European journal of radiology · 2026
Multiparametric MRI radiomics improves growth risk stratification beyond conventional imaging in incidental asymptomatic meningiomas.
СтатьяНазвание и аннотация: Перевод в очереди
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Mei Nan, Chen Yao, Yin Bo, Wang Dongdong, Chen Yumeng, Zheng Zhisong, Cui Jin, Zhao Yajing, Li Xuanxuan, Ruan Zhuoying, Sawlani Vijay, Xiong Ji, Liu Li, Lu Yiping
Аннотация
OBJECTIVE: Incidental asymptomatic meningiomas are increasingly detected, yet predicting growth remains challenging. We aimed to determine whether a combined model integrating multiparametric MRI radiomics with clinical and semantic features improves growth risk stratification beyond conventional imaging in incidental asymptomatic meningiomas.
METHODS: This retrospective study included 519 patients from Institution A (training, n = 416; internal validation, n = 103) and 83 patients from two external institutions (external validation). Tumor growth was defined by volumetric criteria. Radiomic features were extracted from contrast-enhanced T1-weighted imaging (T1C), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and apparent diffusion coefficient (ADC) sequences. A random survival forest (RSF) model combining all features was developed and validated. A simplified scoring system was derived from the most important features.
RESULTS: The combined RSF model demonstrated excellent discrimination, with C-indices of 0.901 (training), 0.837 (internal validation), and 0.813 (external validation), significantly outperforming a clinical-only model (ΔC-index up to 0.223). 9-point radiomics-based scoring system (cutoff = 5 points) stratified patients into high- and low-risk groups. The top predictive features included T1C (GLSZM GrayLevelNonUniformity, wavelet GLCM autocorrelation), T2-FLAIR and ADC texture features, and surface area.
CONCLUSION: Multiparametric MRI radiomics improves growth risk stratification beyond conventional imaging. The simplified scoring system may support personalized surveillance while reducing unnecessary interventions, although prospective validation is required before clinical implementation.
METHODS: This retrospective study included 519 patients from Institution A (training, n = 416; internal validation, n = 103) and 83 patients from two external institutions (external validation). Tumor growth was defined by volumetric criteria. Radiomic features were extracted from contrast-enhanced T1-weighted imaging (T1C), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and apparent diffusion coefficient (ADC) sequences. A random survival forest (RSF) model combining all features was developed and validated. A simplified scoring system was derived from the most important features.
RESULTS: The combined RSF model demonstrated excellent discrimination, with C-indices of 0.901 (training), 0.837 (internal validation), and 0.813 (external validation), significantly outperforming a clinical-only model (ΔC-index up to 0.223). 9-point radiomics-based scoring system (cutoff = 5 points) stratified patients into high- and low-risk groups. The top predictive features included T1C (GLSZM GrayLevelNonUniformity, wavelet GLCM autocorrelation), T2-FLAIR and ADC texture features, and surface area.
CONCLUSION: Multiparametric MRI radiomics improves growth risk stratification beyond conventional imaging. The simplified scoring system may support personalized surveillance while reducing unnecessary interventions, although prospective validation is required before clinical implementation.