Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology.
Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology.
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VERDICT（肿瘤细胞计数的血管、细胞外和限制扩散）是一种扩散 MRI 框架，用于表征肿瘤不同成分的特征，已显示出对身体癌症的诊断实用性。本研究的目的是扩展 VERDICT 框架以全面表征脑肿瘤，由于脑组织的复杂性，这具有挑战性。由此产生的生物标志物显示出与组织学的一致性，并且在比较不同肿瘤类型和亚区域时遵循预期的趋势。这些初步结果有望通过 VERDICT-MRI 对脑肿瘤进行非侵入性表征，这将成为诊断和监测治疗效果的重要工具。这项工作的目的是扩展用于脑肿瘤建模的 VERDICT-MRI 框架，从而能够全面表征肿瘤内和肿瘤周围区域，特别关注细胞和血管特征。弥散 MRI 数据是通过 21 名患有不同类型且具有多种细胞和血管特征的脑肿瘤患者的多个 b 值（范围从 50 至 3500 s/mm2）、弥散时间和回声时间获得的。我们拟合了一系列由不同类型的细胞内、细胞外和血管区室与信号组合产生的扩散模型。我们使用简约标准对模型进行了比较，同时旨在对所有关键组织学脑肿瘤成分进行良好表征。最后，我们使用 ADC（表观扩散系数）作为临床标准参考，评估了肿瘤组织类型分化中表现最佳的模型的参数，并将其与组织病理学和相关灌注 MRI 指标进行比较。脑肿瘤中 VERDICT 表现最好的模型是三室模型，考虑了各向异性受阻和各向同性限制扩散以及各向同性伪扩散。 VERDICT 指标与低级别胶质瘤和转移瘤的组织学外观相一致，并反映了肿瘤内多个活检样本之间的组织病理学发现的差异。组织型之间的比较表明，在高细胞结构的肿瘤（胶质母细胞瘤和转移瘤）中，细胞内和血管分数往往较高，定量分析显示，随着神经胶质瘤分级的增加，肿瘤核心内的细胞内分数（fic）有较高值的趋势。我们还观察到，与胶质母细胞瘤和 WHO 3 胶质瘤周围以及低级别胶质瘤周围的浸润性水肿相比，转移瘤周围血管性水肿的自由水分数较高。总之，我们基于 VERDICT 框架开发并评估了脑肿瘤的多室扩散 MRI 模型，该模型显示了非侵入性微观结构估计和组织学之间的一致性，以及肿瘤类型和亚区域分化的令人鼓舞的趋势。
VERDICT (Vascular, Extracellular, and Restricted DIffusion for Cytometry in Tumours) is a diffusion MRI framework for the characterisation of different components of tumours, which has shown diagnostic utility for body cancer. The aim of this study was to extend the VERDICT framework to comprehensively characterise brain tumours, which is challenging due to the complexity of brain tissues. The resulting biomarkers showed agreement with histology and followed the expected trends when comparing different tumour types and sub-regions. These preliminary results hold promise for the non-invasive characterisation of brain tumours by VERDICT-MRI, which would be an important tool for diagnosis and monitoring of treatment effects. The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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影响因子：
4.7
作者：
Bailey C;Collins DJ;Tunariu N;Orton MR;Morgan VA;Feiweier T;Hawkes DJ;Leach MO;Alexander DC;Panagiotaki E
通讯作者：
Panagiotaki E
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
3.3
作者：
Nilsson, Markus;Szczepankiewicz, Filip;Sundgren, Pia C.
通讯作者：
Sundgren, Pia C.
影响因子：
3.3
作者：
Kellner, Elias;Dhital, Bibek;Reisert, Marco
通讯作者：
Reisert, Marco
影响因子：
4.6
作者：
Pietrzak A;Marszałek A;Kunikowska J;Piotrowski T;Medak A;Pietrasz K;Wojtowicz J;Cholewiński W
通讯作者：
Cholewiński W