高级别胶质瘤（HGG）和治疗后效应（PTRE）之间的区分是具有挑战性的，但先进的成像技术已经被证明具有益处。我们旨在研究通过氨基酸PET鉴定的代谢区域的微结构特征，并评估这种多模态和综合O-(2-18F-氟乙基)-L-酪氨酸-(FET)-PET和快速弥散峭曲率成像（DKI）方法在复发和IDH基因分型检测中的诊断潜力。使用静态18F-FET PET和快速DKI变体调查了59名经神经病理学确认的复发高级别胶质瘤（n = 39）或PTRE（n = 20）参与者。评估了代谢定义的PET和先进扩散指标（80-100%和60-75%的18F-FET摄取区域）。采用Mann-Whitney U检验进行比较分析，使用Holm-Šídák多重比较检验和Wilcoxon符号秩检验。使用受试者工作特征（ROC）曲线、回归和Spearman相关分析进行统计评估。与PTRE相比，复发HGG呈现出增加的18F-FET摄取和扩散性（MD60），但较低的（相对的）平均峭曲张量（rMKT60）和分数量化（FA60）（分别p < .05）。从代谢周边确定的扩散指标显示出改进的诊断性能 - 对于FA60来说最为明显（AUC = 0.86，p < .001），其与18F-FET PET（AUC = 0.86，p < .001）具有类似的益处，并且与氨基酸摄取呈负相关（rs = -0.46，p < .001）。当评估PET和DKI指标时，采用多模态双参数方法进行评估，TBRmax + FA60显示出最高的诊断准确性（AUC = 0.93，p < .001），与PET相比改善了复发的检测（AUC的差异= 0.069，p = 0.04）。FA60和MD60可以区分治疗后的IDH基因型。通过多模态和综合PET / DKI方法检测胶质瘤复发有益于诊断，这相对单独使用PET进行评估具有显著的诊断优势。多模态和综合的18F-FET PET / 快速DKI方法用于无创代谢区域的微结构表征，提高了鉴别复发胶质瘤和治疗后变化的诊断能力，为治疗后患者的诊断工作提供了参考。• 采用整合分析的18F-FET PET和快速DKI的多模态PET / MRI在中枢神经系统癌症评估中具有临床益处，特别是对于检测复发的高级别胶质瘤。• 代谢周边的微结构标志物提供了生物学相关的估计，用以表征肿瘤微环境，并因此具有与氨基酸PET类似的改善诊断准确性的效果。• 结合18F-FET PET和快速DKI可以在检测高级别胶质瘤复发方面实现最佳诊断性能，相较于单独使用PET进行评估具有显著的好处。© 2023年。作者（们）。

Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-L-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping.Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80-100% and 60-75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann-Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations.Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p < .05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance - most pronounced for FA60 (AUC = 0.86, p < .001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p < .001) and was negatively correlated with amino acid uptake (rs =  - 0.46, p < .001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p < .001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p = .04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting.Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone.A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings.• Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone.© 2023. The Author(s).