Clinical and Translational Radiation Oncology

Purpose/Objective There is paucity of data reporting outcomes of breast cancers with initial internal mammary nodal involvement and no visceral metastases, treated with curative hypofractionated radiotherapy . We report the outcomes from a tertiary centre alongside spatial patterns of recurrences in the above group Material/Methods For this retrospective cross-sectional study, consecutive patients contoured as per the ESTRO 2013 guidelines, treated between 2016-2022 were eligible if their diagnostic imaging demonstrated involvement of the internal mammary nodes. Radiotherapy (40 Gy/15#/3 weeks) was delivered to the residual breast / thoracic wall, SCF region corresponding to the ESTRO lymph node level 4 and internal mammary chain nodes. Residual IMN/ level 4 nodes received a boost of 10Gy/5#. Spatial mapping of sites of recurrence at the local site and three nodal sites (axilla, SCF and IMN) was performed using deformable image registration. Sites of recurrence at the local site and three nodal levels were contoured separately. Volumetric intersection of the recurrent gross tumour volume (GTV_recurrence) with treated clinical target volume (CTV) was calculated. Actuarial overall (OS), disease free survival (DFS) & cumulative incidence of local (LR), regional (RR) and loco-regional recurrence(LRR) were calculated using Kaplan Meier method. Univariate comparison of outcomes with or without residual disease was performed using the log rank test. Results The median age of the 61 eligible women was 49 years. 77% received neoadjuvant chemotherapy and the rest adjuvant chemotherapy. 82% patients had a mastectomy. Axillary lymph node dissection was done in 96.7%. Boosts to residual IMN and SCF nodes were delivered to 21(34.4%) and 2 (3.3%) respectively. Median follow up was 3.6 years. Out of the 61 patients, 42 patients were disease free with an estimated 3 year disease free survival of 75% (95% CI 64, 88%). Spatial mapping of locoregional recurrence was possible in all but 1 patient with local (only) recurrence who was lost to follow-up after mammogram only. Among the patients with loco regional recurrence 1 had recurrence in local site + SCF +axilla, 3 had recurrence in the SCF+axilla, 2 in the SCF+IMN and 1 in the axilla+SCF+IMN. Only one patient had isolated axillary recurrence or isolated SCF recurrence. There were no IMN only recurrences. Among the 8 patients with nodal recurrence, a total of 27 individual GTV_recurrence were identified in the axilla(n=11), SCF(n=11) and IMN (n=5). IMN recurrences showed complete or partial overlap with CTV. SCF recurrences were a mix with predominantly in-field recurrences while axillary recurrences occurred outside the treated volume.Four (6.6%) patients had Grade 2 lymphoedema as documented late side effect. Conclusion Aggressive treatment of IMN disease with adjuvant radiation is effective with good locoregional control. Systemic recurrences are common and may benefit from intensification strategies.

Background. Accurate delineation of the gross tumour volume (GTV) - primary tumour (GTVp) and nodal disease (GTVn) - on FDG-PET/CT is a critical step of head and neck radiotherapy planning. Comparisons between lightweight custom networks and the auto-configured nnU-Net v2 are usually reported as end-to-end pipelines, conflating the contribution of the network with that of the inference-time post-processing applied on top of it. We separated the two. Methods. MiniUNet3D (custom 3D U-Net, 18.3 M parameters) and nnU-Net v2 (3d_fullres, 88.2 M parameters) were trained on the same 578 FDG-PET/CT cases (85/15 author-defined split of the HECKTOR 2025 Task 1 set, 8 centres) and evaluated on the same internal cohort. Three arms were compared pairwise: MiniUNet3D raw output at a fixed 0.5 threshold, MiniUNet3D with a locked adaptive post-processing pipeline, and nnU-Net v2. Comparisons used paired Wilcoxon tests with bootstrap confidence intervals, Bonferroni and Benjamini-Hochberg correction, and Cohen's d; catastrophic failure (Dice < 0.01) was compared with an exact McNemar test. Cases with an empty reference for a given target were excluded from that target's analysis (n = 98 GTVp, n = 93 GTVn). Results. With post-processing matched off, nnU-Net v2 was superior: median GTVp Dice 0.799 versus 0.592 (mean difference -0.244, 95 % CI -0.300 to -0.191; d = -0.88) and GTVn 0.774 versus 0.598 (d = -0.82). Post-processing raised MiniUNet3D to 0.800 (GTVp) and 0.738 (GTVn), recovering 79 % of that difference. Post-processed, MiniUNet3D matched nnU-Net v2 on GTVp Dice (p = 0.113) but remained inferior on nodal disease after Bonferroni correction (Dice p = 0.041; surface Dice p = 0.049). Catastrophic GTVp failures were 25/98 raw, 8/98 post-processed and 1/98 for nnU-Net v2 (McNemar p = 0.016). Inference took 34 s versus 78 s per case on the same GPU. Conclusions. Post-processing recovered most, but not all, of the difference between the two models, and it did not confer robustness: an eight-fold higher rate of empty contours on small primaries persisted, which is the more consequential difference for planning safety. Pipeline comparisons reported without a post-processing ablation risk attributing to a network what post-processing supplied.

Background: Primary malignant cardiac tumors (PMCTs) are rare and histologically heterogeneous. Objective: To compare demographics, specific ICD-O-3 morphologies, first-course treatment patterns, annual registered case counts, and unadjusted overall survival between soft-tissue and hematologic PMCTs. Methods: We identified 730 PMCT cases diagnosed from 2000 to 2021 in SEER 18 (ICD-O-3 topography C38.0). Histologic lineage was assigned from ICD-O-3 morphology. Comparative analyses included soft-tissue (n=458) and hematologic (n=212) tumors. First-course variables were primary-site surgery, chemotherapy (yes versus no/unknown), and radiotherapy (radiation versus none/unknown). Groups were compared with chi-square tests. Overall survival was estimated with Kaplan-Meier methods; follow-up was truncated at 120 months. Results: Soft-tissue PMCTs occurred predominantly at ages 45-64 years (67.9%), whereas hematologic PMCTs occurred predominantly at age [&ge;]65 years (63.2%; p<0.001). Men comprised 59.9% of hematologic and 49.3% of soft-tissue cases (p=0.014). The leading soft-tissue morphology was hemangiosarcoma/angiosarcoma (ICD-O-3 9120/3; 201/458, 43.9%); synovial sarcoma accounted for 20/458 cases (4.4%). Diffuse large B-cell lymphoma, NOS, accounted for 131/212 hematologic tumors (61.8%). Any primary-site surgery was recorded in 66.6% of soft-tissue versus 15.6% of hematologic cases (p<0.001). Chemotherapy was recorded in 67.5% versus 51.1% (p<0.001), and radiotherapy in 9.0% versus 20.5% (p<0.001). In exploratory Kaplan-Meier analyses, hematologic patients with recorded chemotherapy had higher unadjusted 120-month overall survival than those without recorded chemotherapy (42.0% versus 12.2%; log-rank p=7.5x10-). Radiation-associated survival differences were not statistically significant in either lineage. Conclusions: Soft-tissue and hematologic PMCTs have distinct age distributions, named histologies, and first-course treatment patterns in SEER. These findings describe registry coding and do not establish treatment effectiveness or population incidence.

Automated lesion segmentation is increasingly central to PSMA PET/CT interpretation, supporting staging, treatment planning, and response assessment at a scale that outpaces available nuclear-medicine expertise. However, automated PSMA-PET/CT whole-body lesion segmentation models are trained on images alone, with no knowledge of where in the body prostate metastases actually tend to occur. Radiologists use clinical domain knowledge of metastatic spread, but its absence in machine learning models produces false positives in anatomically implausible locations and missed lesions in high-risk sites such as the liver. In this work, we explore whether population-level spatial knowledge of metastatic spread can be used to augment deep learning segmentation predictions, and how such a prior should be fused with a network's output, without additional training. We build a data-driven metastasis atlas from 375 expert-annotated whole-body PSMA PET/CT scans and investigate its fusion with a trained segmentation network under a Bayesian framework, in which prediction probabilities from an nnU-Net-based lesion segmentation model serve as the likelihood and the data-driven atlas as the prior. Because metastases occupy only a small fraction of whole-body voxels, the atlas's peak probability is too low, and standard power-scaled or naive Bayesian pooling references lack the tools to deal with this shortcoming. This causes these standard fusion strategies to fail and, in the naive Bayesian case, to sharply degrade performance. We instead derive a calibrated, background-referenced log-odds fusion, one of many possible approaches to combine a population atlas with a deep learning model's predictions, distinct from classical multi-atlas label fusion in that it fuses a single population prior with a trained network's softmax rather than combining several registered atlases. Furthermore, this approach is neutral outside atlas support by construction, reduces exactly to the baseline network when unweighted, and requires no retraining. This atlas fusion significantly improved mean Dice over the baseline nnU-Net on a disjoint internal test set ($+0.011$, Holm-adjusted $p=0.021$) and on an independent external cohort ($+0.0129$, Holm-adjusted $p=3.8\times10^{-16}$), with lesion sensitivity improving from 0.849 to 0.861 internally and Dice improving over baseline in every stratified anatomic region, including the rare, high-risk sites motivating this work, while naive Bayesian pooling degrades performance sharply and power-scaled pooling underperforms it throughout. Our findings suggest that population-level spatial priors can meaningfully augment deep learning predictions in whole-body oncologic segmentation, provided the fusion rule is calibrated to where the prior actually carries signal.

Background: Prostate cancer enzalutamide treatment is approved at a standard dose of 160 mg daily. Concerns for real-world patients -- older and more fragile than those enrolled in clinical trials -- have prompted consideration of initiating treatment with lower doses, but the long-term efficacy of this approach remains unknown. We evaluate the long-term survival and longevity in patients treated with standard versus upfront low-dose enzalutamide. Methods: Retrospective analysis of 151 patients treated with enzalutamide (102 receiving 160 mg; 49 receiving [&le;]80 mg) between 2014--2021 at the Centre Hospitalier Universitaire de Martinique, with complete follow-up through end of life (98.7% completeness of follow-up). Primary outcomes were overall survival (OS), progression-free survival (PFS), and longevity (attained age). Results: Doses [&le;]80 mg were associated with longer median OS (36.3 vs. 20.7 months), improved restricted mean OS (difference of 0.7 years, p=0.05), and enhanced longevity (median 82.5 vs. 78.3 years, p=0.004). PSA response rate at 12 weeks was higher with lower-dose (71.4% vs. 48.8%, p=0.016). In multivariable models adjusted for prognostic factors, [&le;]40 mg compared with 160 mg was non-inferior regarding OS (HR=0.61, 95% CI 0.36--1.06), superior regarding PFS (HR=0.59, 95% CI 0.35--0.99), and superior regarding longevity (HR=0.48, 95% CI 0.28--0.84). Bone metastasis, poor performance status, PSA response, time to PSA nadir, and disease duration were independent predictors of outcomes. A post-hoc analysis revealed a strong association between dose and physician-prescribing profiles, ranging from "endorse-lowest-dose" to "never-deviate-from-full-dose". Conclusions: Lower doses of enzalutamide were non-inferior to full-dose. Dose-adapted strategies warrant further investigation.

IMPORTANCE: A recent meta-analysis showed that chemoimmunotherapy was associated with improved overall survival (OS) compared with immune checkpoint inhibitor (ICI) monotherapy for programmed death-ligand 1 (PD-L1) tumor proportion score (TPS) [&ge;] 50% advanced non-small-cell lung cancer (NSCLC). However, whether this benefit reflects chemotherapy effect or ICI heterogeneity remains unclear. OBJECTIVE: To reassess the survival benefit of adding chemotherapy to ICI monotherapy using agent-stratified comparisons anchored to chemotherapy. DATA SOURCES: The 24 phase 3 randomized clinical trials included in the original meta-analysis (search date, August 3, 2025). DATA EXTRACTION AND SYNTHESIS: Hazard ratios (HRs) for OS and progression-free survival (PFS) were extracted from each trial in the original meta-analysis. Two analytic frameworks were used: within-agent comparisons (same ICI in both chemoimmunotherapy and monotherapy) and across-agent comparisons (ICI in one treatment strategy only). For within-agent comparisons, a two-stage random-effects meta-analysis was conducted. In stage 1, ICI-specific HRs for chemoimmunotherapy and ICI monotherapy versus chemotherapy were pooled and their ratio was calculated (RHR = HRchemoimmuno/HRmono; RHR < 1 favors chemoimmunotherapy). The RHRs were pooled in stage 2. For across-agent comparisons, RHR was derived from pooled HRs by treatment strategy. MAIN OUTCOMES AND MEASURES: Endpoints were OS and PFS. RESULTS: In within-agent comparisons (4 ICIs; 13 trials; N = 3252), pooled RHR was 0.94 (95% CI, 0.78-1.13; P = .48; I2 = 0.0%) for OS and 0.85 (95% CI, 0.68-1.06; P = .14; I2 = 0.0%) for PFS. In across-agent comparisons (7 ICIs; 11 trials; N = 2231), RHR favored chemoimmunotherapy for OS (0.68; 95% CI, 0.50-0.92; P = .01) and PFS (0.46; 95% CI, 0.37-0.58; P < .001). In a sensitivity analysis restricted to trials of NCCN-recommended regimens, pooled RHR was 1.02 (95% CI, 0.81-1.28; P = .87) for OS. CONCLUSIONS AND RELEVANCE: In the within-agent comparisons, adding chemotherapy to ICI monotherapy did not improve OS or PFS in patients with PD-L1 TPS [&ge;] 50% advanced NSCLC. The benefit in the original meta-analysis appears driven by across-ICI heterogeneity. These findings are consistent with ICI monotherapy as a standard first-line option and underscore the need for agent-level stratification in across-trial comparisons.

Human deep-space missions face bone-kidney risks that cannot be extrapolated from six-month ISS data. We built a 12-state Ca-bone-urine-stone mechanistic ODE model and jointly calibrated its 11 physiological parameters on eight ISS targets by Bayesian identification (M0 base = 19-D; M1 extension adds a GCR-bone coupling term for parsimony testing only), then propagated the M0 posterior to four environments (ISS, Lunar subsurface, Lunar surface, Mars). Lumbar-lower BMD loss increases with mission duration and partial-gravity unloading (ISS 180 d -4.83% -> Mars 730 d -12.15%; 2^3 factorial: duration 82.9%, gravity 12.5%, GCR main effect ~ 0), whereas stone rate follows the opposite gradient (ISS 16.1 vs Mars 13.1 per 1000 person-years), reflecting weakened partial-gravity bone resorption alongside residual urinary chemistry changes. The dominant pathway thus shifts from bone-centric on the ISS to kidney-centric on Mars, where residual urinary-chemistry changes-not bone resorption-drive stone risk. The direct GCR-bone coupling term is unidentifiable at current ISS doses (DeltaWAIC = +0.0076 +/- 0.126 SE), so M0 is retained as the main inference model. Bisphosphonates provide >=84% BMD protection but leave a urinary-chemistry residual, so bisphosphonate monotherapy would underestimate Mars stone risk; potassium-magnesium-citrate combinations (RRR_RSS 51%) should therefore be added to deep-space countermeasures. A Lunar-surface 365-day mission is the earliest environment on the NASA roadmap to cross a composite RED threshold. That profile differs from the regolith-shielded 180-day case in both cumulative GCR (~69x) and duration (2x), so a shielding-specific effect cannot be isolated here; forcing the GCR coupling terms to zero leaves all four composite tiers unchanged (0/4, Supp S24), and the shielded 180-day profile is YELLOW rather than GREEN. Independent hold-out validation (Culliton 2025 60-day HDT-bedrest RCT, n=8 control arm of n=24 total) supports the M0 posterior predictive distribution on the lumbar-BMD sub-scope.

Dynamic positron emission tomography (PET) with long axial field-of-view (LAFOV) scanners enables multi-organ imaging and kinetic quantification beyond static (late-phase) imaging; however, the long times typically required for dynamic acquisitions remain clinically impractical. This study evaluates a deep learning (DL) framework to enable abbreviated dynamic PET acquisitions, comparing single-time-window (STW, early dynamic data only) and dual-time-window (DTW, early dynamic data plus a late 5-min static frame) protocols with early dynamic scan durations of 5-30 min and dose levels ranging from 360 MBq to 18 MBq. Seventeen 60-min dynamic [18F]FDG datasets were first motion-corrected using a staggered FALCON pipeline and then used to train and test a spatiotemporal DL model for autoregressive frame prediction. Performance was assessed across the full quantitative workflow, from DL-predicted frames and time-activity curves to organ-based kinetic modeling and voxel-wise parametric imaging in multiple tissues and two patient cohorts. DTW protocols consistently outperformed STW, better preserving late-phase kinetics. For a 15-min early dynamic scan, adding a late 5-min scan reduced mean absolute Ki difference from 23% (STW) to 17% (DTW) in the liver and from 26% to 15% in the thalamus. DTW + DL further reduced errors to [&le;]10% in the liver, thalamus, and breast lesion, and 16% in muscle. Our recommended protocol, 15-min early dynamic scan plus a 5-min late scan with DL, remained robust to up to a 5-fold dose reduction (~74 MBq). Overall, these findings support DL-enabled abbreviated, low-dose dynamic LAFOV PET as a clinically feasible approach for accurate kinetic quantification

Background: Biochemical recurrence (BCR) occurs in 20-40% of men after radical prostatectomy. Existing postoperative recurrence risk tools based on PSA and pathology are clinically useful but show only moderate and variable discrimination, highlighting the need for biomarkers that improve risk stratification and consequent treatment decisions. We hypothesized that the prostate microenvironment, including both the tumor and non-cancerous adjacent tissue, may contain prognostic features associated with adverse postoperative PSA outcomes. Methods: We assembled a cohort of matched tumor-adjacent benign and tumor prostate tissue from 243 men across three institutions to establish a discovery cohort (n=123; 43 postoperative PSA events, 35%) and validation cohort (n=120; 46 events, 38%). For primary binary analyses, a postoperative PSA event included BCR, defined as two consecutive postoperative PSA values >=0.2 ng/mL, or PSA persistence. We performed RNA sequencing of matched tumor-adjacent benign and tumor tissues, quantified immune signatures, and developed an integrated model combining the adjacent-tissue B-cell signature, preoperative PSA, and radical prostatectomy Gleason score (BRIGADE). CAPRA-S-adjusted Cox analyses excluding recurrence-time-0 cases evaluated time to BCR, and CD19 multiplex immunofluorescence provided tissue-level confirmation (n=10). Results: In prostatectomy specimens, tumors from patients without a postoperative PSA event were enriched for B-cell transcriptional programs, whereas tumors from event-positive patients showed elevated proliferation signatures. B-cell-related transcriptional programs were correlated between tumor and adjacent tissue. Tumor-adjacent benign B-cell scores were higher in no-event cases and discriminated postoperative PSA-event status in PCBN discovery (AUC 0.63) and BM validation (AUC 0.81) cohorts, outperforming numerous other immune-related signatures. In CAPRA-S-adjusted Cox sensitivity analyses excluding recurrence-time-0 cases, higher adjacent-tissue B-cell activity was associated with reduced recurrence risk in PCBN (HR 0.42, 95% CI 0.19-0.94; BH-adjusted p=0.035) and BM (HR 0.54, 95% CI 0.30-0.95; BH-adjusted p=0.034). Tissue-based validation showed that CD19+ B-cell density in adjacent benign tissue was higher in no-event than event-positive patients (median 0.1145 vs 0.0471; p=0.008). BRIGADE achieved an AUC of 0.68 in cross-validation and 0.83 in independent validation, compared to AUCs of 0.54-0.63 and 0.44-0.78 for the tested clinical predictors, respectively. At the fixed classification threshold, the validation-cohort odds ratio for BRIGADE was 2.75. The adjacent B-cell score remained associated with lower odds of a postoperative PSA event after adjustment for PSA and Gleason score. Conclusions: B-cell infiltration in tumor-adjacent benign prostate tissue may complement existing clinicopathologic models for stratifying adverse postoperative PSA outcomes and subsequent BCR after radical prostatectomy. The transcriptomic signal was recapitulated by CD19-based tissue staining, supporting further development of a pathology-based assay.

Diffusion models achieve high reconstruction quality in low-dose computed tomography (LDCT), but their iterative sampling trajectories impose substantial computational costs. Unlike unconditional generation, paired LDCT reconstruction starts from an image that already contains the anatomy and spatial structure of the standard-dose CT (SDCT) target; reconstruction primarily requires correcting dose-related noise and artifacts. We therefore introduce Residual Endpoint Flow Matching (REFM), an LDCT reconstruction method that learns to transport an LDCT image directly toward its paired SDCT endpoint rather than defining a noise-to-image trajectory. REFM predicts the residual velocity along linear interpolations between both images and supports single-step and multi-step reconstruction using the same trained network. We evaluate five model capacities using 1 to 50 Euler steps against deterministic U-Net and diffusion-based baselines. Across all REFM variants, one-step inference consistently provides the highest reconstruction quality. On the TCIA validation set, REFM Base achieves 50.98 dB PSNR and 0.9865 SSIM at 94.54 fps, compared with 50.92 dB, 0.9847, and 9.26 fps for DDPM-10. REFM Small retains 50.71 dB while increasing throughput to 198.56 fps. Without fine-tuning, REFM Base also matches the 25-step DDPM baseline on the external Mayo Clinic dataset, although DDPM remains stronger on synthetically degraded CRLM images. Thus, our results show that exploiting paired anatomical correspondence enables diffusion-level LDCT reconstruction with a single step reconstruction.

Background Late-stage cancer incidence is being considered as an earlier endpoint in cancer-screening trials, but its trial-level association with cancer-specific mortality may depend on evidence selection and endpoint harmonization. We evaluated the robustness of this association to source-verified additions. Methods We reconstructed the PubMed corpus underlying a 41-comparison review. Gemini 3.1 Pro Preview was used only to prioritize reports for blinded human reassessment. Reviewers determined eligibility, linked reports from the same trial, harmonized endpoints, and verified comparison-level data. We recalculated unweighted Pearson correlations overall and by cancer type after adding earliest-compatible trial comparisons. Results Among 1209 candidate records, 996 PDFs were assessed. Thirty-three reports absent from the source review were prioritized; 26 were eligible, representing 18 trials, and 8 provided compatible comparisons. Adding these comparisons increased the dataset from 41 to 49 and attenuated the overall correlation from 0.73 (95% confidence interval [CI] = 0.55 to 0.85) to 0.59 (95% CI = 0.37 to 0.75). Updated correlations were 0.49 (95% CI = -0.26 to 0.87) for breast, -0.23 (95% CI = -0.71 to 0.40) for colorectal, and 0.83 (95% CI = 0.54 to 0.95) for lung cancer. One sparse-event comparison influenced the colorectal estimate. Conclusions The overall association was sensitive to evidence composition, and cancer-specific stability varied. Late-stage incidence should be evaluated by cancer type and with prespecified sensitivity analyses for evidence selection and endpoint definitions. Model-assisted prioritization cannot replace human eligibility review, trial reconciliation, and source verification.

Purpose: The primary objective of the Stanford Knee Osteoarthritis PET/MRI Evaluation (SKOPE) study is to develop and evaluate a multimodal, dynamic [18F]NaF PET-MRI framework for characterizing whole-joint physiology and its relationship to osteoarthritis (OA) risk, pain, and disease progression. Specifically, we aim to integrate dynamic PET with quantitative and anatomical MRI, to characterize structural, compositional, and metabolic features across the knee and surrounding musculoskeletal system, evaluate acute tissue responses to exercise, and identify imaging biomarkers associated with OA risk, pain, and disease progression. Methods: The SKOPE study includes multimodal PET-MRI of the knee and surrounding musculoskeletal tissues, with imaging of the knee, tibia, ankle, thigh, hip, pelvis, and lumbosacral spine. Dynamic [18F]NaF PET is combined with conventional anatomical MRI and quantitative MRI techniques, including quantitative double-echo steady-state (qDESS) T2 mapping of cartilage, Dixon fat-fraction imaging, ultrashort echo time (UTE) T2* mapping of short-T2 tissues, UTE imaging of tibial bone, and zero echo time (ZTE) imaging for bone morphology and pseudo-CT generation. Additional MRI sequences characterize muscle composition, bone and joint anatomy, intervertebral discs, and regional vascular anatomy. Selected scans are acquired before and after a standardized exercise protocol to assess the acute physiological response of the joint. Automated segmentation is used to generate subject-specific masks of muscles, bones, vertebrae, and intervertebral discs. A subset of the MRI protocol is repeated at 1- and 2-year follow-up to assess longitudinal changes. Expected Impact: By combining dynamic bone metabolic imaging with quantitative measures of cartilage, menisci, muscle, bone, fat, vascular structures, and the spine and hip, the SKOPE protocol provides a whole-joint and multijoint framework for studying the structural, metabolic, and physiological processes associated with OA and pain. Exercise and longitudinal imaging further enable assessment of acute tissue responses and changes over time, supporting the development of quantitative imaging biomarkers for OA risk, pain, and disease progression.

Background: Low-dose computed tomography (LDCT) screening reduces lung cancer mortality but is limited by low uptake and associated with high rates of false-positives and indeterminate-nodules. Breath volatile organic compound (VOC) analysis is a non-invasive candidate biomarker approach that could complement LDCT, but prior work has relied on laboratory-based high-resolution mass spectrometry (HRMS), limiting point-of-care deployment. Methods: In this pilot study, breath samples were collected from 40 patients with treatment-naive, pathologically confirmed non-small cell lung cancer (NSCLC) and 25 lung-cancer-screening-eligible healthy controls. Paired samples were analyzed via a compact point-of-care GC-MS platform (CLARION) and a laboratory HRMS reference. Diagnostic classification models were built independently for each platform using elastic net logistic regression with leave-one-out cross-validation, and performance was evaluated by area under the receiver operating characteristic curve (AUC). Results: CLARION identified 103 VOCs across breath specimens, compared to over 900 identified by HRMS. Despite this difference in panel size, CLARION achieved diagnostic performance nearly identical to HRMS for distinguishing NSCLC cases from controls (AUC 0.864 vs. 0.863). Compared to controls, performance statistics were similar for early-stage NSCLC (AUC 0.854 vs. 0.841) and adenocarcinoma (AUC 0.770 vs. 0.787). VOCs of interest include p-cymene, phenol, propylbenzene, tetradecane, {beta}-ocimene, 2,3-dihydro-indole, and 1-methylthio-(Z)-1-propene. Conclusion: A compact, point-of-care breath GC-MS platform achieved diagnostic performance for NSCLC detection comparable to a laboratory HRMS reference despite a substantially smaller detected VOC panel. These findings support continued development of point-of-care breath VOC testing as a non-invasive, field-deployable complement to LDCT-based lung cancer screening.

