Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.

ABSTRACT:

Objective

Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.

Materials and methods

This study retrospectively collected data from 601 patients with meningioma confirmed by surgical pathology. For each patient, 3948 radiomics features, 12,288 VGG features, 6144 ResNet features, and 3072 DenseNet features were extracted from MRI images. Thus, univariate logistic regression, correlation analysis, and the Boruta algorithm were applied for further feature dimension reduction, selecting radiomics and DL features highly associated with meningioma sinus invasion. Finally, diagnosis models were constructed using the random forest (RF) algorit

SUBMITTER: Gui Y 

PROVIDER: S-EPMC11869444 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature