目的 研究扩散 MRI (dMRI) 在脑干胶质瘤 (BSG) H3K27M 基因分型中的价值。纳入了具有 dMRI 数据（b = 0、1000 和 2000 s/mm2）和 H3K27M 突变信息的 BSG 患者主要队列。总共计算了 13 个扩散张量和峰度成像 (DTI; DKI) 指标，然后从每个指标中提取 17 个全肿瘤直方图特征和 29 个沿束白质 (WM) 微结构测量值并在基因型内进行评估。通过单变量分析和最小绝对收缩和选择算子方法进行特征选择后，使用多变量逻辑回归分别和联合基于保留的肿瘤和 WM 特征构建 dMRI 衍生的基因分型模型。使用 ROC 曲线测试模型性能，并通过 DeLong 方法进行比较。通过多变量逻辑回归生成包含表现最佳的 dMRI 模型和临床变量的列线图，并在 27 名 BSG 患者的独立队列中进行验证。主要队列中总共包括 117 名患者（80 名 H3K27M 突变体）。总共选择了 29 个肿瘤直方图特征和 41 个 WM 束测量值用于后续基因分型模型构建。结合 WM 束测量显着提高了诊断性能 (p < 0.05)。结合 DKI 和 DTI 指标的肿瘤和 WM 特征的模型显示出最佳性能（AUC = 0.9311）。结合该 dMRI 模型和临床变量的列线图在主要队列和验证队列中分别实现了 0.9321 和 0.8951 的 AUC。dMRI 在 BSG 基因分型中很有价值。肿瘤扩散直方图特征对基因分型有用，而WM束测量对于提高基因分型性能更有价值。本研究发现扩散MRI对于预测脑干胶质瘤H3K27M突变具有重要价值，有助于实现脑干胶质瘤基因型的无创检测和提高脑干胶质瘤的诊断率。• 弥散MRI在脑干胶质瘤H3K27M基因分型中具有重要价值，建立的模型性能满意。 • 全肿瘤扩散直方图特征在H3K27M 基因分型中非常有用，并且白质束的定量测量很有价值，因为它们有可能提高模型性能。 • 该模型结合了最具辨别力的扩散MRI 模型和临床变量，有助于做出临床决策。© 2023。作者，获得欧洲放射学会的独家许可。

To investigate the value of diffusion MRI (dMRI) in H3K27M genotyping of brainstem glioma (BSG).A primary cohort of BSG patients with dMRI data (b = 0, 1000 and 2000 s/mm2) and H3K27M mutation information were included. A total of 13 diffusion tensor and kurtosis imaging (DTI; DKI) metrics were calculated, then 17 whole-tumor histogram features and 29 along-tract white matter (WM) microstructural measurements were extracted from each metric and assessed within genotypes. After feature selection through univariate analysis and the least absolute shrinkage and selection operator method, multivariate logistic regression was used to build dMRI-derived genotyping models based on retained tumor and WM features separately and jointly. Model performances were tested using ROC curves and compared by the DeLong approach. A nomogram incorporating the best-performing dMRI model and clinical variables was generated by multivariate logistic regression and validated in an independent cohort of 27 BSG patients.At total of 117 patients (80 H3K27M-mutant) were included in the primary cohort. In total, 29 tumor histogram features and 41 WM tract measurements were selected for subsequent genotyping model construction. Incorporating WM tract measurements significantly improved diagnostic performances (p < 0.05). The model incorporating tumor and WM features from both DKI and DTI metrics showed the best performance (AUC = 0.9311). The nomogram combining this dMRI model and clinical variables achieved AUCs of 0.9321 and 0.8951 in the primary and validation cohort respectively.dMRI is valuable in BSG genotyping. Tumor diffusion histogram features are useful in genotyping, and WM tract measurements are more valuable in improving genotyping performance.This study found that diffusion MRI is valuable in predicting H3K27M mutation in brainstem gliomas, which is helpful to realize the noninvasive detection of brainstem glioma genotypes and improve the diagnosis of brainstem glioma.• Diffusion MRI has significant value in brainstem glioma H3K27M genotyping, and models with satisfactory performances were built. • Whole-tumor diffusion histogram features are useful in H3K27M genotyping, and quantitative measurements of white matter tracts are valuable as they have the potential to improve model performance. • The model combining the most discriminative diffusion MRI model and clinical variables can help make clinical decision.© 2023. The Author(s), under exclusive licence to European Society of Radiology.