MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets.

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses

final results of a noninterventional study

Background Data are limited regarding routine use of everolimus after initial vascular endothelial growth factor (VEGF)–targeted therapy. The aim of this prospective, noninterventional, observational study was to assess efficacy and safety of everolimus after initial VEGF-targeted treatment in patients with metastatic renal cell carcinoma (mRCC) in routine clinical settings. Methods Everolimus was administered per routine clinical practice. Patients with mRCC of any histology from 116 active sites in Germany were included. The main objective was to determine everolimus efficacy in time to progression (TTP). Progression-free survival (PFS), treatment duration, tumor response, adherence to everolimus regimen, treatment after everolimus, and safety were also assessed. Results In the total population (N = 334), median follow-up was 5.2 months (range, 0–32 months). Median treatment duration (safety population, n = 318) was 6.5 months (95% confidence interval [CI], 5–8 months). Median TTP and median PFS were similar in populations investigated. In patients who received everolimus as second-line treatment (n = 211), median (95% CI) TTP was 7.1 months (5–9 months) and median PFS was 6.9 months (5–9 months). Commonly reported adverse events (safety population, n = 318) were dyspnea (17%), anemia (15%), and fatigue (12%). Limitations of the noninterventional design should be considered. Conclusions This study reflects routine clinical use of everolimus in a large sample of patients with mRCC. Favorable efficacy and safety were seen for everolimus after previous therapy with one VEGF-targeted agent. Results of this study confirm everolimus as one of the standard options in second-line therapy for patients with mRCC. Novartis study code, CRAD001LD27: VFA registry for noninterventional studies (http://www.vfa.de/de/forschung/nisdb/ webcite)

Everolimus in metastatic renal cell carcinoma after failure of initial anti-VEGF therapy: final results of a noninterventional study

Background: Data are limited regarding routine use of everolimus after initial vascular endothelial growth factor (VEGF)-targeted therapy. The aim of this prospective, noninterventional, observational study was to assess efficacy and safety of everolimus after initial VEGF-targeted treatment in patients with metastatic renal cell carcinoma (mRCC) in routine clinical settings. Methods: Everolimus was administered per routine clinical practice. Patients with mRCC of any histology from 116 active sites in Germany were included. The main objective was to determine everolimus efficacy in time to progression (TTP). Progression-free survival (PFS), treatment duration, tumor response, adherence to everolimus regimen, treatment after everolimus, and safety were also assessed. Results: In the total population (N = 334),median follow-up was 5.2 months (range, 0-32 months). Median treatment duration (safety population, n = 318) was 6.5 months (95% confidence interval [CI], 5-8 months). Median TTP and median PFS were similar in populations investigated. In patients who received everolimus as second-line treatment (n = 211),median (95% CI) TTP was 7.1 months (5-9 months) and median PFS was 6.9 months (5-9 months). Commonly reported adverse events (safety population, n = 318) were dyspnea (17%),anemia (15%), and fatigue (12%). Limitations of the noninterventional design should be considered. Conclusions: This study reflects routine clinical use of everolimus in a large sample of patients with mRCC. Favorable efficacy and safety were seen for everolimus after previous therapy with one VEGF-targeted agent. Results of this study confirm everolimus as one of the standard options in second-line therapy for patients with mRCC

a targeted metabolomic approach in two German prospective cohorts

Metabolomic approaches in prospective cohorts may offer a unique snapshot into early metabolic perturbations that are associated with a higher risk of cardiovascular diseases (CVD) in healthy people. We investigated the association of 105 serum metabolites, including acylcarnitines, amino acids, phospholipids and hexose, with risk of myocardial infarction (MI) and ischemic stroke in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) and Heidelberg (25,540 adults) cohorts. Using case-cohort designs, we measured metabolites among individuals who were free of CVD and diabetes at blood draw but developed MI (n = 204 and n = 228) or stroke (n = 147 and n = 121) during follow-up (mean, 7.8 and 7.3 years) and among randomly drawn subcohorts (n = 2214 and n = 770). We used Cox regression analysis and combined results using meta-analysis. Independent of classical CVD risk factors, ten metabolites were associated with risk of MI in both cohorts, including sphingomyelins, diacyl-phosphatidylcholines and acyl-alkyl- phosphatidylcholines with pooled relative risks in the range of 1.21–1.40 per one standard deviation increase in metabolite concentrations. The metabolites showed positive correlations with total- and LDL-cholesterol (r ranged from 0.13 to 0.57). When additionally adjusting for total-, LDL- and HDL- cholesterol, triglycerides and C-reactive protein, acyl-alkyl- phosphatidylcholine C36:3 and diacyl-phosphatidylcholines C38:3 and C40:4 remained associated with risk of MI. When added to classical CVD risk models these metabolites further improved CVD prediction (c-statistics increased from 0.8365 to 0.8384 in EPIC-Potsdam and from 0.8344 to 0.8378 in EPIC- Heidelberg). None of the metabolites was consistently associated with stroke risk. Alterations in sphingomyelin and phosphatidylcholine metabolism, and particularly metabolites of the arachidonic acid pathway are independently associated with risk of MI in healthy adults

Alcohol Consumption, Genetic Variants in Alcohol Deydrogenases, and Risk of Cardiovascular Diseases: A Prospective Study and Meta-Analysis

OBJECTIVE: First, to investigate and compare associations between alcohol consumption and variants in alcohol dehydrogenase (ADH) genes with incidence of cardiovascular diseases (CVD) in a large German cohort. Second, to quantitatively summarize available evidence of prospective studies on polymorphisms in ADH1B and ADH1C and CVD-risk. METHODS: We conducted a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort including a randomly drawn subcohort (n = 2175) and incident cases of myocardial infarction (MI; n = 230) or stroke (n = 208). Mean follow-up time was 8.2±2.2 years. The association between alcohol consumption, ADH1B or ADH1C genotypes, and CVD-risk was assessed using Cox proportional hazards regression. Additionally, we report results on associations of variants in ADH1B and ADH1C with ischemic heart disease and stroke in the context of a meta-analysis of previously published prospective studies published up to November 2011. RESULTS: Compared to individuals who drank >0 to 6 g alcohol/d, we observed a reduced risk of MI among females consuming >12 g alcohol/d (HR = 0.31; 95% CI: 0.10-0.97) and among males consuming >24 to 60 g/d (HR = 0.57; 95% CI: 0.33-0.98) or >60 g alcohol/d (HR = 0.30; 95% CI: 0.12-0.78). Stroke risk was not significantly related to alcohol consumption >6 g/d, but we observed an increased risk of stroke in men reporting no alcohol consumption. Individuals with the slow-coding ADH1B*1/1 genotype reported higher median alcohol consumption. Yet, polymorphisms in ADH1B or ADH1C were not significantly associated with risk of CVD in our data and after pooling results of eligible prospective studies [ADH1B*1/1: RR = 1.35 (95% CI: 0.98-1.88; p for heterogeneity: 0.364); ADH1C*2/2: RR = 1.07 (95% CI: 0.90-1.27; p for heterogeneity: 0.098)]. CONCLUSION: The well described association between alcohol consumption and CVD-risk is not reflected by ADH polymorphisms, which modify the rate of ethanol oxidation

The Spatial Relationship between Apparent Diffusion Coefficient and Standardized Uptake Value of 18

The minimum apparent diffusion coefficient (ADCmin) derived from diffusion-weighted MRI (DW-MRI) and the maximum standardized uptake value (SUVmax) of FDG-PET are markers of aggressiveness in lung cancer. The numeric correlation of the two parameters has been extensively studied, but their spatial interplay is not well understood. After FDG-PET and DW-MRI coregistration, values and location of ADCmin- and SUVmax-voxels were analyzed. The upper limit of the 95% confidence interval for registration accuracy of sequential PET/MRI was 12 mm, and the mean distance (D) between ADCmin- and SUVmax-voxels was 14.0 mm (average of two readers). Spatial mismatch (D > 12 mm) between ADCmin and SUVmax was found in 9/25 patients. A considerable number of mismatch cases (65%) was also seen in a control group that underwent simultaneous PET/MRI. In the entire patient cohort, no statistically significant correlation between SUVmax and ADCmin was seen, while a moderate negative linear relationship (r=-0.5) between SUVmax and ADCmin was observed in tumors with a spatial match (D ≤ 12 mm). In conclusion, spatial mismatch between ADCmin and SUVmax is found in a considerable percentage of patients. The spatial connection of the two parameters SUVmax and ADCmin has a crucial influence on their numeric correlation

Machine learning in cardiovascular radiology:ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. KEY POINTS: • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms