Magnetic resonance imaging · 2025
Time-dependent diffusion MRI-based microstructural mapping for predicting IDH mutation status in glioma: A multicenter study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hu Wanjun, Liu Wentao, Li Darui, Niu Liang, Han Yuping, Chang Junwei, Xu Tianyong, Xiong Yuhui, Ouyang Zhiqiang, Liang Qiang, Zhang Jing
Аннотация
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 ADCPGSE_firstorder_Energy and cellularity_firstorder_10Percentile yielded AUCs of 0.801 (training) and 0.771 (validation). ADCPGSE and cellularity were positively correlated with the Ki-67 index (P < 0.05).
CONCLUSION: Histogram features from td-dMRI (ADCPGSE and cellularity) enabled robust IDH mutation prediction in a multicenter glioma cohort.
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 ADCPGSE_firstorder_Energy and cellularity_firstorder_10Percentile yielded AUCs of 0.801 (training) and 0.771 (validation). ADCPGSE and cellularity were positively correlated with the Ki-67 index (P < 0.05).
CONCLUSION: Histogram features from td-dMRI (ADCPGSE and cellularity) enabled robust IDH mutation prediction in a multicenter glioma cohort.