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Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.
Improved risk stratification in prevention by use of a panel of selected circulating microRNAs
(2017)
Risk stratification is crucial in prevention. Circulating microRNAs have been proposed as biomarkers in cardiovascular disease. Here a miR panel consisting of miRs related to different cardiovascular pathophysiologies, was evaluated to predict outcome in the context of prevention. MiR-34a, miR-223, miR-378, miR-499 and miR-133 were determined from peripheral blood by qPCR and combined to a risk panel. As derivation cohort, 178 individuals of the DETECT study, and as validation cohort, 129 individuals of the SHIP study were used in a case-control approach. Overall mortality and cardiovascular events were outcome measures. The Framingham Risk Score(FRS) and the SCORE system were applied as risk classification systems. The identified miR panel was significantly associated with mortality given by a hazard ratio(HR) of 3.0 (95% (CI): 1.09–8.43; p = 0.034) and of 2.9 (95% CI: 1.32–6.33; p = 0.008) after adjusting for the FRS in the derivation cohort. In a validation cohort the miR-panel had a HR of 1.31 (95% CI: 1.03–1.66; p = 0.03) and of 1.29 (95% CI: 1.02–1.64; p = 0.03) in a FRS/SCORE adjusted-model. A FRS/SCORE risk model was significantly improved to predict mortality by the miR panel with continuous net reclassification index of 0.42/0.49 (p = 0.014/0.005). The present miR panel of 5 circulating miRs is able to improve risk stratification in prevention with respect to mortality beyond the FRS or SCORE.
Soluble Triggering Receptor Expressed on Myeloid Cells 1 (sTREM-1) can be found in the sera of patients with infectious, autoimmune and malignant diseases. The primary objective of this study was to investigate the prognostic significance of sTREM-1 in lung cancer patients. We analyzed the sera of 164 patients with lung cancer of all histologies and all stages at the time of diagnosis. We employed an ELISA using the anti-TREM-1 clone 6B1.1G12 mAb and recombinant human TREM-1. Patient data was collected retrospectively by chart review. In ROC-analysis, a sTREM-1 serum level of 163.1 pg/ml showed the highest Youden-Index. At this cut-off value sTREM-1 was a marker of short survival in patients with NSCLC (median survival 8.5 vs. 13.3 months, p = 0.04). A Cox regression model showed stage (p < 0.001) and sTREM-1 (p = 0.011) to indicate short survival. There were no differences in sTREM-1 serum values among patients with or without infection, pleural effusion or COPD. sTREM-1 was not associated with metastasis at the time of diagnosis and was not a predictor of subsequent metastasis. In SCLC patients sTREM-1 levels were lower than in NSCLC patients (p = 0.001) and did not predict survival. sTREM-1 did not correlate with CRP or the number of neutrophils. In non-small cell lung cancer patients, sTREM-1 in serum has prognostic significance.