Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors.

ABSTRACT: Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs). Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen. Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linea

SUBMITTER: Chen C 

PROVIDER: S-EPMC6714109 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature