Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning.

ABSTRACT:

Background

Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration.

Methods

A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit th

SUBMITTER: Zhang C 

PROVIDER: S-EPMC9373412 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature