OBJECTIVES:Artificial intelligence (AI) tools enable automated assessment of vertebral bone mineral density (BMD), coronary artery calcification (CAC), and aortic calcification on CT scans, offering multimorbidity analysis in chronic obstructive pulmonary disease (COPD) patients. We aimed to investigate the bone-vascular axis with a focus on COPD severity and inhaled corticosteroid (ICS) use. METHODS:Low-dose chest CTs of 540 patients from the COSYCONET study were analyzed using AI-based tools. Total thoracic calcification (TTC) was defined as the sum of CAC and aortic calcification volumes. The BMD was measured for T12 in Hounsfield units. Group comparisons and correlation analyses were stratified by COPD severity (GOLD 0-2 vs. GOLD 3-4) and ICS treatment. The modification effect was assessed with multivariable and interaction models. RESULTS:Higher CAC and TTC were significantly associated with increased age, smoking, BMI, and impaired physical function, while gait speed, female sex, and BMD were inversely associated. With no significant difference between GOLD stages, BMD showed a consistent negative correlation with both CAC (ρ = -0.247 and -0.243) and TTC (ρ = -0.286 and -0.304). Interaction analyses revealed no significant modification effect for COPD severity; a marginally significant trend toward a stronger association among ICS users for CAC (β = -0.175, p = 0.042), but no significant effect for TTC. CONCLUSION:The decreased vertebral BMD was associated with increased vascular calcification, independent of COPD severity and marginally dependent on ICS therapy. These findings support the concept of "bone-vascular axis", likely driven by shared degenerative mechanisms.

ObjectivesIncidental pulmonary nodules are common on computed tomography (CT), and management typically relies on size and volume. Artificial intelligence (AI)-based tools show promise in nodule detection and risk assessment, but their clinical utility remains uncertain. This study aimed to evaluate the accuracy of AI-based software in detecting and predicting the malignancy of incidental pulmonary nodules.Materials and methodsThis retrospective study included patients selected from a cohort of 1,138 individuals who underwent chest CT between 2015 and 2024 and met the inclusion criteria. Patients were classified into benign and malignant groups. Malignancy was determined by histopathology or by at least 2 years of follow-up. Nodule location, size, and type were assessed using by both using radiology reports and AI tools. Three commercial tools (AI-I, AI-II, and AI-III) were assessed for nodule detection and malignancy risk prediction. Agreement was assessed using Cohen’s kappa and the intraclass correlation coefficient (ICC), and diagnostic performance was evaluated using receiver operating characteristic analysis.ResultsChest CT scans of 374 patients (mean age 66 ± 9 yr.; range 37–88 yr.; 231 males) with at least one solid or part-solid nodule were evaluated. AI-I and AI-II demonstrated excellent agreement with radiology reports for nodule localization (κ = 0.95, p < 0.001) and moderate agreement for nodule type (κ = 0.46, p < 0.001). Size assessment showed excellent agreement with ICC values of 0.93 [95%CI = 0.92–0.94] for AI-I and 0.89 [95%CI = 0.86–0.91] for AI-II. AI-II differed from AI-III in malignancy prediction with AUC = 0.77 [95%CI = 0.72–0.81] and 0.89 [95%CI = 0.85–0.92], respectively. Additionally, AI-II showed significantly lower PPV (65.57% vs. 87.20%, p < 0.001) and accuracy (72.1% vs. 82%, p < 0.001) than AI-III.ConclusionAI-based tools demonstrated high accuracy for incidental pulmonary detection; however, their performance in malignancy risk stratification differed substantially.

Background:The e-Lung weighted reticulovascular score (WRVS) is an automated computed tomography biomarker that quantifies interstitial lung disease (ILD) severity and is associated with prognosis in patients with idiopathic pulmonary fibrosis (IPF). The aims of the present study were to evaluate WRVS as a prognostic factor in patients with non-IPF ILD. Methods:The test cohort comprised patients from the Open Source Imaging Consortium and the validation cohort, patients recruited to the prospective German CoWorker ILD registry. Associations between baseline and serial WRVS with future forced vital capacity (FVC) decline and survival were tested. Results:Median survival was 7.1 and 6.1 years in the test (n=302) and validation (n=378) cohorts, respectively. Baseline WRVS was associated with mortality in test (hazard ratio (HR) 1.11, 95% CI 1.08-1.14; p<0.001, C-index 0.75) and validation (HR 1.12, 95% CI 1.09-1.15; p<0.001, C-index 0.72) cohorts. A threshold WRVS of ≥15% was associated with mortality in both cohorts (HR 4.77, 95% CI 3.11-7.31; p<0.001, C-index 0.71, and HR 3.49, 95% CI 2.48-4.91; p<0.001, C-index 0.63 for test and validation cohorts, respectively). After adjustment for FVC, age and sex, baseline WRVS was associated with future FVC decline or death in test (OR 1.13, 95% CI 1.06-1.21; p<0.001, C-index 0.72) and validation (OR 1.18, 95% CI 1.11-1.25; p<0.001, C-index 0.72) cohorts. A rise in WRVS of 3% on serial computed tomography was associated with mortality in both test (HR 5.69, 95% CI 2.77-11.70; p<0.001, C-index 0.75) and validation cohorts (HR 1.99, 95% CI 1.09-3.65; p=0.026, C-index 0.57). Conclusion:In patients with non-IPF ILD, the e-Lung WRVS biomarker is associated with mortality and FVC decline when applied to baseline high-resolution computed tomography scans replicating previous studies in IPF. Patients with an increase in WRVS of 3% on serial computed tomography scans have significantly increased risk of mortality.

The e-Lung weighted reticulovascular score (WRVS) is an automated CT biomarker that quantifies interstitial lung disease (ILD) severity and is associated with prognosis in patients with idiopathic pulmonary fibrosis (IPF). To evaluate WRVS as a prognostic factor in patients with non-IPF fibrotic ILD (fILD). The test cohort comprised patients from the Open Source Imaging Consortium and the validation cohort, patients recruited to the prospective German CoWorker ILD registry. Associations between baseline and serial WRVS with future forced vital capacity (FVC) decline and survival were tested. Median survival was 7.1 and 6.1 years in the test (n=302) and validation (n=378) cohorts respectively. Baseline WRVS was associated with mortality in test (HR 1.11, [1.08–1.14], (p<0.001), C-index 0.75) and validation (HR 1.12, [1.09–1.15], (p<0.001), C-index 0.72) cohorts. A threshold WRVS of ≥15% was associated with mortality in both cohorts (HR 4.77, (3.11–7.31), p<0.001, C-index 0.71) and (HR 3.49, [2.48–4.91], p<0.001, C-index 0.63). After adjustment for FVC, age and sex, baseline WRVS was associated with future FVC decline or death (OR 1.13, [1.06–1.21], p<0.001, C-index 0.72) and (OR 1.18, [1.11–1.25], p<0.001, C-Index 0.72) in test and validation cohorts respectively. A rise in WRVS of 3% on serial CT was associated with mortality in both test (HR 5.69, [2.77–11.70], p<0.001, C-index 0.75) and validation cohorts (HR 1.99, [1.09–3.65], p=0.026), C-index 0.57). In patients with non-IPF fILD, the e-Lung WRVS biomarker is associated with mortality and FVC decline when applied to baseline HRCT scans replicating previous studies in IPF. Patients with an increase in WRVS of 3% on serial CT scans have significantly increased risk of mortality.

This multicenter trial was conducted to evaluate MRI for the longitudinal management of incidental pulmonary nodules in heavy smokers. 239 participants (63.9 ± 8.4 years, 43–82 years) at risk of or with COPD GOLDI-IV from 16 centers prospectively underwent two rounds of same-day low-dose computed tomography (LDCT1 2) and MRI1 2 at an interval of three years in the nationwide COSYCONET trial. All exams were independently assessed for incidental pulmonary nodules in a standardized fashion by two blinded readers, incl. axis measurements and Lung-RADS categorization, with consensual LDCT results serving as the standard of reference. A change in diameter ≥ 2 mm was rated as progress. 11 patients underwent surgery for suspicious nodules after the first round. Two hundred twenty-four of two hundred forty nodules (93.3

Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height prediction models, aiding in diagnosing spinal conditions like compression fractures and supporting individualized, sex-specific medicine. In this study an AI-based CT-imaging spine analysis of 262 subjects (mean age 32.36 years, range 20-54 years) was conducted, including a total of 3117 vertebrae, to assess sex-associated anatomical variations. Automated segmentations provided anterior, central, and posterior vertebral heights. Regression analysis with a cubic spline linear mixed-effects model was adapted to age, sex, and spinal segments. Measurement reliability was confirmed by two readers with an intraclass correlation coefficient (ICC) of 0.94-0.98. Female vertebral heights were consistently smaller than males (p < 0.05). The largest differences were found in the upper thoracic spine (T1-T6), with mean differences of 7.9-9.0%. Specifically, T1 and T2 showed differences of 8.6% and 9.0%, respectively. The strongest height increase between consecutive vertebrae was observed from T9 to L1 (mean slope of 1.46; 6.63% for females and 1.53; 6.48% for males). This study highlights significant sex-based differences in vertebral heights, resulting in sex-adapted nomograms that can enhance diagnostic accuracy and support individualized patient assessments.

Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-network (LCP-CNN), a deep learning-based approach, in comparison to multiparametric statistical methods (Brock model and Lung-RADS®) for risk classification of nodules in cohorts with different risk profiles and underlying pulmonary diseases. Retrospective analysis was conducted on non-contrast and contrast-enhanced CT scans containing pulmonary nodules measuring 5–30 mm. Ground truth was defined by histology or follow-up stability. The final analysis was performed on 297 patients with 422 eligible nodules, of which 105 nodules were malignant. Classification performance of the LCP-CNN, Brock model, and Lung-RADS® was evaluated in terms of diagnostic accuracy measurements including ROC-analysis for different subcohorts (total, screening, emphysema, and interstitial lung disease). LCP-CNN demonstrated superior performance compared to the Brock model in total and screening cohorts (AUC 0.92 (95

Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net. For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used. The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41–0.85/0.40–0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70–0.94/0.67–0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48–0.69/0.47–0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen–Dice coefficients were between 0.61–0.74/0.52–0.77/0.84 (LR-5: 0.74/0.77/0.84). Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.

Objectives A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema. Methods A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen’s kappa (im-κ and ir-κ). Results The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04–0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49–0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1–0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18–0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT. Conclusion Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features. Clinical relevance statement MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered. Key Points • In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

Abstract Objective Investigate the feasibility of detecting early treatment-induced tumor tissue changes in patients with advanced lung adenocarcinoma using diffusion-weighted MRI-derived radiomics features. Methods This prospective observational study included 144 patients receiving either tyrosine kinase inhibitors (TKI, n = 64) or platinum-based chemotherapy (PBC, n = 80) for the treatment of pulmonary adenocarcinoma. Patients underwent diffusion-weighted MRI the day prior to therapy (baseline, all patients), as well as either + 1 (PBC) or + 7 and + 14 (TKI) days after treatment initiation. One hundred ninety-seven radiomics features were extracted from manually delineated tumor volumes. Feature changes over time were analyzed for correlation with treatment response (TR) according to CT-derived RECIST after 2 months and progression-free survival (PFS). Results Out of 14 selected delta-radiomics features, 6 showed significant correlations with PFS or TR. Most significant correlations were found after 14 days. Features quantifying ROI heterogeneity, such as short-run emphasis (p = 0.04(pfs)/0.005(tr)), gradient short-run emphasis (p = 0.06(pfs)/0.01(tr)), and zone percentage (p = 0.02(pfs)/0.01(tr)) increased in patients with overall better TR whereas patients with worse overall response showed an increase in features quantifying ROI homogeneity, such as normalized inverse difference (p = 0.01(pfs)/0.04(tr)). Clustering of these features allows stratification of patients into groups of longer and shorter survival. Conclusion Two weeks after initiation of treatment, diffusion MRI of lung adenocarcinoma reveals quantifiable tissue-level insights that correlate well with future treatment (non-)response. Diffusion MRI-derived radiomics thus shows promise as an early, radiation-free decision-support to predict efficacy and potentially alter the treatment course early. Critical relevance statement Delta-Radiomics texture features derived from diffusion-weighted MRI of lung adenocarcinoma, acquired as early as 2 weeks after initiation of treatment, are significantly correlated with RECIST TR and PFS as obtained through later morphological imaging. Key Points Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management. Graphical Abstract

To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations. Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56–69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods. The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases. Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis. Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions.

Abstract Background Patients with COPD are often affected by loss of bone mineral density (BMD) and osteoporotic fractures. Natriuretic peptides (NP) are known as cardiac markers, but have also been linked to fragility-associated fractures in the elderly. As their functions include regulation of fluid and mineral balance, they also might affect bone metabolism, particularly in systemic disorders such as COPD. Research question We investigated the association between NP serum levels, vertebral fractures and BMD assessed by chest computed tomography (CT) in patients with COPD. Methods Participants of the COSYCONET cohort with CT scans were included. Mean vertebral bone density on CT (BMD-CT) as a risk factor for osteoporosis was assessed at the level of TH12 (AI-Rad Companion), and vertebral compression fractures were visually quantified by two readers. Their relationship with N-terminal pro-B-type natriuretic peptide (NT-proBNP), Mid-regional pro-atrial natriuretic peptide (MRproANP) and Midregional pro-adrenomedullin (MRproADM) was determined using group comparisons and multivariable analyses. Results Among 418 participants (58% male, median age 64 years, FEV1 59.6% predicted), vertebral fractures in TH12 were found in 76 patients (18.1%). Compared to patients without fractures, these had elevated serum levels (p ≤ 0.005) of MRproANP and MRproADM. Using optimal cut-off values in multiple logistic regression analyses, MRproANP levels ≥ 65 nmol/l (OR 2.34; p = 0.011) and age (p = 0.009) were the only significant predictors of fractures after adjustment for sex, BMI, smoking status, FEV1% predicted, SGRQ Activity score, daily physical activity, oral corticosteroids, the diagnosis of cardiac disease, and renal impairment. Correspondingly, MRproANP (p < 0.001), age (p = 0.055), SGRQ Activity score (p = 0.061) and active smoking (p = 0.025) were associated with TH12 vertebral density. Interpretation MRproANP was a marker for osteoporotic vertebral fractures in our COPD patients from the COSYCONET cohort. Its association with reduced vertebral BMD on CT and its known modulating effects on fluid and ion balance are suggestive of direct effects on bone mineralization. Trial registration ClinicalTrials.gov NCT01245933, Date of registration: 18 November 2010.

BackgroundChronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD.MethodsNon-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1–4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated.ResultsInitial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41–0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland–Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements.ConclusionOur study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct – yet complementary – strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Radiomics focuses on extracting and analyzing quantitative features from medical images. Standardizing radiomics is difficult due to variations across studies and centers, making it challenging to identify optimal techniques for any application. Recent works (WORC, Autoradiomics) [1, 2] are introducing radiomics-based frameworks for automated pipeline optimization. Both approaches span the workflow, enabling consistent, and reproducible radiomics analyses. In contrast, finding the ideal solutions for feature extractor and feature selection components, has received less attention. Therefore, we propose the Radiomics Processing Toolkit (RPTK) [3], which adds comprehensive feature extraction and selection components from PyRadiomics and from the Medical Image Radiomics Processor (MIRP) to the radiomics pipeline.We compared RPTK with results fromWORC and Autoradiomics and on six different public benchmark data sets. We demonstrate significant improved performance by incorporating the proposed feature processing and selection techniques across all datasets. Additionally, the choice of the feature extractor significantly enhances prediction performance. Our results provide additional guidance in selecting suitable components for optimized radiomics analyses.

The Strengthening the Screening of Lung Cancer in Europe (SOLACE) initiative, supported by Europe's Beating Cancer Plan, is dedicated to advancing lung cancer screening. This initiative brings together the most extensive pan-European network of respiratory and radiology experts, involving 37 partners from 15 countries. SOLACE aims to enhance equitable access to lung cancer screening by developing targeted recruitment strategies for underrepresented and high-risk populations. Through comprehensive work packages, SOLACE integrates scientific research, pilot studies, and sustainability efforts to bolster regional and national screening efforts across EU member states.Critical relevance statementThe SOLACE project aims to facilitate the optimization and implementation of equitable lung cancer screening programs across the heterogeneous healthcare landscape in EU member states.Key PointsThe effectiveness of lung cancer screening is supported by both scientific evidence and now increasing legislative support.SOLACE aims to develop, test, and disseminate tools to facilitate the realization of lung cancer screening at both a national and regional level.Previously underrepresented populations in lung cancer screening will be targeted by tailored recruitment strategies.SOLACE forms the first pan-European network of experts poised to drive real-world implementation of lung cancer screening.

A rise of radiomics studies and techniques could be observed over the past few years, which centers around the extraction and analysis of quantitative features from medical images. Radiomics offers numerous advantages in disease characterization and treatment response prediction. Despite its promise, radiomics faces challenges in standardizing features and techniques, leading to large variations of approaches across studies and centers, making it difficult to determine the most suitable techniques for any given clinical scenario. Additionally, manually constructing optimized radiomics pipelines can be time-consuming. Recent works (WORC, Autoradiomics) have addressed the aforementioned shortcomings by introducing radiomics-based frameworks for automated pipeline optimization. Both approaches comprehensively span the entire radiomics workflow, enabling consistent, comprehensive, and reproducible radiomics analyses. In contrast, finding the ideal solutions for the workflow’s feature extractor and feature selection components, has received less attention. To address this, we propose the Radiomics Processing Toolkit (RPTK), which adds comprehensive feature extraction and selection components from PyRadiomics and from the Medical Image Radiomics Processor (MIRP) to the radiomics automation pipeline. To validate our approach and demonstrate benefits from the feature-centered components, we comprehensively compared RPTK with results from WORC and Autoradiomics on six public benchmark data sets. We show that we can achieve higher performance by incorporating the proposed feature processing and selection techniques. Our results provide additional guidance in selecting suitable components for optimized radiomics analyses in clinical use cases such as treatment response prediction.

Purpose Radiology departments with the large diagnostic devices CT and MRI contribute significantly to the overall energy consumption of health facilities. However, there is a lack of systematic knowledge about the opinions of radiological staff on the most relevant aspects of sustainability. For this reason, we conducted a comprehensive survey for radiology employees on sentiment and experiences regarding sustainability in radiology. Materials and Methods In collaboration with the Sustainability Network of the German Roentgen Society (DRG), we developed a questionnaire on various dimensions of sustainability in radiology. We conducted a nationwide online survey of radiology employees between July 1 st , 2023 and November 30 th , 2023. The absolute and percentage distributions were then determined. Results From 109 participants, mainly doctors (67/109; 62%) from university hospitals (48/109; 44.0%), 81 out of 109 rated sustainability in professional environment (74.3%) as important or very important. However, only 38 out of 109 (38%) of the respondents were able to name specific sustainable procedures in their institute. The most important topics for a sustainable radiology were waste management (26/109, 22.6%), energy reduction (19/109, 16.5%), conscious behaviour (15/109, 13%) and reduction of obsolete examinations (14/109, 12.2%). In addition, a lack of qualifications (16%), finances (21%) and compliance (21%) were named as challenges for the implementation of sustainable actions in radiology. The perceived importance of specific, sustainable measures in radiology is generally higher than the amount of already established actions. Conclusion Radiology has significant, yet untapped, potential for sustainable optimization. There is a need for qualified and sensitized health care workers in radiology who are committed to sustainability in everyday clinical practice. Among other things, in this study the respondents demand a more critical indication for diagnostic workup, including avoiding redundant examinations, and a technological progress towards energy-efficient devices, which requires a dynamic exchange between radiology, industry and health care facilities.