Magnetic resonance imaging findings of dysembryoplastic neuroepithelial tumors and low-grade astrocytomas

Read full text
Authors: Kai-Wei Yu, Shih-Chieh Lin, Hsin-Hung Chen, Chia-Hung Wu, Wei-An Tai, Chung-Han Yang, Te-Ming Lin, Feng‐Chi Chang
Cited by: 0
References: 27

Abstract

BACKGROUND: Dysembryoplastic neuroepithelial tumor (DNET) and low-grade astrocytoma (LGA) could present very similar magnetic resonance imaging (MRI) findings. DNET has good seizure control after surgical removal and low malignant potential, while LGA may recur or progress. This study was designed to obtain a more accurate pretreatment diagnosis of DNET and LGA based on MRI findings. METHODS: We retrospectively enrolled patients with pathologically proven DNET and LGA from 2000 to 2024. Individual qualitative and quantitative MRI features were evaluated in both tumors, especially the T2-weighted signal intensity ratio (T2SR), which compared tumor T2-weighted signal with that of normal cerebral white matter. The diagnostic performance of conventional qualitative models, including meaningful qualitative MRI features and combined quantitative model, including T2SR and apparent diffusion coefficient (ADC) values, was evaluated using area under the curve (AUC). RESULTS: In total, 70 patients (30 DNET, 40 LGA) were included, with a mean age of 23.2 ± 11.3 (15-57) years, including 36 men (51.4%), 34 women (48.6%). For individual MRI features, DNET had more FLAIR (fluid attenuated inversion recovery) ring sign (16 [53.3%] vs 12 [30.0%], p = 0.049), higher ADC value (2076.2 [53.4] vs 1660.1 [71.9], p < 0.001), and higher T2SR (3.56 ± 0.12 vs 2.68 ± 0.63, p < 0.001). The AUC of the T2SR and ADC value was 0.886 (0.811-0.962) and 0.824 (0.724-0.924), respectively. The combined quantitative model had higher discriminative performance than the conventional qualitative model (AUC: 0.905 vs 0.727, p = 0.011). CONCLUSION: Our study suggested that quantitative MRI features, including T2SR and ADC values, enhanced the discrimination between DNET and LGA and could potentially serve as a complementary imaging marker for improving preoperative diagnostic accuracy.

Fields of Study & Subjects

Glioma Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging Tuberous Sclerosis Complex Research

Publication details

Journal / source
Journal of the Chinese Medical Association
Volume
89
Issue
3
Pages
228–234
Publication date
6 February 2026
Language
EN