Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics.

ABSTRACT:

Objectives

The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.

Methods

A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed b

SUBMITTER: Zhai Y 

PROVIDER: S-EPMC8187861 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature