one as high risk. For ever exposure, the random-effects model across all eligible datasets yielded an odds ratio (OR) of 1.11 (95% CI: 0.98-1.27), with moderate heterogeneity (I{superscript 2}{approx}53%). In sensitivity analyses excluding hazard ratio-only studies and overlapping cohorts, pooled ORs ranged from 1.19 to 1.23, with estimates approaching or reaching statistical significance depending on modeling assumptions. For the highest exposure categories, the random-effects model demonstrated a statistically significant association (OR{approx}1.38; 95% CI: 1.00-1.90), with moderate heterogeneity (I{superscript 2}{approx}61%). Sensitivity analyses excluding selected pooled cohort estimates strengthened the association (OR{approx}1.47; 95% CI: 1.04-2.06). Analyses incorporating alternative cumulative exposure metrics yielded similar significant associations (OR{approx}1.33-1.45) with low or absent residual heterogeneity. Subtype analyses suggested elevated risks particularly for diffuse large B-cell lymphoma and follicular lymphoma in certain datasets. Publication bias assessments revealed evidence of small-study effects in some models, though contour-enhanced analyses suggested that not all asymmetry was attributable to selective publication. Overall certainty of evidence was graded as moderate for highest exposure analyses and low-to-moderate for ever-exposure analyses due to residual heterogeneity and observational design limitations. ConclusionsThis updated synthesis indicates that while associations with ever exposure to glyphosate are modest and sensitive to analytic decisions, higher levels of exposure are consistently associated with increased odds of NHL. Findings are robust across multiple sensitivity analyses addressing overlapping data, exposure classification, and model overfitting. These results support a dose-related association between glyphosate-based herbicide exposure and NHL risk and underscore the need for continued surveillance, improved exposure characterization, and prospective cohort analyses with minimized loss to follow-up and transparent analytic reporting.

BackgroundIn immune checkpoint inhibitor (ICI) trials, overall survival (OS) benefits are well established, yet improvements in quality of life (QoL) are often inconsistent or absent in conventional analyses. This apparent discordance raises important questions: are QoL outcomes truly unrelated to survival, and how can QoL results be better utilized and interpreted? MethodsA model-based meta-analysis (MBMA) of longitudinal EORTC QLQ-C30 global health status/quality of life data from randomized ICI trials was conducted. Longitudinal QoL trajectories were analyzed using a nonlinear mixed-effects model to estimate treatment-related toxicity and long-term QoL improvement. Associations between QoL trajectory parameters and OS were assessed using spearman rank correlation tests and Cox proportional hazards models. ResultsTwenty-seven studies (8,149 ICI and 5,593 control patients) contributed longitudinal QoL data, and 18 studies provided matched OS data. Raw QoL trajectories showed overlap between treatment arms, while OS consistently favored ICIs. MBMA revealed that ICIs had similar toxicity but significantly faster QoL improvement than control therapies (p < 0.0001). Baseline QoL, toxicity, and QoL improvement rate were all significantly associated with OS (p < 0.001). MBMA-based QoL comparisons were more sensitive in detecting associations with survival than raw QoL data, with the strongest association observed at Week 24 (R = -0.37, p = 0.067). ConclusionsConventional analyses comparing QoL at a single time point may obscure meaningful patient-reported benefits. By capturing longitudinal QoL trajectories across trials, MBMA reveals how patient experience evolves alongside survival outcomes and supports improved interpretation and utilization of QoL data in treatment evaluation.

IntroductionThe precise determination of diagnostic cut-off points is essential for the development of multimarker panels in oncology. In previous work on pulmonary nodules, it was observed that the standard two-parameter logistic fit could be insufficient for biomarkers with asymmetric distributions. Furthermore, the calculation of empirical cut-off points based on graphical visualization presented limitations in precision and reproducibility. ObjectiveThis study presents a methodological advancement in the data analysis phase (Stage 1), introducing new Python algorithms for the direct analytical calculation of empirical intersections and robust mathematical modeling using Dual Annealing with both two-parameter and four-parameter logistic functions. This improved methodology feeds into the ThresholdXpert 1.0 software tool for combinatorial optimization of biomarker panels (Stage 2), and is applied here to the diagnostic challenge of hepatocellular carcinoma (HCC). MethodsThe methodology was first validated by re-analyzing a dataset of patients with pulmonary nodules (N=895). It was subsequently applied to an HCC dataset derived from the cohort of Jang et al. (208 HCC, 193 cirrhosis, 401 total), randomly divided into a training set (280) and an independent test set (121). Scripts were developed to compare the previous two-parameter logistic fit with the new two- and four-parameter logistic models. Finally, ThresholdXpert 1.0 was used for multimarker panel optimization. ResultsThe integration of empirical calculation, logistic modeling, and combinatorial optimization through ThresholdXpert 1.0 provides a robust and coherent framework for the development of multimarker diagnostic panels. The four-parameter logistic model provided additional validation without substantially modifying cut-off values for most biomarkers, confirming the stability of the approach while offering greater flexibility for complex distributions. When applied to hepatocellular carcinoma, the framework identified a molecular panel composed of AFP, PIVKA-II, OPN, and DKK-1 with sensitivity of 0.77 and specificity of 0.72, and an optimized panel incorporating inverse MELD that achieved the best overall balance (sensitivity 0.73, specificity 0.75) in independent external validation. These results demonstrate the potential of this approach as a generalizable tool for the optimized design of binary diagnostic systems in oncology. ConclusionThe integration of complementary mathematical modeling enhances the capability of ThresholdXpert 1.0 to identify robust diagnostic panels, as in some cases a single biomarker may outperform biomarker combinations, and vice versa. This approach enabled the integration of molecular biomarkers and clinical variables under a unified mathematical framework. Contactroberto117343@gmail.com

IntroductionBRCA1/2 alterations are increasingly recognized as biologically and clinically relevant features in prostate cancer, yet the prognostic and therapeutic significance of zygosity status remains uncertain. Understanding differences between monoallelic and biallelic inactivation may refine risk stratification and guide therapeutic decision-making. Materials and MethodsA retrospective, desk-based observational analysis was performed using publicly accessible datasets from TCGA-PRAD (primary disease) and SU2C/PCF (metastatic disease). BRCA1/2 status was categorized as wild-type, monoallelic, or biallelic based on mutation, copy-number, and loss-of-heterozygosity profiles. Overall survival was evaluated using Kaplan-Meier estimates and Cox models. Systemic therapy outcomes were assessed by treatment class, incorporating exploratory interaction tests. ResultsIn TCGA-PRAD (n=300), OS did not significantly differ by zygosity (global log-rank p=0.45), with median OS of 80.0 months (wild-type), 78.0 months (monoallelic), and 55.0 months (biallelic). In SU2C/PCF (n=200), zygosity stratified outcomes significantly (global log-rank p=0.04): median OS was 22.0 months (wild-type), 14.0 months (monoallelic), and 16.0 months (biallelic). Treatment analyses showed ARSI exposure improved OS in wild-type disease (HR 0.60; 95% CI 0.38-0.95), while interaction testing suggested potential heterogeneity without statistical confirmation (interaction p=0.092). PARP inhibitor exposure showed directionally favorable HRs in wild-type and monoallelic groups but no significant interaction (interaction p=0.757). No therapy class demonstrated consistent effect modification by zygosity. ConclusionBRCA1/2 zygosity shows prognostic relevance in metastatic prostate cancer but not clearly in primary disease. While zygosity did not consistently modify systemic therapy associations in this dataset, findings support zygosity-aware reporting as a practical tool for molecular stratification and future research design.

A recent randomized clinical trial in non-small cell lung cancer1 confirms what numerous observational studies have reported time of day (ToD) may dramatically influence treatment outcomes in cancer patients. In this recent trial median overall survival (OS) decreased from 28 months in the early ToD arm to 16.8 months in the late ToD arm. We raise the concern that clinical trial outcomes may be influenced by seemingly minor biases in treatment time across arms. We also suggest that by measuring or randomizing treatment-time in clinical trials, we may identify beneficial ToD dependent treatments that would otherwise be overlooked.

Immune-related adverse events (irAEs) affect up to 40% of patients receiving immune checkpoint inhibitors, yet their identification depends on laborious and inconsistent manual chart review. Here we developed and evaluated an agentic large language model system to extract the presence, temporality, severity grade, attribution, and certainty of six irAE types from clinical notes. Retrospectively (263 notes), the system achieved macro-averaged F1 of 0.92 for detection and 0.66 for multi-class severity grading; self-consistency improved F1 by 0.14. The best-performing configuration cost approximately $0.02 per note. In prospective silent deployment over three months (884 notes), detection F1 was 0.72-0.79. In a randomized crossover study of clinical trial staff (17 participants, 316 observations), agentic assistance reduced annotation time by 40% (P < 0.001), increased complete-match accuracy (OR 1.45; 95% CI 1.01-2.09; P = 0.045), and improved inter-annotator agreement (Krippendorffs from 0.22-0.51 to 0.82-0.85). These results demonstrate that agentic AI coupled with human verification could enhance efficiency, performance, and consistency for irAE assessment.

PurposeTo describe the rationale, methods, and baseline sample descriptives of the Adolescent and Young Adult Tracking Engagement and Management Skills (AYA TEAMS) cohort. The AYA TEAMS study is a longitudinal observational cohort study that aims to identify determinants and patterns of self-management and engagement in cancer-related long-term follow-up (LTFU) care and validate a novel transition readiness assessment among adolescent and young adult (AYA) survivors of childhood cancer. ParticipantsAYA survivors of childhood cancer (ages 16-25) and their caregivers were enrolled from 3 large pediatric oncology centers across the United States from 2020-2022 and followed for 2 years (minimum) to 3 years and 3 months (if transferred to adult care). AYA inclusion criteria were: past childhood cancer diagnosis, at least 2 years off-treatment, 5 years since diagnosis, engaged with the participating pediatric health care system within the last 18 months, cognitively able to complete study procedures, and English speaking. AYA completed a comprehensive battery of measures including assessments of self-management and transition readiness at baseline and annually for 2 years. For AYA transferred to adult care, separate measures were administered at the time of transfer (following last pediatric visit) and 15 months post transfer. Caregivers (English or Spanish-speaking) completed a single survey at baseline to capture family functioning, psychosocial risk, and transition readiness. Cancer diagnosis, treatment modalities, treatment-related late effects, and engagement in LTFU care were captured via electronic medical record review. In total, 709 AYA were enrolled and 587 were included in the final cohort [Mage=19.7 years, 52.5% female, 38.2% from racial and/or ethnic minoritized groups, (REMG)]. The cohort was on average 7.3 years old at the time of diagnosis and 10.5 years off treatment. Half (52.5%) were survivors of leukemia/lymphoma, 38.0% solid tumors, and 9.5% central nervous system tumors. Three hundred and ninety-nine caregivers participated (90% mothers). Findings to DateEnrolled AYA excluded from the baseline cohort were more likely to be male, from REMG, and/or to enroll without a caregiver. Baseline cohort differences between sites emerged for age, race and ethnicity, socioeconomic status, and treatment modalities and intensity. Future PlansData collection was completed in April 2025. Findings from this cohort will elucidate important predictors of self-management and engagement in recommended annual LTFU and inform the design of interventions to reduce disengagement in LTFU. Strengths and LimitationsO_LIThis study is the first known prospective cohort of AYA-only long-term survivors of childhood cancer in the United States recruited from pediatric cancer centers. C_LIO_LIThis study achieved high enrollment and retention rates across a medically and demographically diverse sample. C_LIO_LIInformed by multiple theoretical self-management models, this study will be able to examine predictors and transactional relationships of AYA survivor self-management, including engagement in pediatric and adult cancer-related long-term follow-up care. C_LIO_LIReliance on English-speaking AYA and those currently engaged with the health care system are limitations. C_LI

Introduction: Manual data extraction from unstructured clinical notes is labor-intensive and impractical for large-scale clinical and research operations. Existing automated approaches typically require large language models, dedicated computational infrastructure, and/or task-specific fine-tuning that depends on curated data. The objective of this study is to enable accurate extraction with smaller locally deployed models using a disease-site specific pipeline and prompt configuration that are optimized and reusable. Materials/Methods: We developed OncoRAG, a four-phase pipeline that (1) generates feature-specific search terms via ontology enrichment, (2) constructs a clinical knowledge graph from notes using biomedical named entity recognition, (3) retrieves relevant context using graph-diffusion reranking, and (4) extracts features via structured prompts. We ran OncoRAG using Microsoft Phi-3-medium-instruct (14B parameters), a midsize language model deployed locally via Ollama. The pipeline was applied to three cohorts: triple-negative breast cancer (TNBC; npatients=104, nfeatures=42; primary development), recurrent high-grade glioma (RiCi; npatients=191, nfeatures=19; cross-lingual validation in German), and MIMIC-IV (npatients=100, nfeatures=10; external testing). Downstream task utility was assessed by comparing survival models for 3-year progression-free survival built from automatically extracted versus manually curated features. Results: The pipeline achieved mean F1 scores of 0.80 +/- 0.07 (TNBC; npatients=44, nfeatures=42), 0.79 +/- 0.12 (RiCi; npatients=61, nfeatures=19), and 0.84 +/- 0.06 (MIMIC-IV; npatients=100, nfeatures=10) on test sets under the automatic configuration. Compared to direct LLM prompting and naive RAG baselines, OncoRAG improved the mean F1-score by 0.19 to 0.22 and 0.17 to 0.19, respectively. Manual configuration refinement further improved the F1-score to 0.83 (TNBC) and 0.81 (RiCi), with no change in MIMIC-IV. Extraction time averaged 1.7-1.9 seconds per feature with the 14B model. Substituting a smaller 3.8B model reduced extraction time by 57%, with a decrease in F1-score (0.03-0.10). For TNBC, the extraction time was reduced from approximately two weeks of manual abstraction to under 2.5 hours. In an exploratory survival analysis, models using automatically extracted features showed a comparable C-index to those with manual curation (0.77 vs 0.76; 12 events). Conclusions: OncoRAG, deployed locally using a mid-size language model, achieved accurate feature extraction from multilingual oncology notes without fine-tuning. It was validated against manual extraction for both retrieval accuracy and survival model development. This locally deployable approach, which requires no external data sharing, addresses a critical bottleneck in scalable oncology research.

BackgroundAdjuvant abemaciclib+endocrine therapy (ET) improves long-term outcomes in high-risk, hormone receptor-positive (HR+)/HER2-negative early breast cancer (eBC). However, treatment is frequently complicated by diarrhea, affecting adherence and quality of life (QoL). Increasing evidence suggests that abemaciclib-induced gastrointestinal toxicity may involve gut microbiota alterations. We conducted a prospective case-control pilot study evaluating the efficacy of MBR-01, a standardized prebiotic/probiotic formulation, in mitigating abemaciclib-induced diarrhea. MethodsWe enrolled 20 patients with high-risk HR+/HER2-negative eBC considered unfit for adjuvant chemotherapy. Patients received abemaciclib+letrozole (control, n=10) or abemaciclib+letrozole+MBR-01 (experimental, n=10). The primary endpoint was the incidence and severity of diarrhea; secondary endpoints included treatment adherence, QoL assessments and exploratory baseline/week-12 microbiota characterization according to treatment arm. Trial registration number: ISRCTN11948182. ResultsDiarrhea occurred in all patients. In the control group, diarrhea was predominantly grade 1 (50%) or grade 2 (40%), with one grade 3 event (10%). In the MBR-01 group, diarrhea frequency and severity were reduced by [~]70% at the end of week-12; 80% of patients experienced only grade 1 diarrhea or none by week-12, and no grade [&ge;]3 events. Dose modification was only required in one control. Alpha-diversity and depletion of F.prausnitzii were associated with earlier diarrhea onset and longer duration; enrichment in E.coli correlated with higher grade events. MBR-01 supplementation seemed to preserve microbial diversity and limited E.coli expansion. QoL was significantly improved with MBR-01. ConclusionMBR-01 may effectively mitigate abemaciclib-induced diarrhea, likely through the achievement of stabilization of gut microbiota composition. Larger prospective studies are warranted to validate these preliminary findings. HighlightsO_LIMBR-01, a prebiotic/probiotic, was given to reduce abemaciclib-induced diarrhea. C_LIO_LIMBR-01 reduced diarrhea by [~]70%, most patients had G0-1, one G [&ge;]3 at week 12. C_LIO_LIMBR-01 patients keep abemaciclib drug dose; 10% of controls required reduction. C_LIO_LIMBR-01 halved stool frequency and improved quality of life. C_LIO_LIMBR-01 preserved gut diversity, maintaining F. prausnitzii and limiting E. coli. C_LI

BackgroundQuality-of-life (QoL) assessment is essential in breast cancer care, yet limited evidence describes how interrelated QoL domains change during pharmacotherapy. This study aimed to evaluate correlations among functional and symptom scales using the EORTC QLQ-C30 and QLQ-BR23, highlighting their ability to reveal multidimensional QoL patterns. MethodsA prospective observational study was conducted in two second-referral hospitals in Indonesia, enrolling 106 female breast cancer patients. QoL was assessed before and after pharmacotherapy using QLQ-C30 and QLQ-BR23. Changes in scores ({Delta}) were computed, and interdomain relationships were analyzed using Spearmans rho. ResultsPhysical functioning correlated with role functioning ({rho} = 0.55, p <0.001), emotial functioning ({rho} = 0.33, p <0.001), and social functioning ({rho} = 0.31, p = 0.002). Role and social functioning were likewise correlated ({rho} = 0.32, p = 0.001), indicating that improvements across functional domains tended to occur in parallel. Symptom scales showed strong positive clustering, including fatigue with pain ({rho} = 0.37, p <0.001), insomnia ({rho} = 0.35, p <0.001), and systemic side effects ({rho} = 0.48, p <0.001). Functional and symptom domains generally exhibited inverse relationships: physical functioning negatively correlated with fatigue ({rho} = -0.40), pain ({rho} = -0.43), both p <0.001, and systemic side effects ({rho} = -0.26; p = 0.01). ConclusionThe QLQ-C30 and QLQ-BR23 instruments effectively captured structured, clinically meaningful interdependencies. Functional improvements consistently aligned with symptom reductions, revealing coherent functional-symptom clustering. These findings underscore the sensitivity of QoL instruments to detect multidimensional patient-reported changes during breast cancer pharmacotherapy.

PurposeCirculating tumor DNA (ctDNA) analyses are informative as an early indicator of immunotherapy response in advanced non-small cell lung cancer (NSCLC); however, the clinical value of ctDNA molecular response requires further validation. Patients and MethodsAs part of a prospective clinical protocol (NCT05995821), we conducted targeted error-correction sequencing of ctDNA (n=328) and matched WBC DNA (n=109) from 109 patients with metastatic NSCLC who received anti-PD-(L)1 either as monotherapy or in combination. Following cellular origin resolution of 2,818 variants, landmark molecular response (mR) was defined as undetectable ctDNA within 3-9 weeks of treatment initiation. ResultsPre-treatment ctDNA burden, but not blood tumor mutation burden, predicted survival. Implementing a tumor-naive WBC DNA-informed approach increased the number of evaluable cases without compromising the overall accuracy of landmark ctDNA molecular responses. A direct comparison of single-timepoint on-therapy ctDNA assessment with ctDNA dynamics from baseline to the 3-9-week interval, along with an analysis of heterogeneity in molecular response within the 3-9-week window, showed that undetectable ctDNA at the landmark timepoint can effectively predict survival outcomes. A significant enrichment in landmark ctDNA mR was noted among patients with progression-free survival (PFS) [&ge;]6 months with immunotherapy (p=2.5e-05) and chemo-immunotherapy (p=0.02). Patients in the landmark mR group had longer progression-free (p=1.6e-06) and overall survival (p=2.5e-05) than those with molecular progression. ConclusionsLandmark ctDNA molecular response provides a real-time, accurate approach for monitoring immunotherapy clinical outcomes. Although not currently validated for regulatory use, these findings demonstrate the potential utility of ctDNA as an early endpoint in clinical trials. Translational RelevanceEmploying circulating tumor DNA (ctDNA) dynamics as an early indicator of immunotherapy response requires a roadmap for the next-generation sequencing approach, definition of molecular response and establishment of its clinical sensitivity. In this study, we introduce the concept of a landmark ctDNA molecular response, determined 3-9 weeks after initiation of immunotherapy, that maximizes the number of evaluable patients without sacrificing the specificity of the approach. Notably, when evaluating heterogeneity in ctDNA detection within the landmark 3-9-week window and assessing the impact of landmark interval dynamics on survival, we found that a single ctDNA assessment performed similarly to multiple ctDNA measurements within the landmark window (most notably, regardless of whether the timepoints were concordant or discordant). Our findings demonstrate that a single assessment of early on-therapy landmark ctDNA molecular response, can identify patients at risk of disease progression and enable future intervention and therapy optimization.

Immunotherapy with immune checkpoint inhibitors and immunotherapy combined with chemotherapy have represented promising treatments for NSCLC patients leading to prolonged survival. However, the majority of patients with advanced NSCLC have a poor prognosis. The identification and development of biomarkers for stratifying responders and non responders to immune checkpoint inhibitors contribute to unravel the mechanism of immune checkpoint pathway and the immune tumor interaction underlying the responses and are urgently needed to improve clinical outcomes of immune checkpoint inhibitor treatment. In this study, we analyzed the clinical and gene mutation data of NCSLC patients treated with nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy (the immunotherapy treated group, n=119) and chemotherapy alone (the chemotherapy alone treated group, n=991) extracted from the MSK CHORD dataset. A DeevSurv model, a deep learning based extension of the Cox proportional hazards model was trained to generate survival risk score of each patient with binary statuses of thirty one gene mutations as input features into the model. The thirty one genes were selected based on population level mutation frequency, patient level variance in mutation status, and univariate Cox proportional hazards analyses evaluating the association between the presence or absence of each gene mutation and overall survival. The performance of the trained DeepSurv model was evaluated on the test set of the immunotherapy treated group using the concordance indexes (C index). The trained model was subsequently applied without retraining to the entire chemotherapy alone treated group as a control. The resulting C indexes for the immunotherapy treated group and chemotherapy alone treated group were 0.789 and 0.483, respectively. All patients within each group were divided into high and low risk groups according to the median predicted risk score. Kaplan Meier survival curves of high and low risk groups (n=43 vs n=70) in the immunotherapy treated group revealed a significant separation (log rank p<0.001), whereas no separation was observed in chemotherapy alone treated group (p=0.62). In the combined cohort of the immunotherapy treated group and chemotherapy alone treated group, the interaction between the DeepSurv derived risk score and treatment modality was significant (HR for interaction 1.47, 95% CI from 1.32 to 1.65, p<0.005), suggesting the DeepSurv derived risk score predictive value specific to the immunotherapy. Principal component analysis and permutation importance analysis were performed as complementary analyses to assess individual genes associated with the DeepSurv derived risk score and identified ZFHX3, SMARCA4, ALK, BTK, and NOTCH2 as major contributors to survival risk stratification. Collectively. we suggested that nonlinear coupling pattern of 31 tumor gene mutation statuses in the DeepSurv model captures the heterogeneity of survival risk among nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy treated patients with NSCLC which was visualized as clear separation between high risk and low risk groups divided by the median value of the risk scores.

ImportancePersistent racial and ethnic disparities in breast and prostate cancer mortality are well documented. Most prior studies emphasize between-group differences and rely on population averages or single composite measures of social disadvantage, which can obscure high-need communities within groups. How socio-behavioral determinants of health vary within groups across local gradients of cancer mortality remains incompletely characterized. A framework that combines race- and cancer-specific mortality with local, domain-level socio-behavioral profiles may help identify where burden is greatest and which specific barriers warrant prioritization. ObjectiveTo determine how socio-behavioral risk relates to breast and prostate cancer mortality within racial and ethnic groups and to characterize domain-specific behavioral profiles across low-, moderate- and high-mortality counties to inform targeted, equity-oriented cancer control strategies. DesignCross-sectional study of U.S. counties. Setting United States, county-level analysis. Participants3,141 U.S. counties, stratified within Non-Hispanic White, Non-Hispanic Black, and Hispanic populations. ExposuresCounty-level socio-behavioral determinants of health measured using a composite index comprising seven domains: community solidarity; education, health literacy, and digital connectivity; quality of care; housing and environmental risk; economic livelihoods; lifestyle behaviors; and touchpoints with care. Main outcomes and measuresRace/ethnicity-specific, age-adjusted breast and prostate cancer mortality rates (2018-2022) and county-level socio-behavioral risk scores. Counties were grouped into mortality tertiles within each race/ethnicity-by-cancer-stratum. ResultsAcross groups, higher socio-behavioral risk was associated with higher breast and prostate cancer mortality. For breast cancer, socio-behavioral risk increased monotonically across mortality tertiles for all groups, with the largest within-group increases among Hispanic and Non-Hispanic Black women. For prostate cancer, risk generally increased across mortality tertiles for all groups. Although Hispanic populations had lower population-average mortality, high-mortality Hispanic counties exhibited pronounced risk in lifestyle behaviors, economic livelihoods, and touchpoints with care. Domain patterns associated with high mortality varied by race, ethnicity, and cancer type, with touchpoints with care and economic livelihoods consistently prominent. Conclusions and relevanceWithin-group heterogeneity in socio-behavioral risk is substantial across U.S. counties. Linking population-specific, domain-level socio-behavioral profiles to cancer mortality may support more precise and equity-oriented cancer control strategies than reliance on group averages or composite indices. Key pointsO_ST_ABSQuestionC_ST_ABSWithin racial and ethnic groups, how do socio-behavioral determinants of health vary across US counties with low, moderate, and high breast and prostate cancer mortality? FindingsIn this cross-sectional study, higher county-level socio-behavioral risk was associated with higher breast and prostate cancer mortality across racial and ethnic groups. Race/ethnicity-specific, domain-level profiles revealed within-group heterogeneity, including persistently elevated risk among Non-Hispanic Black populations and pronounced domain-specific gaps in high-mortality Hispanic counties. MeaningLinking population-specific socio-behavioral profiles to local cancer mortality can guide more precise and equity-oriented prioritization of intervention domains and geographies than reliance on group averages or composite indices.

AO_SCPLOWBSTRACTC_SCPLOWLung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the tumor microenvironment. While multi-omics integration is essential for capturing this complexity, leveraging these data to explicitly define survival-associated subpopulations remains a significant challenge. In this study, we developed NeuroMDAVIS-FS, an unsupervised deep learning framework designed to stratify lung cancer patients by survival risk, and identify molecular determinants underlying improved clinical outcomes. Using the CPTAC cohort, we integrated genomic (CNV), transcriptomic (RNA-seq), and proteomic profiles to extract modality-specific features. Candidate biomarkers were validated through Kaplan- Meier (KM) survival analysis and univariate Cox proportional hazards (CoxPH) regression. A final multivariate CoxPH model effectively stratified patients into high-risk and low-risk cohorts (Kaplan Meier p-value < 0.001). Notably, the integration of these molecular features with baseline clinical models significantly enhanced prognostic accuracy, improving the concordance index by 43.79% in LUAD, 31.05% in LSCC, and 23.76% across the pan-lung cancer cohort. These results demonstrate that NeuroMDAVIS-FS identifies robust, biologically relevant features that surpass traditional clinical variables in predicting patient outcomes, offering a scalable path for precision oncology.