Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base.

ABSTRACT: Purpose: The aim of this study was to investigate the diagnostic value of machine-learning models with radiomic features and clinical features in preoperative differentiation of common lesions located in the anterior skull base. Methods: A total of 235 patients diagnosed with pituitary adenoma, meningioma, craniopharyngioma, or Rathke cleft cyst were enrolled in the current study. The discrimination was divided into three groups: pituitary adenoma vs. craniopharyngioma, meningioma vs. craniopharyngioma, and pituitary adenoma vs. Rathke cleft cyst. In each group, five selection methods were adopted to select suitable features for the classifier, and nine machine-learning classifiers were employed to build discriminative models. The diagnostic performance of each combination wa

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC7270197 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature