The widespread adoption of computed tomography has increased the detection of lung nodules. However, deep learning methods for classification of benign and malignant nodules often fail to comprehensively integrate global and local features, and most of these methods have not been validated through clinical trials. Here we developed DeepFAN, a transformer-based model trained on more than 10,000 pathology-confirmed nodules, and conducted a multireader, multicase clinical trial (Chinese Clinical Trial Registry: ChiCTR2400084624) to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) values of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on a clinical trial dataset involving 400 cases across three independent medical institutions. Explainability analysis indicated higher contributions from global than local features. The average performance of 12 readers improved significantly: by 10.9% (95% CI 8.3-13.5%) for AUC, 10.0% (95% CI 8.9-11.1%) for accuracy, 7.6% (95% CI 6.1-9.2%) for sensitivity and 12.6% (95% CI 10.9-14.3%) for specificity (all P < 0.001). Nodule-level interreader diagnostic consistency improved from fair to moderate (overall κ: 0.313 versus 0.421; P = 0.019). These results indicate that DeepFAN can effectively assist junior radiologists and could help to homogenize diagnostic quality and reduce unnecessary follow-up of patients with indeterminate pulmonary nodules.

Abstract Purpose To compare intrapulmonary vessel volume (IPVV) on computed tomography pulmonary angiography (CTPA) between vasculitis patients with pulmonary vascular involvement and CTPA-negative subjects. Methods This study included 207 vasculitis patients with pulmonary vascular involvement between March 2019 and November 2024 and 202 CTPA-negative subjects between February 2019 and February 2025. A computer-aided pulmonary vascular segmentation algorithm was employed to automatically measure total intrapulmonary vessel volume (TIPVV), intrapulmonary arterial vessel volume (IPVVa) and intrapulmonary venous vessel volume (IPVVv). Additionally, IPVVs were analyzed and compared within five specific vessel diameter groups: 0.8–1.6 mm, 1.6–2.4 mm, 2.4–3.2 mm, 3.2–4.0 mm, and > 4.0 mm. Results TIPVV and IPVVv showed no significant differences between groups. The IPVVa measured in CTPA-negative subjects was 47.79 (42.48, 54.10) mL·m− 2, while that in vasculitis patients with pulmonary vascular involvement was 44.86 (39.33, 52.58) mL·m− 2. The IPVVa in vasculitis patients with pulmonary vascular involvement was significantly lower than that in CTPA-negative subjects (p < 0.01). In pulmonary arteries with diameters of 0.8–1.6 mm and 2.4–3.2 mm, the IPVVa in vasculitis patients with pulmonary vascular involvement was lower than that in CTPA-negative subjects (p < 0.05). In pulmonary veins with diameters of 1.6–2.4 mm and 3.2–4.0 mm, the IPVVv in vasculitis patients with pulmonary vascular involvement was higher than that in CTPA-negative subjects (p < 0.05). Conclusions The computer-aided pulmonary vascular segmentation algorithm can automatically measure IPVV, enabling quantitative assessment of small pulmonary vessel involvement in vasculitis.

Purpose:Deep learning has achieved remarkable progress in low-dose computed tomography (LDCT) denoising; however, radiologists struggle to trust black-box models they cannot verify or control. Zero-shot methods eliminate training data requirements but fail on computed tomography's (CT) spatially correlated noise. We demonstrate that a transparent mathematical operator, when made content-adaptive, can match deep learning performance while remaining fully interpretable. Approach:We introduce Filter2Noise (F2N), which replaces conventional deep networks with an attention-guided bilateral filter that adapts to local anatomy. A lightweight attention module (3.6k parameters) predicts optimal filtering strategies for each image region by analyzing tissue type, texture, and noise characteristics. To enable robust learning from a single noisy image with correlated noise, we develop Euclidean local shuffle, which strategically disrupts noise correlations while preserving anatomical structure, and a multi-scale self-supervised loss that enforces consistency across resolutions. Results:On the Mayo Clinic LDCT Grand Challenge, F2N achieves 39.76 dB peak signal-to-noise ratio, outperforming the next-best zero-shot method by 1.88 dB, while using 360× fewer parameters (3.6k versus 1.3M). Clinical validation on photon-counting CT demonstrates that F2N elevates low-dose images to full-dose quality (no statistically significant difference in contrast-to-noise ratio, p = 0.10 ). The learned filtering strategy is fully visualizable: parameter maps reveal content-aware behavior. Radiologists can interactively adjust these parameters post-training to refine denoising in diagnostically critical regions. Conclusions:F2N reconciles competitive performance with complete interpretability and user control, providing radiologists with a verifiable tool that works across scanners and protocols without retraining.

BACKGROUND:The redistribution of pulmonary blood volume (PBV) across COVID-19 severity levels and its prognostic value for the less pathogenic, predominantly upper respiratory tract-infecting Omicron variant remain unclear. This study investigates PBV distribution patterns and validates its predictive utility for Omicron outcomes. METHODS:This retrospective study enrolled consecutive patients (November 2022-January 2023) with baseline CT and clinical data, followed for six months. Patients were divided into mild/moderate (MM) and severe/critical (SC) groups according to COVID-19 severity. Pre-trained deep learning algorithms quantified total, lobar, and vessel-size-specific PBV. Adjusted multivariable analyses determined odds ratios (OR) for clinical outcomes, and logistic regression models based on PBV were constructed to predict adverse events. RESULTS:Among 921 patients (61 ± 20 years, 460 men), 755 were in the MM group and 166 in the SC group. Compared to MM patients, SC patients showed significantly lower total PBV (259 mL vs. 239 mL, p = 0.002) and redistribution from lower to upper lobes (upper vs. lower; MM, 21% vs. 23%; SC, 23% vs. 18%) and from small-calibre (≤5 mm2, 44% vs. 32%, p < 0.0005) to large-calibre (>10 mm2, 39% vs. 51%, p < 0.0005) vessels. PBV (especially in vessels ≤5 mm2) predicted six-month composite outcomes (OR = 4.66, AUC = 0.79, sensitivity = 92%) and mortality (OR = 3.34, AUC = 0.75, sensitivity = 93%) for the Omicron variant with high sensitivity, but at a higher risk threshold (42%) than that reported for more pathogenic variants in previous publications. CONCLUSIONS:Severe/critical COVID-19 is associated with reduced PBV and its redistribution across lung regions and vessel sizes. PBV retains predictive value for clinical outcomes in the immune-evasive Omicron variant.

Background:Following the COVID-19 pandemic, co-infections with multiple respiratory viruses have become increasingly common, complicating the accurate prediction of disease progression and prognosis. This study assessed the use of computed tomography (CT)-derived body composition parameters combined with clinical risk factors to predict the severity and short-term adverse outcomes of viral pneumonia. Methods:A total of 140 hospitalized patients with viral pneumonia who had undergone chest CT were retrospectively included and stratified into severe and non-severe groups. Body composition, including visceral and subcutaneous adipose volumes (VAV and SAV) and erector spinae volume (ESV), was measured at T4, T8, and T12 costovertebral joint levels and the corresponding whole-vertebral level using 3D-Slicer. Serological indicators and 30-day adverse outcomes were recorded. Clinical, imaging, and integrated models were developed to distinguish patients' severity and outcome. Results:The median age was 77 years (IQR: 68-85), with 91 (65%) patients being male. Sixty-five (46.4%) patients were severe patients and 26 (18.6%) had adverse outcomes within 30 days. Lower T12-ESV and higher T12-VAV were associated with viral pneumonia severity after adjusting for confounders (OR = 0.823, 95% CI [0.733-0.924], p = 0.001; OR = 1.030, 95% CI [1.014-1.047], p < 0.001). Those with adverse outcomes had reduced subcutaneous fat (p < 0.05). The area under the curve (AUC) of the integrated model using clinical and body composition parameters was the highest (AUC = 0.843, p < 0.001). Conclusion:Decreased ESV and increased VAV at T12 level are independent predictors of severe viral pneumonia, while reduced SAV correlates well with adverse 30-day outcomes. CT-derived body composition parameters can predict the progression and short-term prognosis of viral pneumonia, thereby aiding clinical decision-making.

Diffuse cystic lung diseases (DCLDs) comprise a broad spectrum of disorders characterized by diffusely distributed pulmonary cysts, posing significant diagnostic challenges due to overlapping imaging features. High-resolution CT serves as the cornerstone for the diagnosis of DCLDs, yet its reliability is limited by technical constraints, including insufficient spatial resolution for subtle cysts and variable correlation with functional impairment. Furthermore, persistent discrepancies between imaging findings and underlying pathology highlight the inherent limitations of current modalities. This review synthesizes current diagnostic workflows, emphasizing the integration of imaging with clinical, genetic, and histopathological data within a multidisciplinary framework and appraising the impact of emerging technologies on diagnostic precision. Throughout this evolution, radiologists retain a central role in timely recognition, collaborative decision-making, and outcome optimization for patients with DCLDs.

The accurate identification of children with refractory Mycoplasma pneumoniae pneumonia (RMPP) remains challenging. This study aimed to develop a transformer-based model utilizing clinically indicated chest computed tomography (CT) to stratify pediatric RMPP risk at a critical decision point. Non-contrast chest CT data from a multicenter retrospective cohort of 1224 pediatric patients with Mycoplasma pneumoniae pneumonia who underwent clinically indicated CT were used to develop a transformer-based deep learning framework (trans-DLF). The primary cohort comprised training (n = 506), validation (n = 140), and internal testing (n = 139) cohorts, with two independent external cohorts (n = 331 and n = 108) used to evaluate generalizability. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared against a three-dimensional convolutional neural network (3D-CNN), a clinical model, and a multimodal nomogram. Interpretability was examined using gradient-weighted class activation mapping (Grad-CAM). The median age was 6.83 years (interquartile range, 5.0–8.6 years), and 609 (49.8

Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore whether deep learning reconstruction (DLR) could keep the image quality and reduce the radiation dose compared with hybrid iterative reconstruction (HIR) in prone position scanning for patients of early-stage ILD. This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed. The effective dose of LDCT was 1.22 ± 0.09 mSv, 63.7

Background: Multiple spectral images can be extrapolated from Spectral Detector CT (SDCT), ED, and OED images. ED and OED images are highly sensitive to moisture-rich tissues. Moreover, they have the potential to detect pulmonary artery thrombi in non-enhanced chest CT images. Objective: The objective of this study was to assess the sensitivity, specificity, and accuracy of ED and OED images obtained using SDCT for the detection of pulmonary embolism on non-enhanced images. Aims: This study aimed to evaluate the utility of unenhanced spectral imaging, Electron Density (ED), and Overlay Electron Density (OED) images for assessing pulmonary embolisms in patients with suspected or confirmed Acute Pulmonary Embolism (APE). Methods: Seventy-nine patients who underwent unenhanced and Computed Tomography Pulmonary Angiography (CTPA) using dual-layer spectral detector CT to evaluate APE between November, 2021 and April, 2022 were enrolled in this retrospective study. Based on unenhanced spectral and CTPA images, two radiologists identified areas of high density in the main, lobar, and segmental pulmonary arteries on ED and OED images and detected Pulmonary Embolism (PE) on enhanced images using a consultative approach. CTPA results were considered the gold standard. The diagnostic performance of ED and OED in detecting PE was analyzed. Results: PE was detected in 40 patients (40/79), and 17, 69, and 20 PEs were detected in the main, lobar, and segmental arteries, respectively. The PE detection sensitivity on ED images was 69.7–94.7%, and the specificity was 58.5–98.2% for the individual, main, lobe, and segmental pulmonary arteries. The sensitivity and specificity for OED images were 94.1–95.2% and 80.0–98.1%, respectively. The positive predictive value (PPV) and negative predictive value (NPV) were 53.6–87.7% and 69.7–95.9% for ED images and 48.5–88.9% and 94.1–98.9% for OED images, respectively. The accuracy was 76.0–98.9% and 87.3–96.2% when using ED and OED images, respectively. The research identified that whether it was main, lobar, or segmental pulmonary arteries with blood clots, EDW values ranged from 108.1–108.8%EDW, which were 3.9–4.2%EDW higher than those of arteries without emboli. Pulmonary arteries with emboli standardised ED values were 103.6-104.3%EDW. Conclusion: ED and OED images using spectral CT without contrast media demonstrated high diagnostic performance and could improve the visualization of PE.

