RESUMO
BACKGROUND: The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the "bouncing beta" phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software. RESULTS: Thirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the "elbow sign" of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions. CONCLUSIONS: PCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.
RESUMO
TGFbeta1 plasma levels and TGFbeta1 genetic polymorphisms have been linked to radiation-induced lung injury. As the correlation between plasma levels and genotype is not always straightforward, this study investigated the association between TGFbeta1 genotype, considering several polymorphisms, and in vitro TGFbeta1 secretion of human lymphocytes. Four polymorphisms in TGFbeta1 (c.-800, c.-509, c.29, c.74) affecting TGFbeta1 secretion and a deletion (c.69_77del9) in TGFbetaR1 influencing TGFbeta1 signaling were genotyped in 250 healthy individuals. TGFbeta1 levels were determined for 10 individuals with wild type alleles for all considered polymorphisms and for 21 individuals harboring variant alleles of a single polymorphism and no variant alleles at any other considered polymorphic location. After cell proliferation and cell viability quality control criteria, TGFbeta1 levels were obtained for 17 individuals. Mean TGFbeta1 levels were 1046.2pg, 566.7pg, 740.6pg, 1001.8pg, 701.2pg and 2844pg per 10(6)cells for, respectively, c.-800 heterozygous, c.-509/c.29 heterozygous, c.-509/c.29 homozygous variant, c.69_77del9 heterozygous and c.69_77del9 homozygous variant cells. Only for c.-800 heterozygous cells this difference was borderline statistically significant compared to wild type cells (p=0.071). In conclusion, the effect of genetic polymorphisms in TGFbeta1/TGFbetaR1 on secreted TGFbeta1 levels in vitro could not be clarified. The inter-individual variation in TGFbeta1 levels for individuals with the same genotype was too large to obtain statistically significant differences among genotypes.
