Citations

Abstract Background While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor‐intensive. Several auto‐segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear. Purpose This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose‐survival association in a multi‐institutional clinical trial dataset. Methods Three deep learning‐based auto‐segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan–Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high‐and low‐risk groups, and the cut‐off values were determined by minimizing mean absolute error (MAE) between DLAS‐derived and manual OS curves. Results All three models demonstrated significantly different OS curves between the high risk and low risk groups ( p < 0.05). The HR was 1.40 (95% CI: 1.02–1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03–1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03–1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut‐off. After optimizing the cut‐off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%). Conclusion Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group‐level overall survival stratification comparable to that previously reported using manual contours in the multi‐institutional cohort. Further validation with paired manual contours and pre‐specified cutoffs is warranted.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Abstract Background The left anterior descending (LAD) coronary artery is a critical organ‐at‐risk in thoracic radiotherapy. In thoracic stereotactic body radiation therapy (SBRT), sharp dose gradients and poor LAD contrast on 4DCT scans introduce uncertainty in LAD dose assessment. Methods We evaluated the performance of an AI‐generated LAD region structure as a surrogate for manual LAD contours in early‐stage NSCLC. We used deep inspiration breath hold (DIBH) scans from 49 patients with manually delineated LADs; planning risk volume contours were expanded by 3 mm. Then, 96 lung SBRT dose maps were registered by matching the left anterior portion of the heart contours. Dice similarity coefficient (DSC), inclusion ratios (IR), dose–volume histogram (DVH) metrics, linear regression, and linear mixed models were used to compare the structures. The correlation of the AI LAD region and LAD dose to mean heart dose (MHD) was assessed. Results The AI LAD region showed strong correlation with manual LAD contours for maximum dose (D1%, R 2 = 0.93) and volume receiving ≥10 Gy (V10Gy, R 2 = 0.81). The planning risk volume also demonstrated high correlation to the AI LAD region. MHD was a poor predictor of LAD dose ( R 2 ≈0.4). Additional dose metric correlations were assessed. Discussion To investigate dose volume effects to the LAD during radiotherapy for stage I NSCLC, a robust segmentation paradigm must be established. It must be easily implementable on typical imaging acquired during treatment, with an emphasis on clinical translatability. Our model has shown that the AI LAD region has a strong correlation with LAD dose under typical lung SBRT doses and supports its use for further investigation into LAD dose volume constraints during lung SBRT. Conclusion AI LAD region provides a clinically feasible and scalable alternative to manual LAD contouring for dose estimation in lung SBRT. Its strong correlation with key dose metrics validates its use in large‐scale retrospective and prospective studies.

Abstract Spatial transcriptomics has been widely applied to study the spatial distribution of cell types, cell states, and specific gene expression in tissue samples. However, we show that there is a prevalent transcript leakage problem in spatial transcriptomics data, where transcripts expressed by a cell diffuse to its neighborhood and are recurrently detected in the nearby cells. By analyzing published data sets, we show that this problem is general across data produced from different tissues and different species using different imaging-based and sequencing-based spatial transcriptomics platforms. It affects both upstream tasks such as expression quantification as well as downstream tasks such as cell-type annotation and detection of spatially-dependent gene expression. To tackle the transcript leakage problem, we propose a reference-free Bayesian model-based method, DeLeakage, which cleans up the data much more effectively than existing denoising methods. DeLeakage also improves cell-type annotation and avoids false detection of spatially dependent expression.