Outcomes for high-grade pediatric brain tumor patients remain poor, but there is optimism that chimeric antigen receptor (CAR) T cell therapy can improve prognosis. We present the results from a phase I clinical trial of IL13BBz-CAR T cells infused weekly into the cerebral ventricles in pediatric and young adult patients with recurrent or refractory brain tumors. The trial met its primary objectives of feasibility, safety, and tolerability, with one dose-limiting toxicity. 8 of 16 patients evaluable for response experienced radiographic size decreases consistent with biologic activity and with an anti-tumor response. Two patients met protocol criteria for response. Median survival for patients receiving lymphodepletion was 20.5 months from diagnosis and 6.9 months from treatment for patients with midline glioma, and 187 months from diagnosis and 7.5 months from treatment for patients with ependymoma. Importantly, patients who did not receive lymphodepletion developed anti-CAR humoral and cellular immune responses detectable in the CSF and peripheral blood, whereas patients receiving lymphodepletion had no evidence of CSF anti-CAR immunity. Taken together, these findings demonstrate the safety, tolerability, and biological activity of locoregionally-delivered IL13BBz-CAR T cells for children and young adults with CNS tumors. Moreover, we show that anti-CAR immune responses arise in patients not receiving lymphodepletion, but not in the CSF of patients receiving systemic lymphodepletion. Further investigation of adoptive cellular therapies combined with immunosuppression is warranted in this patient population. ClinicalTrials.gov registration: NCT04510051.

Motivation: Cell-free DNA methylation provides a minimally invasive signal for early cancer detection and tissue-of-origin prediction. Most methods represent methylation measurements as independent fixed-window features and therefore do not explicitly model relationships among genomic regions. Results: We developed PANGEM (Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome), a graph-learning framework that represents genomic bins as nodes and integrates CpG context, genomic proximity, and sample-specific methylation similarity in the graph topology. Across five repeated stratified train-test splits, PANGEM achieved the highest mean performance among evaluated methods, with an AUROC/AUPR of 0.997/1.000 for binary cancer detection and macro-AUROC/AUPR of 0.977/0.870 for multiclass tissue-of-origin prediction. In the independent INSPIRE cohort, 72 of 78 cancer cases (92.3%) exceeded the binary classification threshold, and PANGEM correctly classified 9 of 17 head and neck cancer cases (52.9%), the highest accuracy among evaluated methods. Subnetwork analysis further identified recurrent, graph-connected methylation patterns, including a 111-DMR subnetwork with increased methylation in cancer samples.

Chemoradiotherapy (CRT) is the standard-of-care therapy for many solid malignancies, yet predictive biomarkers of treatment response remain limited. We identified a germline single nucleotide polymorphism (SNP) in an intrinsically disordered region of the lysine demethylase KDM3C/JMJD1C (p.S464T) that is associated with CRT outcomes in locally advanced rectal cancers (LARC) and head and neck squamous cell carcinoma (LA-HNSCC). In silico modeling with AlphaFold predicted S464T substitution influenced interaction between phosphorylated KDM3C and RNF8 FHA domain. In cellular models, conversion of S464 to T464 increased sensitivity to DNA-damaging agents. S464T substitution impaired damage-induced MDC1-RAP80 signaling and downstream RAP80-BRCA1 colocalization. SNP carrying cells impaired DNA repair causing genotoxic stress that is associated with increased cGAS-cGAMP innate immune signaling and increased apoptosis. Population analyses with the SNP highlighted an increase incidence of UV-induced skin and other cancers, linking inherited variation in the chromatin regulatory gene KDM3C to genome instability, cancer risk, and therapeutic vulnerability.

To evaluate the cost-utility and 5-year budget impact of first-line olaparib plus abiraterone versus abiraterone alone for metastatic castration-resistant prostate cancer (mCRPC) in China after the eleventh round of volume-based procurement (VBP). The intention-to-treat (ITT) population was assigned primary decision-analytic weight; the prespecified BRCA1/2-mutated (BRCAm) subgroup was a supporting analysis.

Background: Fluctuations in pain intensity are intrinsic to non-specific chronic low back pain (nsCLBP). Nevertheless, pain fluctuations have rarely been considered when investigating pathophysiological mechanisms. Therefore, a novel study protocol was developed and implemented to systematically assess the impact of fluctuating pain states on pain-related measures. Methods: The final study cohort consisted of 45 nsCLBP patients and 47 age- and sex-matched healthy controls (HCs). Patients participated in three visits, conducted during different pain states (i.e. clinically relevant pain, low-intensity clinical pain / pain-free, clinically irrelevant pain induced using a Qutenza 8% capsaicin patch). Pain fluctuations were monitored through online assessments every four days and guided the pseudorandomized visit scheduling. HCs participated in a single visit. Each study visit comprised a multimodal battery of pain-related measures. Results: 93.33% of patients completed all three visit types in a pseudorandomized order (chi-squared=1.50, p=0.826). Visit scheduling was possible due to the high self-report adherence (median=93.48%), unrelated to self-report burden (rho=-0.097, p=0.53). Study visits were conducted during different pain states, as indicated by: i) the significantly higher low back pain intensity in the clinically relevant pain visit (mean[SD]: 3.98[0.90]), compared to the low-intensity clinical pain (1.03[0.86]) and clinically irrelevant pain (1.13[0.82]) visits (p-values<0.001), as well as by ii) the successful induction of a moderate-to-high clinically irrelevant pain across assessments. Conclusion: Despite scheduling complexity and pain state transition uncertainty, a pain state-dependent pseudorandomized study design is feasible and could improve the understanding of nsCLBP mechanisms.

Objective: Whether a scheduled antiseizure medication (ASM) continues on schedule across the ICU-to-floor transfer has not been characterized. We quantified ASM administration-gap frequency across this transfer and compared it with gap frequency during matched non-transfer intervals in the same patient and drug. Methods: In this retrospective MIMIC-IV (version 3.1) cohort study, we identified epilepsy and status-epilepticus admissions with an ICU stay followed by floor transfer and a scheduled ASM order active at ICU departure. A gap was defined as an interval exceeding 1.5 times the expected dosing interval between the last ICU dose and first floor dose, or no further dose before discharge, and compared with a matched non-transfer control interval in the same patient and drug (paired McNemar test). A multivariable model evaluated six prespecified clinical predictors; sociodemographic variables were summarized descriptively. Results: Among 2,469 ASM transition-by-drug observations (1,583 admissions, 1,335 patients), an administration gap occurred in 251 (10.2%; 95% CI, 8.7%-11.7%). Gap frequency across the transfer exceeded frequency during matched non-transfer control intervals in the same patient and drug: a paired rate difference of 5.8 percentage points (95% CI, 4.4-7.1; 7.5% vs 1.7%; P = 7.3 x 10^-22) before the transfer and 6.4 percentage points (95% CI, 4.9-7.9; 8.9% vs 2.5%; P = 1.9 x 10^-23) after. Gap rates were similar for intravenous-available (9.9%) and oral-only (11.4%) drugs (rate difference, 1.5 percentage points; 95% CI, -1.6 to 4.5; P = .34). None of six prespecified predictors reached significance after correction. Significance: An antiseizure medication administration gap occurred in approximately 1 of every 10 drug-transition observations at the ICU-to-floor transfer, exceeding matched non-transfer gap rates by 5.8 to 6.4 percentage points. This transfer-associated excess, rather than any single medication or patient characteristic, supports a structured medication-continuity check.

Routine childhood growth surveillance is a cornerstone of pediatric care. Growth pattern abnormalities are often early manifestations of chronic disease. Yet subtle abnormalities are frequently underrecognized, leading to diagnostic delays and avoidable morbidity. We introduce SPROUT (System for Pediatric Recognition Of Undiagnosed Trajectories), a generalized, multi-agent large language model (LLM) reasoning system designed to identify a broad spectrum of pediatric growth-related conditions from longitudinal electronic health records (EHRs) earlier than standard clinical practice. Using a large pediatric primary care EHR dataset, we developed and validated SPROUT as a two-stage system. First, a highly specific LLM screener flags concerning longitudinal growth patterns. Second, an Orchestrator module coordinates a multidisciplinary panel of LLM agents to generate a ranked differential diagnosis. To correct systemic reasoning errors, a Trainer module injects meta-knowledge into the panel via a dedicated "Learner" agent. Diagnostic capability was evaluated using a walk-forward, visit-by-visit simulation leading up to the diagnosis date. The SPROUT screener model achieved 98% (83/85) specificity and 28% (9/32) sensitivity on a gold-standard dataset of pediatric primary care patients when evaluated one year before the index date, and 100% specificity and 47% sensitivity when evaluated using longitudinal data up to the day of diagnosis. When applied to 300 control patients (i.e., healthy or undiagnosed), the screener flagged 15. Subsequent expert panel review confirmed high suspicion for undiagnosed pathology in 33% (5/15) of these cases. In chronological walk-forward validation on disease cases, the diagnostic engine identified conditions well before standard-of-care documentation. One year prior to clinical diagnosis, the system achieved sensitivities of 81% for type 1 diabetes mellitus, 56% for pituitary disorders, and 44% for celiac disease. The SPROUT multi-agent system demonstrates the ability to detect a significant portion of latent growth-related pediatric conditions months to years before current clinical standards while minimizing false positives. These results support its potential as a decision support tool for reducing diagnostic delays in pediatric care.