Time-dependent diffusion MRI–based microstructural mapping for predicting IDH mutation status in glioma: A multicenter study

IF 2.1 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Magnetic resonance imaging Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI:10.1016/j.mri.2025.110598

Wanjun Hu , Wentao Liu , Darui Li , Liang Niu , Yuping Han , Junwei Chang , Tianyong Xu , Yuhui Xiong , Zhiqiang Ouyang , Qiang Liang , Jing Zhang

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引用次数: 0

Abstract

Objectives

To evaluate whether time-dependent diffusion MRI(td-dMRI)-based microstructural histogram parameters can accurately distinguish IDH mutation status in gliomas, with multicenter external validation.

Methods

In this prospective dual-center study, patients with pathologically confirmed glioma underwent preoperative conventional MRI and td-dMRI-based. Microstructural parameters including intracellular volume fraction (Vin), intracellular diffusivity (Din), extracellular diffusivity (Dex), cellularity, and cell diameter—were derived using the IMPULSED model. ADC values at different frequencies (25 Hz, 50 Hz, PGSE) were also computed. Tumor regions were manually segmented by two radiologists, excluding necrosis and edema. Voxel-wise values were extracted, and histogram features were computed using PyRadiomics. Feature selection included the Mann–Whitney U test, Spearman correlation, and logistic regression. A predictive model for IDH mutation status was developed using data from Center A and externally validated in Center B. Model performance was assessed using AUC, calibration curves, and confusion matrices.

Results

Among 147 histogram features extracted from seven td-dMRI–based mapping, eight nonredundant features significantly differed between IDH-wildtype and IDH-mutant gliomas. A logistic regression model based on ADC_PGSE_firstorder_Energy and cellularity_firstorder_10Percentile yielded AUCs of 0.801 (training) and 0.771 (validation). ADC_PGSE and cellularity were positively correlated with the Ki-67 index (P < 0.05).

Conclusion

Histogram features from td-dMRI (ADC_PGSE and cellularity) enabled robust IDH mutation prediction in a multicenter glioma cohort.

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预测胶质瘤中IDH突变状态的时间依赖扩散核磁共振显微结构定位：一项多中心研究。

目的：通过多中心外部验证，评估基于时间依赖扩散MRI（td-dMRI）的显微结构直方图参数能否准确区分胶质瘤中IDH突变状态。方法：在这项前瞻性双中心研究中，病理证实的胶质瘤患者术前接受常规MRI和td- dmri。微结构参数包括细胞内体积分数（Vin）、细胞内扩散率（Din）、细胞外扩散率（Dex）、细胞度和细胞直径——使用impulse模型推导。还计算了不同频率（25 Hz, 50 Hz， PGSE）下的ADC值。肿瘤区域由两名放射科医生手工分割，排除坏死和水肿。提取体素值，并使用PyRadiomics计算直方图特征。特征选择包括Mann-Whitney U检验、Spearman相关和logistic回归。利用A中心的数据建立了IDH突变状态的预测模型，并在b中心进行了外部验证。使用AUC、校准曲线和混淆矩阵评估模型的性能。结果：从7个基于td- dmri的图谱中提取的147个直方图特征中，有8个非冗余特征在idh -野生型和idh -突变型胶质瘤之间存在显著差异。基于adcpgse_first storder_energy和cellularity_first storder_10percentile的逻辑回归模型的auc为0.801（训练）和0.771（验证）。ADCPGSE和细胞性与Ki-67指数呈正相关（P ）结论：来自dd - dmri的直方图特征（ADCPGSE和细胞性）能够在多中心胶质瘤队列中进行稳健的IDH突变预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Magnetic resonance imaging 医学-核医学

CiteScore

4.70

自引率

4.00%

发文量

194

审稿时长

83 days

期刊介绍： Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.