Nationwide Survey - What is important for a sustainable radiology? / Bundesweite Umfrage ­ Was ist wichtig für eine nachhaltigere Radiologie?

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.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 1st, 2023 and November 30th, 2023. The absolute and percentage distributions were then determined.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.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. · Of 109 respondents from radiology departments, 74.3% consider sustainability to be important or very important in a professional context.. · Waste management (22.6%), energy reduction (16.5%), conscious behaviour (13%) and reduction of obsolete or redundant examinations (12.2%) are, according to those surveyed, most important for a more sustainable radiology.. · Sustainability initiatives have been institutionally established among 38% of participants. However, key challenges to the implementation of sustainable practices in radiology include insufficient compliance from staff and patients (21%), limited access to funding (21%), and a lack of necessary qualifications (16%).. · The perceived importance of specific measures for sustainability in radiology is generally higher than the previously established measures.. · Technology & energy efficiency (59.6%), energy contracting (46.8%) and waste management (34.9%) are the areas of interest with the highest priority.. · Palm V, Wucherpfennig L, Do TD et al. Nationwide Survey - What is important for a sustainable radiology?. Fortschr Röntgenstr 2024; DOI 10.1055/a-2378-6366.

Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer.

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.

How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.

OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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. CRITICAL RELEVANCE STATEMENT: 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. KEY POINTS: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities.

Midregional Proatrial Natriuretic Peptide (MRproANP) is associated with vertebral fractures and low bone density in patients with chronic obstructive pulmonary disease (COPD).

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.

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.

Background: Chronic 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. Methods: Non-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. Results: Initial 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. Conclusion: Our 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.

Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial.

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.

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.

OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries.

OBJECTIVES: Lung cancer screening (LCS), using low-dose computed tomography (LDCT), can be more efficient by simultaneously screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the Big-3 diseases. This study aimed to determine the willingness to participate in (combinations of) Big-3 screening in four European countries and the relative importance of amendable participation barriers. METHODS: An online cross-sectional survey aimed at (former) smokers aged 50-75 years elicited the willingness of individuals to participate in Big-3 screening and used analytical hierarchy processing (AHP) to determine the importance of participation barriers. RESULTS: Respondents were from France (n = 391), Germany (n = 338), Italy (n = 399), and the Netherlands (n = 342), and consisted of 51.2% men. The willingness to participate in screening was marginally influenced by the diseases screened for (maximum difference of 3.1%, for Big-3 screening (73.4%) vs. lung cancer and COPD screening (70.3%)) and by country (maximum difference of 3.7%, between France (68.5%) and the Netherlands (72.3%)). The largest effect on willingness to participate was personal perceived risk of lung cancer. The most important barriers were the missed cases during screening (weight 0.19) and frequency of screening (weight 0.14), while diseases screened for (weight 0.11) ranked low. CONCLUSIONS: The difference in willingness to participate in LCS showed marginal increase with inclusion of more diseases and limited variation between countries. A marginal increase in participation might result in a marginal additional benefit of Big-3 screening. The amendable participation barriers are similar to previous studies, and the new criterion, diseases screened for, is relatively unimportant. CLINICAL RELEVANCE STATEMENT: Adding diseases to combination screening modestly improves participation, driven by personal perceived risk. These findings guide program design and campaigns for lung cancer and Big-3 screening. Benefits of Big-3 screening lie in long-term health and economic impact, not participation increase. KEY POINTS: • It is unknown whether or how combination screening might affect participation. • The addition of chronic obstructive pulmonary disease and cardiovascular disease to lung cancer screening resulted in a marginal increase in willingness to participate. • The primary determinant influencing individuals' engagement in such programs is their personal perceived risk of the disease.

Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules.

OBJECTIVES: The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). METHODS: CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach). RESULTS: In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. CONCLUSION: CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. CLINICAL RELEVANCE STATEMENT: Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. KEY POINTS: • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.

[Patient-centered, value-based management of incidental findings in radiology]. / Patientenzentriertes, wertbasiertes Management von radiologischen Zufallsbefunden.

As a byproduct of the increased use of high-resolution radiological imaging, the prevalence of incidental findings (IFs) has been increasing for years. The discovery of an incidental finding can allow early treatment of a potentially health-threatening disease and thus decisively change the course of the disease. However, many incidental findings are of low risk with little or no health impact, and yet their discovery often leads to a cascade of additional investigations. It is undisputed that incidental findings can have a direct impact on the life of the person and that not only psychosocial aspects such as worries and anxiety due to false-positive findings play a role, but that insurance, legal or professional problems can also occur under certain circumstances, which is why the correct handling of incidental findings and the accompanying ethical challenges that apply to them regularly give rise to discussions. General principles to consider when managing incidental findings are responsibility for the well-being of the patient/study participant and of society. In order to avoid overdiagnosis and overtreatment and to achieve high patient benefit, radiologists and clinicians must know how to properly deal with IFs. In recent years, various national and international societies have published important guidelines ("white papers") on how to deal with the management of IFs. It is important that radiologists are fully aware of and follow these guidelines and are also available to referring physicians for further discussions and advice. The most important fact is that the well-being of the patient must always be at the center of all decisions.

Sustainability and Climate Protection in Radiology - An Overview. / Nachhaltigkeit und Klimaschutz in der Radiologie ­ Ein Überblick.

BACKGROUND: Sustainability is becoming increasingly important in radiology. Besides climate protection - economic, ecological, and social aspects are integral elements of sustainability. An overview of the scientific background of the sustainability and environmental impact of radiology as well as possibilities for future concepts for more sustainable diagnostic and interventional radiology are presented below.The three elements of sustainability:1. EcologyWith an annually increasing number of tomographic images, Germany is in one of the leading positions worldwide in a per capita comparison. The energy consumption of an MRI system is comparable to 26 four-person households annually. CT and MRI together make a significant contribution to the overall energy consumption of a hospital. In particular, the energy consumption in the idle or inactive state is responsible for a relevant proportion.2. EconomyA critical assessment of the indications for radiological imaging is important not only because of radiation protection, but also in terms of sustainability and "value-based radiology". As part of the "Choosing Wisely" initiative, a total of 600 recommendations for avoiding unnecessary examinations were compiled from various medical societies, including specific indications in radiological diagnostics.3. Social SustainabilityThe alignment of radiology to the needs of patients and referring physicians is a core aspect of the social component of sustainability. Likewise, ensuring employee loyalty by supporting and maintaining motivation, well-being, and job satisfaction is an essential aspect of social sustainability. In addition, sustainable concepts are of relevance in teaching and research, such as the educational curriculum for residents in radiology, RADUCATION or the recommendations of the International Committee of Medical Journal Editors. KEY POINTS: · Sustainability comprises three pillars: economy, ecology and the social component.. · Radiologies have a high optimization potential due to a significant demand of these resources.. · A dialogue between medicine, politics and industry is necessary for a sustainable radiology.. · The discourse, knowledge transfer and public communication of recommendations are part of the sustainability network of the German Roentgen Society (DRG).. CITATION FORMAT: · Palm V, Heye T, Molwitz I et al. Sustainability and Climate Protection in Radiology - An Overview. Fortschr Röntgenstr 2023; 195: 981 - 988.

MRI Compared with Low-Dose CT for Incidental Lung Nodule Detection in COPD: A Multicenter Trial.

Purpose: To investigate morphofunctional chest MRI for the detection and management of incidental pulmonary nodules in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods: In this prospective study, 567 participants (mean age, 66 years ± 9 [SD]; 340 men) underwent same-day contrast-enhanced MRI and nonenhanced low-dose CT (LDCT) in a nationwide multicenter trial (clinicaltrials.gov: NCT01245933). Nodule dimensions, morphologic features, and Lung Imaging Reporting and Data System (Lung-RADS) category were assessed at MRI by two blinded radiologists, and consensual LDCT results served as the reference standard. Comparisons were performed using the Student t test, and agreements were assessed using the Cohen weighted κ. Results: A total of 525 nodules larger than 3 mm in diameter were detected at LDCT in 178 participants, with a mean diameter of 7.2 mm ± 6.1 (range, 3.1-63.1 mm). Nodules were not detected in the remaining 389 participants. Sensitivity and positive predictive values with MRI for readers 1 and 2, respectively, were 63.0% and 84.8% and 60.2% and 83.9% for solid nodules (n = 495), 17.6% and 75.0% and 17.6% and 60.0% for part-solid nodules (n = 17), and 7.7% and 100% and 7.7% and 50.0% for ground-glass nodules (n = 13). For nodules 6 mm or greater in diameter, sensitivity and positive predictive values were 73.3% and 92.2% for reader 1 and 71.4% and 93.2% for reader 2, respectively. Readers underestimated the long-axis diameter at MRI by 0.5 mm ± 1.7 (reader 1) and 0.5 mm ± 1.5 (reader 2) compared with LDCT (P < .001). For Lung-RADS categorization per nodule using MRI, there was substantial to perfect interreader agreement (κ = 0.75-1.00) and intermethod agreement compared with LDCT (κ = 0.70-1.00 and 0.69-1.00). Conclusion: In a multicenter setting, morphofunctional MRI showed moderate sensitivity for detection of incidental pulmonary nodules in participants with COPD but high agreement with LDCT for Lung-RADS classification of nodules.Clinical trial registration no. NCT01245933 and NCT02629432Keywords: MRI, CT, Thorax, Lung, Chronic Obstructive Pulmonary Disease, Screening© RSNA, 2023 Supplemental material is available for this article.

"Will I change nodule management recommendations if I change my CAD system?"-impact of volumetric deviation between different CAD systems on lesion management.

OBJECTIVES: To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study. METHODS: In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems. RESULTS: There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems. CONCLUSIONS: Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist. KEY POINTS: • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules.

Asynchronous calibration of quantitative computed tomography bone mineral density assessment for opportunistic osteoporosis screening: phantom-based validation and parameter influence evaluation.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80-150 kVp) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of - 9.2% ± 14.0% with a strong correlation (R2 = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, - 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine.

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

Changes in aortic diameter induced by weight loss: The HELENA trial- whole-body MR imaging in a dietary intervention trial.

Obesity-related metabolic disorders such as hypertension, hyperlipidemia and chronic inflammation have been associated with aortic dilatation and resulting in aortic aneurysms in many cases. Whether weight loss may reduce the risk of aortic dilatation is not clear. In this study, the diameter of the descending thoracic aorta, infrarenal abdominal aorta and aortic bifurcation of 144 overweight or obese non-smoking adults were measured by MR-imaging, at baseline, and 12 and 50 weeks after weight loss by calorie restriction. Changes in aortic diameter, anthropometric measures and body composition and metabolic markers were evaluated using linear mixed models. The association of the aortic diameters with the aforementioned clinical parameters was analyzed using Spearman`s correlation. Weight loss was associated with a reduction in the thoracic and abdominal aortic diameters 12 weeks after weight loss (predicted relative differences for Quartile 4: 2.5% ± 0.5 and -2.2% ± 0.8, p < 0.031; respectively). Furthermore, there was a nominal reduction in aortic diameters during the 50-weeks follow-up period. Aortic diameters were positively associated with weight, visceral adipose tissue, glucose, HbA1c and with both systolic and diastolic blood pressure. Weight loss induced by calorie restriction may reduce aortic diameters. Future studies are needed to investigate, whether the reduction of aortic diameters via calorie restriction may help to prevent aortic aneurysms.

Changes in Kidney Fat upon Dietary-Induced Weight Loss.

As the metabolic role of kidney fat remains unclear, we investigated the effects of dietary weight loss on kidney fat content (KFC) and its connection to kidney function and metabolism. Overweight or obese participants (n = 137) of a dietary intervention trial were classified into quartiles of weight loss in a post hoc manner. Kidney sinus (KSF) and cortex fat (KCF) were measured by magnetic resonance imaging at baseline, week 12 and week 50. Weight loss effects on KFC were evaluated by linear mixed models. Repeated measures correlations between KFC, other body fat measures and metabolic biomarkers were obtained. KSF, but not KCF, decreased significantly across weight loss quartiles at week 12 (quartile 4: -21.3%; p = 0.02) and 50 (-22.0%, p = 0.001), which remained significant after adjusting for VAT. There were smaller improvements regarding creatinine (-2.5%, p = 0.02) at week 12, but not week 50. KSF, but not KCF, correlated with visceral (rrm = 0.38) and subcutaneous fat volumes (rrm = 0.31) and liver fat content (rrm = 0.32), as well as diastolic blood pressure and biomarkers of lipid, glucose and liver metabolism. Dietary weight loss is associated with decreases in KSF, but not KCF, which suggests that KSF may be the metabolically relevant ectopic fat depot of the kidney. KSF may be targeted for obesity-related disease prevention.

Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard. Some patients do not respond to targeted therapies and many patients suffer from tumor recurrence, which can in part be explained by tumor heterogeneity. This points out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment to assess patient outcome. METHOD: The contents of this review are based on a literature search conducted using the PubMed database in March 2021 and the authors' experience. RESULTS AND CONCLUSION: The use of radiomics and artificial intelligence-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping. Several studies show promising results for models predicting molecular alterations, with the best results being achieved by combining structural and functional imaging. Radiomics could help solve the pressing clinical need for assessing and predicting therapy response. To reach this goal, advanced tumor phenotyping, considering tumor heterogeneity, is required. This could be achieved by integrating structural and functional imaging biomarkers with clinical data sources, such as liquid biopsy results. However, to allow for radiomics-based approaches to be introduced into clinical practice, further standardization using large, multi-center datasets is required. KEY POINTS: · Some NSCLC patients do not benefit from targeted therapies, and many patients suffer from tumor recurrence, pointing out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment.. · The use of radiomics-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping.. · A multi-omics approach integrating not only structural and functional imaging biomarkers but also clinical data sources, such as liquid biopsy results, could further enhance the prediction and assessment of therapy response.. CITATION FORMAT: · Kroschke J, von Stackelberg O, Heußel CP et al. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. Fortschr Röntgenstr 2022; 194: 720 - 727.

An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling.

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.