Abstract Nonmyeloablative-related haploidentical allogeneic stem cell transplantation (NMAC-HID allo-HSCT) has emerged as an additional treatment to achieve durable remission in sickle cell disease (SCD), a prevalent blood disorder characterized by painful vaso-occlusive crises and chronic anemia. The standard of care (SOC) for SCD includes hydroxyurea, pain management, and blood transfusion, but patients with SCD still lose several decades of life expectancy. Gene therapy (GT) for SCD is the other treatment for lifelong disease amelioration in SCD, with accessibility limited by cost and manufacturing capacity in the United States and globally. Two recent prospective studies that evaluated NMAC-HID allo-HSCT validated haploidentical allotransplantation as an efficacious and accessible treatment option in the era of GT. Given the ongoing price negotiation across jurisdictions for GT implementation and the absence of cost-effectiveness data comparing NMAC-HID allo-HSCT and GT, we conducted a cost-effectiveness analysis of NMAC-HID allo-HSCT vs GT vs SOC for adults and children living with SCD. The primary outcomes were the incremental cost-effectiveness ratio and the net monetary benefits across these 3 strategies. The secondary outcome was the maximum cost-effective threshold price for GT compared with NMAC-HID allo-HSCT. Treatment with SOC, NMAC-HID allo-HSCT, and GT accrued 14.3, 20.1, and 22.1 quality-adjusted life-years at costs of $1.22 million, $1.15 million, and $2.75 million, respectively. NMAC-HID allo-HSCT was the cost-effective strategy compared with GT in 100% of 10 000 Monte Carlo iterations across the base case and all scenario analyses. The maximum cost-effective thresholds for GT vs SOC were $1.4 million in the United States and $4200 to $22 000 across India, Nigeria, and Tanzania, depending on willingness-to-pay thresholds.

e12644 Background: Standard neoadjuvant therapy for HER2-positive early breast cancer combines multi-agent chemotherapy with HER2-targeted antibodies and is associated with substantial toxicity. HER2-directed antibody–drug conjugates (ADCs) may enable de-escalation. We compared efficacy and toxicity of neoadjuvant ADC-based regimens given without concurrent chemotherapy versus standard chemotherapy-based regimens, including by platinum use in the control arm. Methods: We searched PubMed, Embase, Cochrane CENTRAL, Scopus, and Web of Science through December 18, 2024 for randomized controlled trials in early/locally advanced HER2-positive breast cancer. Eligible trials compared neoadjuvant ADC-based regimens without concurrent chemotherapy versus standard chemotherapy-based regimens. Outcomes were pathologic complete response (pCR; per trial definition), grade ≥3 adverse events (AEs), and treatment discontinuation. Odds ratios (ORs) were pooled and heterogeneity was summarized with I². Results: Nine randomized trials (n = 2,021; mean age 50.7±13.7) were included. There was no significant difference in pCR between groups (OR 0.93, 95% CI 0.62–1.40; I² = 59.8%). ADC-based regimens were associated with fewer grade ≥3 AEs (OR 0.30, 95% CI 0.11–0.84; I² = 91.5%). Treatment discontinuation did not differ (OR 1.10, 95% CI 0.39–3.12; I² = 85.8%). Findings were consistent when stratified by platinum use in the control regimen. Conclusions: In HER2-positive early breast cancer, neoadjuvant ADC-based regimens without concurrent chemotherapy achieve pCR comparable to standard chemotherapy while substantially reducing severe toxicity without increasing discontinuation, independent of platinum use. Confirmation in phase III trials with mature survival endpoints is needed.

Abstract Maternal iron deficiency (ID) affects nearly 85% of all pregnancies worldwide by the third trimester and is strongly linked with maternal morbidity, poor birth outcomes, and cognitive and behavioral developmental deficits in children. Despite its burden on maternofetal health, no guidelines exist for routine screening of ID in pregnant individuals in the United States. The World Health Organization uses a diagnostic (ie, nonscreening) ferritin threshold of 15 μg/L for ID in the first trimester of pregnancy, with no guidance for the second and third trimesters. Routine screening for ID in pregnancy could identify a large proportion of mothers who remain undiagnosed and untreated under the current clinical status quo. Accordingly, we conducted, to our knowledge, the first cost-effectiveness analysis of ferritin-based ID screening in pregnant women in the United States, comparing thresholds of (1) 30 μg/L vs (2) 15 μg/L vs (3) no screening from a modified US societal perspective accounting for wages lost to infusion time, infusion administration costs, and the cost of annual hematology follow-up. We found that screening at 30 μg/L was the preferred strategy across extensive base-case, threshold, and sensitivity analyses and across all examined willingness-to-pay thresholds, with an incremental cost-effectiveness ratio of $34 000/quality-adjusted life year (QALY; 95% credible interval, $32 000-$36 200/QALY) compared with no screening. No parameter variation changed this outcome, and screening at 30 μg/L was favored in 100% of 10 000 Monte Carlo iterations. Population-level regular second- and third-trimester screening for ID using physiologically informed ferritin ranges should be considered for all pregnant women in the United States.

Background/Objectives: Accurate detection of pulmonary nodules on chest CT is critical for lung cancer screening, yet training robust detectors remains challenging due to the high cost of reliable annotations. In this work, we present a systematic study of curriculum learning for CT-based lung nodule detection on the enhanced LUNA25 benchmark and propose a hybrid curriculum learning framework for data-efficient optimization. Methods: Our approach estimates sample difficulty by fusing clinically interpretable handcrafted factors such as nodule size and count with model-driven signals such as prediction confidence from a teacher model, and constructs a three-stage progressive training curriculum from easy to hard samples. Using YOLOv11s as a strong baseline, the proposed hybrid curriculum is compared against conventional training without curriculum learning and against single-source curricula. Results: On the held-out LUNA25 test set, hybrid curriculum learning increases mAP50 from 0.672 to 0.696, mAP50–95 from 0.369 to 0.385, recall from 0.588 to 0.634, and precision from 0.725 to 0.764. Extensive data-efficiency experiments with proportional reductions (1/2, 1/5, 1/10) and fixed training samples (5000–40,000 slices) further confirm consistent gains across limited-data regimes. Conclusions: These results demonstrate that jointly leveraging intrinsic image complexity and optimization-aware feedback provides effective sample scheduling for robust and data-efficient lung nodule detection.

Abstract Background The van Herk margin formula, derived for 3‐D conformal radiotherapy with a uniform 3.2 mm dose penumbra and no intrinsic dose buffer, remains widely used to design CTV‐to‐PTV expansions in contemporary image‐guided prostate radiotherapy. However, coplanar VMAT techniques often feature broader penumbrae and explicit PTV–isodose clearances, potentially violating the assumptions underlying the original formulation and leading to overly conservative margins. Purpose We investigated whether the widely adopted van Herk margin formula overestimates clinical target volume (CTV)‐to‐planning target volume (PTV) expansions for contemporary coplanar volumetric modulated arc therapy (VMAT) prostate treatments. Methods Fifty consecutive intact‐prostate VMAT radiotherapy plans (two coplanar arcs; clinical margins 3 mm, except for 2 mm posterior) were exported. Direction‐specific 90% isodose–to‐PTV gaps and penumbra widths were measured. Candidate anisotropic margins were tested by eroding the PTV to create CTV_eval. Monte‐Carlo simulations combined systematic (Σ) shifts with Gaussian random (σ′) blurring kernels of 0–2 mm were performed. Acceptability criteria of (i) CTV Dmin0.03 cc ≥ 90% Rx in ≥ 90% of simulated scenarios or (ii) population tumor‐control probability (TCP) loss < 1 % were used. Results VMAT plans exhibited intrinsic 90% isodose clearances of 3–5 mm laterally/anteriorly and 1–2 mm superior–inferiorly, while axial‐plane penumbras were up to fivefold broader than van Herk's assumption. With a [0, 0, 2] mm (LR/AP/SI) margin, ≥ 90% of patients maintained Dmin0.03 cc ≥ 90% Rx provided Σ lay within an ellipsoid of [2.0, 1.5, 1.8] mm and σ′ ≤ [1.5, 2.0, 1.5] mm. Under TCP criteria the safe Σ ellipsoid for high‐risk disease was [2.5, 1.9, 2.2] mm, while low‐intermediate risk was even less sensitive. Conclusions For image‐guided coplanar VMAT prostate radiotherapy, an anisotropic [0, 0, 2] mm CTV‐to‐PTV margin is sufficient for target coverage. A modified margin expression that subtracts the measured isodose–to‐PTV gap and uses reduced random‐error coefficients better reflects modern practice.