Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma.

ABSTRACT:

Objectives

To investigate the predictive value of the quantitative T2-FLAIR mismatch ratio (qT2FM) with fully automated tumor segmentation in adult-type diffuse lower-grade gliomas (LGGs).

Materials and methods

This retrospective study included 218 consecutive patients (mean age, 47 years ± 15 [SD]; 125 males) diagnosed with adult-type diffuse LGG. The cohort was classified into IDH wild-type (IDHwt), IDH-mutant with 1p/19q-codeletion (IDHmut-Codel), and IDH-mutant without 1p/19q-codeletion (IDHmut-Noncodel) subtypes. Tumor masks were obtained using deep learning-based segmentation, and qT2FM was calculated from the differences in signal intensity ratios on T2 and FLAIR images. Multivariable logistic regression identified predictors for identifying IDHmut-Noncodel and IDH mu

SUBMITTER: Jeon YH 

PROVIDER: S-EPMC12350433 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature