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https://www.selleckchem.com/products/mg149.html Although slight differences in R50% and nCI were found, the parameters most affecting MS were MU and DT. VOLO solutions were selected in 80% of cases by both physicians with 88% inter-observer agreement. CONCLUSIONS The good performance of the VOLO optimization system, together with the large reduction in OT, make it a useful tool to improve the efficiency of CK SBRT planning and delivery. The proposed methodology for comparing different planning solutions can be applied in other contexts. Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development. We report the electronic properties of black phosphorene nanoring (BPN) and adsorption behavior of chronic kidney disease biomarker vapors on BPN. The designed BPN is stable, which is ensured by the formation energy

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