BackgroundGrading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading.MethodsWe considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort.ResultsFive significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts.DiscussionGlioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.

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Transmissible spongiform encephalopathies (TSEs, prion diseases) are a class of fatal neurodegenerative diseases affecting a variety of mammalian species including humans. A misfolded form of the prion protein (PrP TSE ) is the major, if not sole, component of the infectious agent. Recent TSE outbreaks in domesticated and wild animal populations has created the need for safe and effective disposal of large quantities of potentially infected materials. Here, we report the results of a study to evaluate the potential for transport of PrP TSE derived from carcasses and associated wastes in a municipal solid waste (MSW) landfill. Column experiments were conducted to evaluate PrP TSE transport in quartz sand, two fine-textured burial soils currently used in landfill practice, a green waste residual material (a potential burial material), and fresh and aged MSW. PrP TSE was retained by quartz sand and the fine-textured burial soils, with no detectable PrP TSE eluted over more than 40 pore volumes. In contrast, PrP TSE was more mobile in MSW and green waste residual. Transport parameters were estimated from the experimental data and used to model PrP TSE migration in a MSW landfill. To the extent that the PrP TSE used mimics that released from decomposing carcasses, burial of CWD-infected materials at MSW landfills could provide secure containment of PrP TSE provided reasonable burial strategies (e.g., encasement in soil) are used.

Growing evidence suggests that the efficacy of immunotherapy in non-small cell lung cancers (NSCLCs) is associated with the immune microenvironment within the tumor. We aimed to explore radiologic phenotyping using a radiomics approach to assess the immune microenvironment in NSCLC. Two independent NSCLC cohorts (training and test sets) were included. Single-sample gene set enrichment analysis was used to determine the tumor microenvironment, where type 1 helper T (Th1) cells, type 2 helper T (Th2) cells, and cytotoxic T cells were the targets for prediction with computed tomographic (CT) radiomic features. Multiple algorithms were in the modeling followed by final model selection. The training dataset comprised 89 NSCLCs and the test set included 60 cases of lung squamous cell carcinoma and adenocarcinoma. A total of 239 CT radiomic features were used. A linear discriminant analysis model was selected for the final model of Th2 cell group prediction. The area under the curve value of the final model on the test set was 0.684. Predictors of the linear discriminant analysis model were skewness (total and outer pixels), kurtosis, variance (subsampled from delta [subtraction inner pixels from outer pixels]), and informational measure of correlation. The performances of radiomics on test set of Th1 and cytotoxic T cell were not accurate enough to be predictable. A radiomics approach can be used to interrogate an entire tumor in a noninvasive manner and provide added diagnostic value to identify the immune microenvironment of NSCLC, in particular, Th2 cell signatures. Fig 3. Distributions of type 1 helper T-cell, type 2 helper T-cell, and cytotoxic T cell signatures of the test set (TCGA cohort) and training set ("Lung3" cohort).