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基于扩散峰度成像的个体脑肿瘤侵袭图谱

Individual Brain Tumor Invasion Mapping Based on Diffusion Kurtosis Imaging.

作者信息

Pogosbekyan E L, Zakharova N E, Batalov A I, Shevchenko A M, Fadeeva L M, Bykanov A E, Tyurina A N, Chekhonin I V, Galstyan S A, Pitskhelauri D I, Pronin I N, Usachev D Yu

机构信息

Medical Physicist, Department of Neuroradiology; N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.

MD, DSc, Professor of the Russian Academy of Sciences, Chief Researcher, Department of Neuroradiology; N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, 16, 4th Tverskaya-Yamskaya St., Moscow, 125047, Russia.

出版信息

Sovrem Tekhnologii Med. 2025;17(1):81-90. doi: 10.17691/stm2025.17.1.08. Epub 2025 Feb 28.

Abstract

UNLABELLED

is to develop and implement an algorithm for image analysis in brain tumors (glioblastoma and metastasis) based on diffusion kurtosis MRI images (DKI) for the assessment of anisotropic changes in brain tissues in the directions from the tumor to the intact (as shown by the standard MRI data) white matter, which will enable generating individual tumor invasion maps.

MATERIALS AND METHODS

A healthy volunteer and two patients (one with glioblastoma and the other with a single metastasis of small cell lung cancer) were examined by DKI obtaining 12 parametric kurtosis maps for each participant.

RESULTS

During the investigation, we have developed an algorithm of DKI analysis and plotting the profile of tissue parameters in the direction from the tumor towards the unaffected white matter according to the data of standard MRI. Changes of the DKI indicators along the trajectories built using the proposed algorithm in the perifocal zone of glioblastoma and metastasis have been compared in this work. We obtained not only changes in the parameters (gradients in trajectory plots) but also a visual reflection (on color maps) of a known pathomorphology of the process - no significant gradients of DKI parameters were detected in the perifocal metastasis edema, since there was a pure vasogenic edema and no infiltrative component. In glioblastoma, gradients of DKI parameters were found not only in the zone of perifocal edema but beyond the zone of MR signal as well, which is believed to reflect diffusion disorders along the white matter fibers and different degrees of brain tissue infiltration by glioblastoma cells.

CONCLUSION

The developed algorithm of DKI analysis in brain tumors makes it possible to determine the degree of changes in the tissue microstructure in the perifocal zone of brain glioblastoma relative to the metastasis. The study aimed at obtaining individual maps of tumor invasion, which will be applied in planning neurosurgical and radiation treatment and for predicting directions of further growth of malignant gliomas.

摘要

未标注

目的是基于扩散峰度MRI图像(DKI)开发并实施一种用于脑肿瘤(胶质母细胞瘤和转移瘤)图像分析的算法,以评估从肿瘤到完整(如标准MRI数据所示)白质方向上脑组织的各向异性变化,从而生成个体肿瘤侵袭图。

材料与方法

一名健康志愿者和两名患者(一名患有胶质母细胞瘤,另一名患有小细胞肺癌单发转移瘤)接受了DKI检查,为每位参与者获取了12个参数化峰度图。

结果

在研究过程中,我们开发了一种DKI分析算法,并根据标准MRI数据绘制从肿瘤向未受影响白质方向的组织参数剖面图。在这项工作中,比较了胶质母细胞瘤和转移瘤灶周区域中使用所提出算法构建的轨迹上DKI指标的变化。我们不仅获得了参数变化(轨迹图中的梯度),还获得了该过程已知病理形态的视觉反映(在彩色图上)——在灶周转移瘤水肿中未检测到DKI参数的显著梯度,因为存在单纯的血管源性水肿且无浸润成分。在胶质母细胞瘤中,不仅在灶周水肿区域,而且在MR信号区域之外也发现了DKI参数的梯度,这被认为反映了沿白质纤维的扩散障碍以及胶质母细胞瘤细胞对脑组织的不同程度浸润。

结论

所开发的脑肿瘤DKI分析算法能够确定脑胶质母细胞瘤灶周区域相对于转移瘤的组织微观结构变化程度。该研究旨在获得个体肿瘤侵袭图,这将应用于神经外科手术和放射治疗规划以及预测恶性胶质瘤的进一步生长方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05aa/11892574/6654db9c1b8d/STM-17-1-08-g001.jpg

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