This study aimed to evaluate the long-term systematic effectiveness and reliability of the Objective Structured Clinical Examination (OSCE) in radiology resident training, from the perspectives of both examiners and examinees. This retrospective observational study analyzed subjective evaluations and objective examination data collected over 6 years (2018–2021, 2023, and 2024). Subjective evaluations were gathered via questionnaires from 198 examiners and 818 examinees to assess the difficulty and satisfaction with the OSCE. Objective data, including examination scores, difficulty indices, and discrimination indices, for each OSCE station were analyzed using correlation analysis and t-tests. The OSCE demonstrated stable performance over 6 years, with consistent difficulty levels and discrimination ability across all stations. The average scores for individual stations varied; however, the overall final scores remained stable. Strong correlations between the station and final scores indicate good discrimination. Examinees rated the overall difficulty higher than examiners, but the objective indices aligned with examiner assessments. Over 6 years (198 examiners, 818 examinees), OSCE scores stabilized (85.48–88.48), with improved consistency (station range narrowed to 85.51–93.9 by 2024). Difficulty (0.12–0.15) and discrimination indices remained stable (most p < 0.05). Examinees rated it harder than examiners (p < 0.001). The OSCE is a reliable, valid, and effective assessment tool in radiology. Evaluating the OSCE from both subjective and objective perspectives ensured the robustness and validity of the examination. This 6-year study evaluates the Objective Structured Clinical Examination (OSCE) in radiology training through multidimensional analysis of examination metrics (difficulty indices and discrimination coefficients) and stakeholder feedback (n = 198 examiners, 818 examinees), demonstrating its consistency for clinical competency assessment.

Flexible bronchoscopy (FB) is recommended for pediatric Mycoplasma pneumoniae pneumonia (MPP) with persistent consolidation or atelectasis, though substantial heterogeneity in treatment effects exists. This study aimed to develop a causal forest-based predictive model to identify pediatric MPP patients most likely to benefit from FB. This retrospective two-center study enrolled pediatric MPP patients in derivation (n = 753) and validation (n = 139) cohorts. Clinical, laboratory, and AI-quantified computed tomography (CT) data were analyzed. Individual treatment effects (ITEs) were estimated using causal forest algorithms. FB-beneficial subgroups were defined using receiver operating characteristic (ROC) analysis of ITEs, with the varying treatment effect across the subgroups validated via multivariable linear regression. Subgroup characteristics, feature importance, and heatmap-based feature interactions were also analyzed. FB treatment significantly reduced total fever duration in identified FB-beneficial subgroups in both derivation (β = − 1.16, p < 0.001) and validation (β = − 0.68, p = 0.04) cohorts. These beneficial subgroups exhibited significantly higher consolidation/atelectasis volume (CAV), pneumonia attenuation (PA), and consolidation-to-pneumonia ratio (CAR) compared to non-beneficial groups (all p < 0.001). Heatmap analyses confirmed that increased CAV combined with elevated PA or lymphocyte counts could improve FB efficacy. This study developed and validated an individualized prediction model to identify pediatric MPP patients most likely to benefit from FB treatment. Our model may serve as a tool to support clinicians in optimizing FB utilization, potentially reducing unnecessary interventions and associated risks. An accessible online tool of this model facilitates practical clinical implementation.

To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT). Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning–based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images. Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0

BACKGROUND:Spread through air spaces (STAS) is a distinct, aggressive pattern of primary lung adenocarcinoma (LUAD) that affects both prognosis and treatment strategies for patients. This study aimed to quantify intratumoural heterogeneity (ITH) and integrate the quantitative metrics with intratumoural-peritumoural habitat features and clinical-radiologic characteristics to preoperatively predict the STAS status of primary LUAD and further explore the potential biological basis underlying the prediction model. METHODS:Conventional radiomics features and habitat features were extracted from intratumoural and peritumoural regions on preoperative computerized tomography (CT) images. A new index, the ITH score, was developed to quantify ITH levels. Univariable and multivariable logistic regression analyses were conducted to identify clinical-radiologic characteristics associated with STAS. Various machine learning algorithms were used to build the prediction models. Additionally, intratumoural-peritumoural habitat features, ITH score, and clinical-radiologic characteristics were integrated into a combined model. Finally, 24 patients with RNA sequencing data were utilised for gene expression analysis. RESULTS:A total of 1268 patients (median age, 60 years; IQR, 53.8-66.0 years; 850 female) were divided into the training set (n = 943), validation set (n = 236), and external test set (n = 89). Using the Light Gradient Boosting Machine classifier, the combined model demonstrated the highest predictive performance for STAS, achieving an AUC value of 0.97 in the training, 0.98 in the validation, and 0.91 in the external test set. Differentially expressed genes in a high combined model probability group were associated with monocarboxylic acid transport and metabolism. CONCLUSIONS:The combined model demonstrated superior performance in predicting STAS in patients with primary LUAD.

Wheezing, a prevalent respiratory symptom in children, poses diagnostic and management complexities due to its clinical and etiological diversity and the challenge of capturing objective data on pediatric airway inflammation and function. Despite recent advances in understanding pediatric wheezing illnesses, challenges remain in early etiologic diagnosis, phenotype classification, and management. These are mainly attributed to the absence of standardized diagnostic methodologies and effective personalized treatment protocols. The evolution of artificial intelligence (AI) technology introduces new opportunities for managing pediatric wheezing illnesses. The utilization of AI in healthcare has shown considerable promise in disease identification, treatment suggestions, and personalized medicine. Although the application of AI in pediatric wheezing is relatively minimal currently, its utilization in diseases related to pediatric wheezing, specifically in the diagnosis and management of pneumonia and asthma, has seen numerous successful instances. Consequently, this review primarily summarized the application of AI in pediatric wheezing illnesses, and explored the use of AI in pediatric pneumonia and asthma, with an aim to culminate valuable experience from these domains, thereby enriching the application of AI in pediatric wheezing illnesses.

Objective: This study aimed to compare automated three-dimensional Intrapulmonary Vessel Volume (IPVV) differences between lung and mediastinal windows in healthy individuals using quantitative measurements obtained from chest Computed Tomography (CT) plain scans. Methods: A total of 258 participants (aged 21–83 years) with negative chest CT scans from routine physical examinations conducted between January to November 2023 were retrospectively enrolled. For each healthy participant, an algorithm was used to automatically extract total lung IPVVs as well as IPVVs for vessels of specific diameter. Differences in IPVVs were then compared between those extracted using the lung window and those extracted using the mediastinal window. Results: The IPVVs for the entire lung, intrapulmonary arteries, intrapulmonary veins, and small pulmonary vessels (categorized by different diameters) extracted from the lung window were significantly higher than those extracted from the mediastinal window (p<0.01). No significant sex-based differences in IPVV were observed for pulmonary arteries and veins with diameters between 0.8 and 1.6 mm, as well as pulmonary veins with diameters between 2.4 and 3.2 mm. However, in pulmonary arteries and veins with diameters between 1.6 and 2.4 mm, females had significantly higher IPVVs than males. In all other cases, IPVVs were larger in males than in females. Conclusion: This method of automatic IPVV extraction and quantitative assessment has been proven to be feasible. Automated IPVV expression effectively identified morphological characteristics of intrapulmonary vessels. The study has concluded IPVVs extracted from the lung window to be generally larger than those extracted from the mediastinal window.

Background: Community-acquired pneumonia (CAP) is a leading cause of pediatric hospitalizations and a risk factor for chronic respiratory conditions. Glucocorticoids (GCs) are used as adjunctive therapy to reduce inflammation, but their efficacy in infants and toddlers remains unclear. Method: A retrospective study of 1116 infants and toddlers with severe CAP was conducted, using causal forest to estimate individual treatment effects (ITEs), with the duration of intensive care unit (ICU) stay as the outcome. Patients were stratified based on ITEs to investigate the heterogeneous treatment effect (HTE) and identify responders. Generalized linear models validated the HTE across subclasses, followed by comparative analyses to characterize responders. Variable importance was assessed using the causal model, and Shapley additive explanations (SHAP) quantified each variable’s contribution to the ITE. Analysis was also performed in mechanically ventilated patients (MV group). Results: GCs demonstrated significant HTE. Older patients and those with elevated inflammation markers showed better responses, whereas no such benefit was observed in respiratory syncytial virus-infected patients. These subgroups experienced shorter ICU stays both in the whole cohort (β = −0.16, p < 0.001) and MV group (β = −0.34, p < 0.001), and shorter ventilation duration was observed in the MV group (β = −0.35, p < 0.001). Age and the anion gap were key predictors of ITEs. SHAP analysis revealed a positive correlation between age and GC effectiveness. Conclusions: Significant heterogeneity in GC treatment effects exists among infants and toddlers with severe CAP, highlighting the need for optimization of GC use in this population.

3045 Background: There is a growing need for risk evaluation and treatment monitoring in cancer care. However, current methods, mainly imaging, can be burdensome for patients over time and prone to variability among readers. Recent research has highlighted the potential of tumor-informed circulating tumor DNA (ctDNA) testing for identifying postoperative minimal residual disease (MRD) due to its high sensitivity by tracking individualized mutations. Nevertheless, its use in early-stage patients prior to surgery is constrained by limited tissue availability and extended turnaround times. MUSETALK-Lung01 (multiomics sequencing technique application kick-start) is a prospective, longitudinal, observational study designed to evaluate the clinical utility of a tumor-naïve ctDNA assay in patients with early-stage non-small cell lung cancer (NSCLC). Methods: Pretreatment plasma samples were prospectively collected from participants with stage I-IIIA NSCLC. Cell-free DNA was extracted and analyzed using a blood assay that interrogates both epigenetic and genetic information. The detection status and the estimated fraction of ctDNA were reported by a machine learning classifier and an independent statistical model, respectively. The calling threshold corresponding to a 99% clinical specificity was verified in a subgroup from the THUNDER study (NCT04820868). Longitudinal data, including vital status, cancer status, and treatment, were collected for up to 5 years. The study was approved by the institutional review board and all participants were required to provide informed consent. Results: A total of 289 participants from the MUSETALK-Lung01 study were analyzed. Of these, 49% (141/289) reached the 5-year follow-up, with a median follow-up duration of 59 months. To assess the prognostic value of preoperative ctDNA levels, relapse-free survival (RFS) and overall survival (OS) were evaluated across different stages and pathological subtypes separately. In stage I LUAD patients (N = 179), ctDNA-positive patients (N = 20) had significantly inferior RFS compared to ctDNA-negative patients (2-year RFS: 70% [95% CI: 46%–88%] vs. 94% [95% CI: 90%–97%]; log-rank p < 0.001). In contrast, no association was found between preoperative ctDNA detection and RFS in stage II-IIIA LUAD or non-LUAD NSCLC, irrespective of the clinical stage. Specifically, among the 179 stage I LUAD patients, 11 relapsed within 2 years, and 6 of these had positive ctDNA test results. This rate was significantly higher than in patients who relapsed between 2 and 5 years (1/12) or never relapsed (13/156; χ 2 test, p < 0.001). Conclusions: These findings indicate that presurgical ctDNA can serve as a prognostic indicator in early-stage NSCLC. Tumor-naive ctDNA testing may enhance the standard workflow by identifying high-risk patients who could benefit from innovative treatments. Clinical trial information: NCT04820868 .

Abstracts The impact of early-stage tumors on gene expression in adjacent tissues remains uncertain, despite the known influence of the tumor microenvironment on tumor progression. Here, we systematically analyze early-stage lung adenocarcinoma (LUAD) and surrounding tissues across multiple distinct regions, from the tumor core to distant tissues. DNA methylation profiling in a 12-patient cohort reveals two distinct patterns of methylation changes. Steep changes occurring at the tumor boundary and shallow changes showing a gradual shift over increasing distance to the tumor. Approximately 17,000 CpG sites demonstrate shallow changing trends without clear boundaries, potentially affecting 2655 genes. In half of the patients, tissues within 10 mm beyond the tumor show methylation patterns similar to tumors. We test mRNA expression of key genes affected by these methylation patterns and observe that the protein expression pattern of WNT7B demonstrates no steep changes at the tumor boundary, supporting their regulatory role. Adding a 59-patient four-year-prognosis cohort allowed us to rigorously assess the clinical relevance of these methylation change trends. These shallow changes reflect tumor characteristics and have the potential for prognostic prediction in patients, warranting further investigation.

Background:Mycoplasma pneumoniae pneumonia (MPP) is a major cause of community-acquired pneumonia (CAP) in children, with some cases progressing to refractory MPP (RMPP). RMPP is associated with a hypercoagulable state and pulmonary embolism. This study aimed to investigate pulmonary microvascular changes in RMPP and evaluate the predictive value of pulmonary blood volume (PBV) parameters. Methods:A retrospective study using UV-Net-based pulmonary vascular analysis included 512 pediatric MPP patients in a cross-validation cohort and 124 pediatric MPP patients in an external testing cohort. Pulmonary blood vessels were segmented and classified by cross-sectional area into three blood-volume fractions: BV5%, representing the percentage of PBV contained in vessels with a cross-sectional area less than 5 mm2; BV5-10%, representing the percentage of PBV contained in vessels with a cross-sectional area between 5 and 10 mm2; and BV10%, representing the percentage of PBV contained in vessels with a cross-sectional area greater than 10 mm2. Logistic regression and extreme gradient boosting were used to analyze associations and predict RMPP. Model performance was assessed via receiver operating characteristic (ROC) curve analysis. Results:Patients with RMPP, compared to patients with non-RMPP, had a significantly lower BV5% (median: 58.50% vs. 60.63%, P=0.007) and higher BV10% (median: 20.90% vs. 19.39%, P=0.004). Multivariate analysis revealed BV5% as a protective predictor [odds ratio (OR) =0.70, P=0.005] and BV10% as a risk factor for RMPP (OR =1.49, P=0.002). Compared with the clinical-only model, the model incorporating these computed tomography (CT)-derived parameters significantly improved performance in the cross-validation cohort, demonstrating superiority in terms of area under the ROC curve (AUC) and other metrics (combined model: AUC =0.91, 95% CI: 0.89-0.94; clinical-only model: AUC =0.88, 95% CI: 0.86-0.91; P<0.001). In the external testing cohort, the combined model consistently outperformed the clinical-only model in accuracy, precision, specificity, and F1 score. Conclusions:Quantitative analysis revealed microvascular alterations in patients with RMPP. Integrating CT-derived biomarkers can enhance RMPP prediction and facilitate early intervention.