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堆叠平面内组织学用于定量验证无创成像生物标志物:在侵袭性脑肿瘤模型中的应用。

Stacked in-plane histology for quantitative validation of non-invasive imaging biomarkers: Application to an infiltrative brain tumour model.

机构信息

Glasgow Experimental MRI Centre, Institute of Neuroscience and Psychology, University of Glasgow, G61 1QH, UK; Department of Physics, College of Science, University of Misan, Iraq.

Centre for Clinical Brain Sciences, University of Edinburgh, EH16 4SB, UK.

出版信息

J Neurosci Methods. 2019 Oct 1;326:108372. doi: 10.1016/j.jneumeth.2019.108372. Epub 2019 Jul 23.

Abstract

BACKGROUND

While it is generally agreed that histopathology is the gold standard for assessing non-invasive imaging biomarkers, most validation has been by qualitative visual comparison. To date, the difficulties involved in accurately co-registering histology sections with imaging slices have prevented a voxel-by-voxel assessment of imaging modalities. By contrast with previous studies, which focus on improving the registration algorithms, we have taken the approach of improving the quality of the histological processing and analysis.

NEW METHOD

To account for imaging slice orientation and thickness, multiple histology sections were cut in the MR imaging plane and averaged to produce stacked in-plane histology (SIH) maps. When combined with intensity sensitive staining this approach gives histopathology maps, which can be used as the gold standard to validate imaging biomarkers.

RESULTS

We applied this pipeline to a patient-derived mouse model of glioblastoma multiforme (GBM). Increasing the number of stacked histology sections significantly increased SIH measured tumour volume. The SIH technique proposed here resulted in reduced variability of volume measurements and this allowed significant improvements in the quantitative volumetric assessment of multiple MRI modalities. Further, high quality registration enabled a voxel-wise comparison between MRI and histopathology maps. Previous approaches to the validation of imaging biomarkers with histology, have been either qualitative or of limited accuracy. Here we propose a pipeline that allows for a more accurate validation via co-registration with SIH maps, potentially allowing validation in a voxel-wise mode.

CONCLUSION

This work demonstrates that methodically produced SIH maps facilitate the quantitative histopathologic assessment of imaging biomarkers.

摘要

背景

虽然普遍认为组织病理学是评估非侵入性成像生物标志物的金标准,但大多数验证都是通过定性视觉比较进行的。迄今为止,由于难以准确地将组织学切片与成像切片配准,因此无法对成像方式进行体素级别的评估。与以前专注于改进配准算法的研究不同,我们采取了改进组织学处理和分析质量的方法。

新方法

为了考虑成像切片的方向和厚度,在 MR 成像平面中切割多个组织学切片,并对其进行平均以产生堆叠的平面内组织学(SIH)图。当与强度敏感的染色结合使用时,这种方法可以生成组织病理学图,可作为验证成像生物标志物的金标准。

结果

我们将该流程应用于胶质母细胞瘤多形性(GBM)的患者衍生小鼠模型中。增加堆叠的组织学切片数量可显著增加 SIH 测量的肿瘤体积。这里提出的 SIH 技术可减少体积测量的可变性,并可显著提高多种 MRI 模式的定量体积评估。此外,高质量的配准使我们能够在 MRI 和组织病理学图之间进行体素级比较。以前用于用组织学验证成像生物标志物的方法要么是定性的,要么是准确性有限的。在这里,我们提出了一种通过与 SIH 图配准来进行更准确验证的方法,从而可能以体素级模式进行验证。

结论

这项工作表明,有系统地生成的 SIH 图有助于对成像生物标志物进行定量组织病理学评估。

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