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多模态磁共振成像技术在胶质母细胞瘤与脑转移瘤诊断与鉴别诊断中的研究进展
郝之月 高阳 吴琼

Cite this article as: Hao ZY, Gao Y, Wu Q. Research progress of multimodal magnetic resonance imaging in the diagnosis and differential diagnosis of glioblastoma and brain metastases[J]. Chin J Magn Reson Imaging, 2022, 13(8): 125-129.本文引用格式:郝之月, 高阳, 吴琼. 多模态磁共振成像技术在胶质母细胞瘤与脑转移瘤诊断与鉴别诊断中的研究进展[J]. 磁共振成像, 2022, 13(8): 125-129. DOI:10.12015/issn.1674-8034.2022.08.028.


[摘要] 胶质母细胞瘤与脑转移瘤是两种中枢神经系统较为常见的恶性肿瘤,在常规影像序列中表现出相似的影像特征,因此无法实现准确鉴别,特别是对于缺乏病史支持的单发转移瘤,术前正确的鉴别诊断对于患者临床治疗方案的制订及预后的分析具有重要意义。多模态MRI在胶质母细胞瘤与脑转移瘤的鉴别诊断中体现出了较高的临床价值,但各MRI模型在鉴别两种病变时准确度与特异度不同,联合使用多种MRI模型可有效提高诊断效能。并且由于两种病变水肿形成机制的差异,瘤周水肿区的各参数在鉴别两种病变时诊断效能更高。本文就动态磁敏感对比增强灌注成像、动态对比增强MRI、扩散张量成像以及血氧水平依赖功能MRI等多模态MRI技术在胶质母细胞瘤与脑转移瘤鉴别诊断方面的研究进展予以综述,并扩展了一些未来可能用于解决这一临床问题的其他MRI模型,如平均表观传播扩散MRI、神经突定向扩散与密度成像以及扩散微结构成像等,以期为后续研究提供参考思路。
[Abstract] Glioblastoma and brain metastases are two common malignant diseases of the central nervous system. The two diseases show similar image features in conventional image sequences, and conventional image examination can not differentiate them accurately, especially for single metastasis without medical history support. Correct preoperative differential diagnosis is of great significance for the formulation of clinical treatment and the analysis of survival prognosis. Multimodal MRI has shown high clinical value in differentiating glioblastoma from brain metastases. However, the accuracy and specificity of each MRI model in differentiating the two lesions are different. The combined use of multiple MRI models can effectively improve the diagnostic efficiency. Due to the difference of edema formation mechanism between the two diseases, the parameters of peritumoral edema area have higher diagnostic efficiency in differentiating the two diseases. This paper reviews the research progress of multimodal MRI techniques such as dynamic susceptibility contrast, dynamic contrast enhanced, diffusion tensor imaging and blood oxygen level dependent functional MRI in the differential diagnosis of glioblastoma and brain metastases, and extends some other magnetic resonance models that may be used to solve this clinical problem in the future, such as a mean apparent propagator-MRI, neurite orientation dispersion and density imaging and diffusion microstructure imaging, in order to provide reference ideas for follow-up research.
[关键词] 多模态磁共振成像;灌注成像;扩散成像;胶质母细胞瘤;脑转移瘤
[Keywords] multimodal magnetic resonance imaging;perfusion imaging;diffusion magnetic resonance imaging;glioblastoma;brain metastasis

郝之月    高阳 *   吴琼   

内蒙古医科大学附属医院影像诊断科,呼和浩特 010050

高阳,E-mail:1390903990@qq.com

作者利益冲突声明:全体作者均声明无利益冲突。


基金项目: 内蒙古自治区科技计划项目 2019GG047
收稿日期:2022-04-08
接受日期:2022-06-02
中图分类号:R445.2  R730.264  R739.41 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.08.028
本文引用格式:郝之月, 高阳, 吴琼. 多模态磁共振成像技术在胶质母细胞瘤与脑转移瘤诊断与鉴别诊断中的研究进展[J]. 磁共振成像, 2022, 13(8): 125-129. DOI:10.12015/issn.1674-8034.2022.08.028

       胶质瘤是中枢神经系统最常见的原发性肿瘤,其中胶质母细胞瘤(glioblastoma, GBM)更是以生长速度快及侵袭性强等特点严重地影响了患者的生活质量和生存周期[1]。GBM在常规MR图像上常表现为不规则、花环样的明显强化,中心多有囊变坏死,伴大片瘤周水肿。但在某些特定情况下,这部分患者与脑转移瘤(brain metastases, BMs)患者的鉴别存在难点,如全身综合性评估后却无原发肿瘤发现者[2]。这两种病变往往对应着不同的临床治疗手段及生存预后,无创且准确的鉴别诊断具有重要意义。

       常规MRI已逐渐不能满足临床的实际需要。随着MRI技术的不断革新,磁共振灌注成像(perfusion weighted imaging, PWI)、扩散MRI(diffusion MRI, dMRI)等多种功能MRI(functional MRI, fMRI)技术有望提高GBM与BMs鉴别诊断的准确性。本文就多模态MRI在GBM与BMs鉴别诊断的应用价值进行综述。

1 GBM与BMs诊断与鉴别诊断的MRI研究现状

1.1 动态磁敏感对比增强灌注成像

       动态磁敏感对比(dynamic susceptibility contrast, DSC)增强灌注成像是诊断颅内占位最常用的灌注技术,经静脉注射钆对比剂作为示踪剂,监测对比剂在受检组织中的动态变化,并通过定量参数反映病变新生血管程度及血管通透性,进一步获取组织微循环的病理生理信息。

       Askaner等[3]研究发现,在肿瘤实质强化区,GBM与BMs之间的相对脑血容量(relative cerebral blood volume, rCBV)差异无统计学意义。Lee等[4]从GBM与BMs实质区的灌注曲线中推导出信号恢复百分比(percentage signal recovery, PSR)以鉴别两种病变,但差异仍无统计学意义。结果表明肿瘤实质区的各灌注参数难以有效鉴别两种疾病。这可能是因为两种病变的实质强化区均存在大量新生毛细血管,血管通透性也相似,因此多表现出类似的灌注模式。

       在进一步水肿分层的研究中,Aparici等[5]发现,GBM与BMs的rCBV与相对脑血流量(relative cerebral blood flow, rCBF)为鉴别两种病变提供了可靠依据,且GBM水肿区的各灌注参数呈梯度改变,这与She等[6]的研究结果一致。这一现象支持血管源性水肿与浸润性水肿假说[7]。即BMs多由于内部或周围的毛细血管渗漏形成单纯的血管源性水肿,而GBM的瘤周区因含有肿瘤细胞和肿瘤血管生成导致的高血管化区域,形成浸润性水肿。

       DSC无法捕获时间-信号强度曲线的动态信息,对脑肿瘤的准确诊断存在不足。Park等[8]将自动编码器应用于时间-信号强度曲线以获得代表性的时间模式,随后通过卷积神经网络学习这些模式,实现了GBM与BMs的有效鉴别。同期,有学者通过对时间-信号强度曲线中感兴趣区内所有体素进行分析,得出了类似的结论[9]

       关于灌注参数实用性的研究,各位学者持不同的看法。多位学者[10, 11]在GBM、BMs及原发性中枢神经系统淋巴瘤的鉴别研究中发现,瘤周区的相对信号恢复百分比(relative percentage signal recovery, rPSR)用于鉴别的准确性低于rCBV;这与Mangla等[12]的结果相矛盾。DSC参数的获取受多种因素的影响,血脑屏障的破坏会导致对比剂外渗到细胞外间隙,测量得到的rCBV并不可靠[13, 14]。预加载对比剂有助于减轻这些泄漏影响,但会影响PSR测量的准确性[15, 16]。DSC各参数鉴别的实用性值得深入探讨,提示未来还需更大规模的研究。

1.2 动态对比增强MRI

       与DSC不同,动态对比增强(dynamic contrast enhanced, DCE)-MRI不依赖于血脑屏障的完整性,且空间分辨率更高,对磁敏感伪影的敏感性也更低。DCE-MRI通过药代动力学模型定量获得组织的灌注参数与渗透参数,可无创地提供肿瘤血流动力学特征。

       Lu等[17]研究发现,肿瘤实质区的各参数在两种病变中差异无明显统计学意义,这与冯梦薇等[18]研究结果类似。然而在同期的研究中,Bazyar等[19]认为,实质区血浆体积分数(fractional volume of the intravascular compartment, Vp)平均值有助于区分GBM与BMs。两者结论的不一致可能是因为GBM与BMs的实质区通常伴有囊变坏死和血管增生,正常的实质微结构已发生扭曲,影响了参数的可靠性。

       Tupý等[20]进一步在瘤周区研究发现,GBM组与BMs组之间的容积转移常数(volume transfer constant, Ktrans)差异有统计学意义。Ktrans代表对比剂在血浆和细胞外血管外空间之间的转运速率,GBM中存在大量迂曲排列的小血管,使得血液流动缓慢,这可能造成了两种病变内对比剂转运速度的不同。多位学者[20, 21]分析时间-信号强度曲线下的初始面积(initial area under the contrast-uptake curve, iAUC)发现,不论在实质区还是瘤周区,曲线的形态学特征均可区分高血管性的GBM和低血管性BMs,并且与Ktrans值相比,iAUC能更好地反映肿瘤的药代动力学变化,这为DCE鉴别两种病变提供了新的研究思路。单一的DCE-MRI对于患者的精确诊断仍然不够,未来应结合多种手段来提高诊断效率,完善诊疗过程。

1.3 磁共振扩散张量成像

       磁共振扩散张量成像(diffusion tensor imaging, DTI)假设组织的水分子扩散呈高斯分布,利用扩散张量描述组织内各方向上水分子的扩散程度。常用的DTI参数各向异性分数(fractional anisotropy, FA)可反映水分子在扩散主方向上的扩散程度,平均扩散率(mean diffusivity, MD)反映体素内水分子扩散的平均程度,与组织内水含量的多少有关。

       既往研究发现[22, 23, 24],GBM与BMs实质区的FA及MD差异无统计学意义;但瘤周水肿区的FA与MD是鉴别两种病变的可靠依据。然而,有学者提出了不同的看法[25, 26],研究发现GBM肿瘤实质区的FA显著高于BMs,这可能是因为GBM的肿瘤细胞产生大量特异性细胞外基质,作为细胞粘附和迁移的成分,这些分子在细胞外基质中聚集,导致高度各向异性。Skogen等[24]在DTI的衍生参数上利用纹理分析对两种病变实现了有效鉴别,Samani等[27]基于深度学习的方法进一步佐证了这一结论。

       Holly等[28]首次利用DTI体积纤维束造影鉴别GBM和BMs,发现GBM瘤周水肿区的纤维束总数和束密度均显著高于BMs。转移性病变随血液入侵并取代部分白质纤维束,同时导致周围脑组织出现大量血管源性水肿,从而导致更严重的白质破坏,这可能是BMs水肿区的纤维束及束密度明显低于GBM的原因。但此研究仍存在一定局限,如入组的GBM患者的肿瘤体积均大于BMs患者,可能会导致束密度的变化。因此,对于DTI体积纤维束造影能否有效鉴别GBM与BMs还有待进一步研究。

1.4 血氧水平依赖fMRI

       血氧水平依赖fMRI(blood oxygen level dependent fMRI, BOLD-fMRI)利用内源性血红蛋白作为对比剂,基于神经活动时T2加权像的信号变化,显示大脑功能活动并获得脑组织的生理病理信息。

       Heynold等[29]对20位GBM患者以及13位BMs患者进行定量BOLD-fMRI以及灌注成像,并通过后处理软件计算得到感兴趣区的磁共振生理标记图。研究表明,在肿瘤的实质强化区,GBM的新生血管活性与氧代谢率更高,这与GBM的高度侵袭性以及高细胞密度有关[30]。在瘤周水肿区,GBM的微血管灌注、新生血管活性以及组织氧张力更高,这可能是因为GBM水肿区存在肿瘤细胞浸润和微血管增生以维持高代谢需求,而BMs水肿区为单纯的血管源性水肿,会对毛细血管形成局部压迫,导致灌注减低[31]。BOLD-fMRI的信号强度与神经系统的活动以及由此产生的血流变化有关。脑肿瘤的占位效应以及肿瘤生物学相关因素,如肿瘤内毛细血管密度、肿瘤内的动静脉分流以及肿瘤的氧摄取指数等都可能影响BOLD-fMRI的信号变化,进而对BOLD-fMRI在脑肿瘤的准确诊断与鉴别诊断产生影响[32]。目前关于BOLD-fMRI在GBM与BMs鉴别诊断中的研究较少,未来还需更大规模的前瞻性研究对此结果进行验证。

       以上各项研究表明,多模态MRI技术作为鉴别GBM与BMs的非侵入性检测方法为患者及临床医生选择治疗方案和评估预后提供了可靠依据。并且进一步发现,由于两种病变水肿形成机制的差异,瘤周水肿区的各参数在鉴别时诊断效能更高。基于fMRI的影像组学在鉴别GBM与BMs中展示出独特的优势。但仍存在一些问题有待解决,如手工绘制感兴趣区造成研究的主观性太强从而产生测量误差,未严格限制BMs原发病灶的组织类型等,提示在今后的研究中需规范研究过程,联合使用多种成像技术为两种疾病的鉴别诊断提供更加全面的信息。

2 GBM与BMs诊断与鉴别诊断的MRI研究进展

2.1 平均表观传播扩散MRI

       为获取更精细的纤维束成像,有学者提出了扩散频谱成像(diffusion spectrum imaging, DSI)[33]。平均表观传播扩散MRI(mean apparent propagator-MRI, MAP-MRI)是基于DSI推导出的一种新的空间数据采集分析模型,提供了多个定量参数,可进一步描述神经纤维的几何结构特征与空间走形规律[34]

       已有学者采用MAP-MRI在胶质瘤分级[35]、预测分子基因型[36]、评估胶质瘤诱发的运动性癫痫患者的皮质脊髓束结构完整性[37, 38]等多个方面进行了研究,但在胶质瘤诊断与鉴别诊断方面,相关研究还有待开展。Sun等[39]对40位弥漫性胶质瘤的患者进行MAP-MRI发现,随着胶质瘤级别升高,q-空间逆方差(q-space inverse variance, QIV)和均方位移(mean squared displacement, MSD)呈下降趋势,而返回轴概率(return-to-axis probabilities, RTAP)和返回原点概率(return-to-origin probability, RTOP)呈升高趋势。胶质瘤的微观结构随着肿瘤级别的增加变得更加复杂,细胞密度更高、核异型性明显,肿瘤新生血管、囊变坏死及出血更加常见。QIV对于扩散受限的组织成分变化程度较为敏感,因此更高级别的胶质瘤的QIV较低。RTOP代表水分子在实验扩散过程中按时回到原点的距离,RTAP与返回平面概率(return-to-plane probabilities, RTPP)分别为水分子回到代表主要扩散方向的轴向与径向的概率。RTAP和RTOP随着肿瘤级别的增加而增加,这表明恶性程度更高的胶质瘤中的水分子更有可能回到起始位置,扩散更受限。然而RTPP在不同级别的胶质瘤中没有显著差异,这可能与轴突直径、髓鞘的堆积和数量有关[40]。同时他们发现,在异柠檬酸脱氢酶(isocitrate dehydrogenase, IDH)野生型胶质瘤中,MSD显著降低,而RTOP、RTAP和RTPP显著升高。这可能是因为IDH野生型胶质瘤的肿瘤新生血管明显,肿瘤实体部分细胞密度更高,因而具有更强的侵袭性,扩散受限更明显[41, 42]。有学者[43]利用五种扩散成像技术鉴别GBM与BMs发现,MAP-MRI的最佳鉴别参数为肿瘤实质区的MSD,但并非五种扩散技术中诊断效能最高的参数。

       MAP-MRI需要较长的采集时间才能获得足够的三维扩散空间样本,同步多层切片技术(simultaneous multi-slice, SMS)通过同时激励多个切片并使用数学模型分离混叠切片可缩短数据采集时间,但该数学模型可能导致MAP-MRI的参数估计产生偏差,在一定程度上限制了其在临床工作中的应用。MAP-MRI能否有效鉴别GBM与BMs未来还需更多研究进行佐证。

2.2 神经突定向扩散与密度成像

       微结构成像被认为主要由两种理论模型构成,即分别以DSI及神经突定向扩散与密度成像(neurite orientation dispersion and density imaging, NODDI)为代表的信号模型与隔室模型。信号模型是将每个体素视为单一隔室,在dMRI毫米级的成像分辨率上进行模型重建,以获得微米级的微观结构特征,但其模型指标仅代表由信号特征推测出的组织特征,并无实际生理意义;而隔室模型是将体素中的信号视为多个隔室共同作用的结果,通过对目标组织进行生物物理建模,直接获取组织微观结构特征与dMRI信号之间的关联,即每个隔室对应于不同细胞成分的扩散情况。

       2012年Zhang等[44]提出了NODDI,这是一种包括神经突内、神经突外和脑脊液三种隔室的微观结构模型。三个隔室中水分子扩散互不影响,经后处理可获得独立的定量MRI参数:神经突内体积分数(intracellular volume fraction, Vic)、神经突外体积分数(entracellular volume fraction, Vec)及脑脊液体积分数(isotropic volume fraction, Viso),参数间的关系:Vic+Vec+Viso=1。NODDI可同时描述灰质与白质内的微观结构特征,并且将脑脊液作为单独的隔室分离出来,有效提高了参数的生理特异性[45]

       到目前为止,运用NODDI技术鉴别GBM与BMs的研究较少。Kadota等[46]在两种病变的肿瘤实质区进行NODDI扫描,未发现任何参数的差异,这与Mao等[43]结论相悖。进一步在瘤周区发现,GBM的Vec显著高于BMs,推测Vec图上的高信号与沿纤维束侵入的肿瘤细胞有关。NODDI在描述含有高度分散和交叉的轴突的结构时精确度不高,这可能导致了各研究结果的差异。现有微结构成像模型的不足促进了dMRI技术向更好的成像质量以及更加简便高效的数据采集性能方向发展。

2.3 扩散微结构成像

       灰质与白质中存在大量神经轴突以及树突,这些结构介于细胞等微观结构以及灰白质等宏观结构之间,称为介观结构。现有的扩散模型多致力于获得清晰的介观结构以评估微观结构特征,但受到采集时间冗长、模型稳健性差等因素影响,在临床应用中存在障碍。现有学者提出扩散微结构成像(diffusion microstructure imaging, DMI)[47],可以将微观结构特性从介观结构的影响中分离出来,避免重建介观结构。DMI是类似于NODDI的一种三室模型,它利用贝叶斯估计器的监督机器学习代替原有的经典拟合方式,提高了模型定量估计的稳健性以及成像速度。

       DMI已用于多种白质病变的诊断,有研究表明,DMI在研究特发性正常压力脑积水患者脑室周围白质的改变[48]和颞叶癫痫[49]时存在重要意义。Würtemberger等[50]使用DMI对19例GBM和17例BMs患者进行检查,在瘤周水肿区测量发现,与GBM相比,BMs的自由水分数(free water/CSF volume fraction, V-CSF)显著增加,轴突内体积分数(intra-axonal volume fraction, V-intra)和轴突外体积分数(extra-axonal volume fraction, V-extra)显著降低。参数的改变符合组织病理学关于两种病变瘤周水肿的研究[51]

       DMI在GBM与BMs的鉴别诊断中显示出较好的诊断效能,但尚未有研究将该模型与其他扩散模型进行比较,对于其在临床中的应用价值尚不明确。

3 小结与展望

       综上所述,多模态MRI技术可用于鉴别GBM与BMs,且往往联合使用展现出更高的诊断效能。但目前的各种fMRI在脑肿瘤的准确诊断与鉴别诊断上还存在一定的不足,如各研究机构的机器与扫描参数无统一标准,各项研究纳入的总体病例数相对较少等。随着更多相对完善的模型不断问世,新的尝试与探索可能会展现出更好的临床应用价值。结合fMRI的影像组学在鉴别GBM与BMs时体现出了较好的发展前景,但相关研究较少,对于在常规临床工作中的应用存在一定挑战。

       IDH的突变与GBM患者的治疗方案、生存预后有紧密联系。目前,仅个别研究在GBM与BMs鉴别诊断中考虑到GBM的基因型突变的影响,这提示未来的研究需对GBM患者的来源进一步细分,提高研究的可靠性。此外,部分癌症患者可能罹患与原发病灶无关的新的恶性肿瘤,此时在颅内发现的BMs并不能明确转移来源,无法实现精准治疗而影响患者生存期。不同来源的BMs在常规MRI序列上往往表现出类似的影像特征,提示未来多模态MRI可在预测多发癌症患者脑转移的组织来源投入更多研究,为临床工作提供便利。

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