A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients.

To estimate epithermal growth factor receptor (EGFR) expression level in glioblastoma (GBM) patients using radiogenomic analysis of magnetic resonance images (MRI). A comparative study using a deep convolutional neural network (CNN)-based regression, deep neural network, least absolute shrinkage and selection operator (LASSO) regression, elastic net regression, and linear regression with no regularization was carried out to estimate EGFR expression of 166 GBM patients. Except for the deep CNN case, overfitting was prevented by using feature selection, and loss values for each method were compared.

ULTRASONOGRAPHY IN DETERMINING THE RECTAL DIAMETER AND RECTAL WALL THICKNESS IN CHILDREN WITH AND WITHOUT CONSTIPATION: A CASE-CONTROL STUDY.

Background: Ultrasonography has shown to be useful in the diagnosis of constipation.

Objective: The aim of this study was to compare the rectal diameter and rectal wall thickness in children with and without constipation.

Methods: Children with the diagnosis of constipation according to Rome III criteria were included in the study.