Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples.

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets. We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine). According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

Association of beta-defensin-1 gene polymorphisms with Pseudomonas aeruginosa airway colonization in cystic fibrosis.

Lung disease and Pseudomonas aeruginosa (P. aeruginosa) airway colonization represent a major cause of morbidity and mortality in cystic fibrosis (CF). Human beta-defensin (hBD)-1 is believed to play an important role in mucosal innate immunity in the lung. This study aimed to investigate whether three single-nucleotide polymorphisms (SNPs) in the 5'-untranslated region of DEFB1, G-52A, C-44G and G-20A were associated with P. aeruginosa airway colonization in CF. A total of 224 CF patients and 196 control subjects were studied. DEFB1 SNPs were characterized by restriction fragment length polymorphisms. Patients' sputum samples were collected and analyzed by standard methods. Single SNP analysis suggested that CF patients carrying the -52AA and the -20GG genotypes had a higher rate of P. aeruginosa airway colonization than patients homozygous and heterozygous for the -52G and -20A alleles (P=0.01 and P=0.007, respectively). A significant association between the ACG haplotype and chronic P. aeruginosa infection was also identified (odds ratio (95% confidence interval): 3.00 (1.42-6.36), P=0.004). These results indicate that variant alleles in DEFB1 might contribute to the colonization of P. aeruginosa in CF.

[Biology of the "Salmonella wien" (author's transl)]. / Biologia della "Salmonella Wien"

The Authors after a theoric introduction in regard to the taxonomic position, the history and the epidemiology of the Salmonella wien, explain the biologic characteristic of the bacterium according to results of their experiments.