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Journal of Central South University Medical Sciences logoLink to Journal of Central South University Medical Sciences
. 2021 Apr 28;46(4):414–420. [Article in Chinese] doi: 10.11817/j.issn.1672-7347.2021.200316

定量功能磁共振成像技术在前列腺癌中的临床应用及进展

Clinical application and progress of quantitative functional magnetic resonance imaging in prostate cancer

LI Mengsi 1,2, LI Wenzheng 1,
Editor: 傅 希文
PMCID: PMC10930317  PMID: 33967089

Abstract

Magnetic resonance imaging (MRI) is a very important imaging method for diagnosis and treatment of prostate cancer (PCa) in clinical practice. As functional MRI is growing and maturing, its quantitative parameters are expected to enhance the clinical value of MRI furtherly. Intravoxel incoherent motion diffusion imaging, diffusion tensor imaging, and diffusion kurtosis imaging, which were derived from diffusion weighted imaging, have provided richer and more accurate parameters. The newly-developed magnetic resonance elastography can complement the mechanical characteristics of PCa.

Keywords: prostate cancer, diffusion weighted imaging, intravoxel incoherent motion diffusion imaging, diffusion tensor imaging, diffusion kurtosis imaging, dynamic contrast enhanced magnetic resonance imaging, magnetic resonance spectroscopy, magnetic resonance elastography


前列腺癌(prostate cancer,PCa)是许多国家男性中最常见的癌症,最新的全球癌症统计研究显示PCa的发病率自2007年以来增加了42%[1]。多参数磁共振成像(magnetic resonance imaging,MRI)技术在PCa的诊疗过程中占据非常重要的地位[2],功能MRI作为多参数MRI技术的重要内容,其提供的肿瘤微观变化的信息在PCa检出、诊断与鉴别诊断、侵袭性评估、治疗疗效及复发监测等方面均具有重要价值。

1. 扩散加权成像

扩散加权成像(diffusion weighted imaging,DWI)是前列腺功能MRI中应用最为成熟的技术之一,其通过检测活体组织内水分子的布朗运动,反映水分子在组织中的运动情况,进而分析组织的内部结构。作为前列腺影像报告和数据系统的主序列之一,它在PCa的检出和定位方面已经得到广泛的认可,其主要参数是表观扩散系数(apparent diffusion coefficient,ADC)。

ADC可以较好地区分PCa与前列腺良性病变[3-5],但是在区分移行带PCa与移行带基质增生结节中的效果欠佳,因为后者致密的基质成分多、腺体成分少,也容易引起水分子扩散受限。应用ADC直方图分析可改善此类情况。Liu等[6]通过直方图分析33例移行带PCa与29例基质增生病变,发现第10个百分位的ADC值鉴别移行带PCa与基质增生结节有最大的曲线下面积(area under curve,AUC)(0.87),与ADC平均值的AUC(0.83)相比,差异有统计学意义(P<0.05)。

DWI已经被证明是评估肿瘤侵袭性的有效技术。一项包括13项研究、共1 107个PCa灶的荟萃分析显示:ADC区分低、高级别PCa的总体敏感性为76.9%,总体特异性为77.0%[7]。病理上采用Gleason评分(Gleason score,GS)评价PCa侵袭性,已经证明ADC与GS呈负相关,随着PCa病理等级增加ADC下降[8]。然而,相同GS或相近GS的PCa由于ADC重叠而使术前非侵入性鉴别困难,而GS 4+3与GS 3+4、GS 3+3与GS 3+4(GS评分系统把前列腺癌组织分为主要分级区和次要分级区,每区按5级评分,1~5代表组织分化由好到差)的PCa关乎不同治疗方法的选择与预后,最近研究[9-10]发现ADC比率(肿瘤组织与正常组织的ADC比值)是解决上述问题的一个方向。Alessandrino等[9]回顾性分析了76个得分为GS 3+4和43个得分为GS 4+3的PCa病灶,发现两者之间的ADC比率可以将两者较好地鉴别。Wu等[10]研究发现ADC比率与GS呈负相关,在区分GS 3+3与GS 3+4的PCa的性能上最好。此外,ADC也被普遍用于监测肿瘤治疗效果。放疗期间及放射治疗后肿瘤细胞的死亡及微环境的改变,会导致ADC升高,ADC可应用于监测放射治疗的疗效[11]。同样地,当内分泌治疗后诱导肿瘤细胞毒性反应,使癌区的水分子受限情况减轻,这为ADC评估内分泌治疗的疗效奠定了基础[12]

2. 体素内不相干运动成像

在DWI中,ADC所代表的水分子扩散包括纯水分子扩散和毛细血管微循环灌注相关的伪扩散。体素内不相干运动成像(intravoxel incoherent motion,IVIM)作为在DWI基础上延伸出的一种双指数函数模型,通过对多个b值拟合可得到纯扩散系数(diffusion coefficient,D)、伪扩散系数(pseudo diffusion coefficient,D*)和灌注分数(perfusion fraction,f),分别代表真实水分子扩散、微循环灌注情况和灌注过程在整体扩散过程所占的比例[13]。与ADC类似,已经有多项IVIM研究表明D值在PCa的诊断与鉴别诊断、评估病理分级方面价值良好[14-19]

与单指数DWI模型相比,Zhang等[19]对48例PCa进行直方图分析发现D与GS相对于ADC表现出更好的相关性,而且对PCa分层的性能优于ADC;而Bao等[20]同样应用全肿瘤直方图分析移行带PCa,却发现ADC与GS的相关性高于D;这可能与两者的研究选用不同的b值以及PCa发生在不同的区域有关。但是IVIM的灌注参数(D*和f)应用在诊断及PCa风险评估中的价值目前仍存在较大的争议。限制IVIM在临床PCa中进一步应用的一个主要原因是灌注参数的可重复性差,选取合适范围的b值大小、个数和使用新的拟合算法可能改善这一情况。最近有研究[21]表明:在IVIM的标准双指数模型上结合全变差惩罚函数,使用8个b值可以改善IVIM的灌注图像质量,但该研究只使用了6个PCa作为样本,其结果需要进一步的验证。由于IVIM能在不注射对比剂的情况下反映组织灌注信息,对于不适宜使用动态增强的病人,IVIM仍然是较佳的备选序列。

IVIM可将水分子扩散与灌注区分开来的理论为PCa的乏氧成像提供了新的方向。Hompland等[22]发现利用IVIM信号生成的弥散系数与血容量分数图像可以整合生成氧气消耗-供应图像,其得到的低氧分数与缺氧标志物哌莫硝唑密切相关。受此研究的启发,Chen等[23]发现由IVIM衍生的缺氧指标可作为提高PCa风险分层性能的有利补充。由于缺氧在肿瘤的发生、发展中至关重要,IVIM探测的缺氧信息将为精准预测PCa的侵袭性提供更多信息。

3. 扩散张量成像

在具有固定排列顺序的组织结构中,水分子在各个方向的弥散是不同的,单指数DWI技术忽视了水分子扩散程度存在异质性,因此在DWI技术的基础上提出了扩散张量成像(diffusion tensor imaging,DTI)技术,主要用于量化水分子运动的各项异性[24]。各项异性分数(fractional anisotropy,FA)与ADC是DTI临床应用的两个主要参数,前者表示水分子在组织内扩散的各向异性程度,ADC则反应水分子扩散快慢。另外,通过对各体素水分子扩散信息进行处理可得到纤维示踪图(fiber tractography,FT),可以显示前列腺周围神经的走行[25]

在DTI的研究中,ADC在PCa的诊断与鉴别诊断及评估PCa侵袭性的价值方面已经得到许多研究[26-29]的支持,而FA的价值仍需要更多的研究去验证。多数研究[30-32]认为PCa的FA高于非癌组织,因为通常PCa的异质性比非癌组织大。Tian等[29]在50例PCa中发现FA与GS呈正相关(r=0.884,P<0.05),可能的原因是因为高级别PCa中细胞膜及细胞内黏度增加、细胞外空间显著减少,从而导致了扩散各向异性增加。一项分析DT1特征与病理上肿瘤组织的组成成分定量的相关性研究指出:FA与间质呈负相关,而高级别PCa中肿瘤间质通常较少[33],也支持FA随PCa分级增加而增加的观点。然而,也有研究[34]表明FA与GS没有相关性,FA与GS相关性的差异可能与扩散方向的数量及DTI图像的信噪比有关[35-36]。DTI的衍生参数也是前列腺DTI的研究热点。一项新的DTI研究[30]提取了20个新的DTI参数,发现体积扩散、相对各向异性等多个参数能很好地表征PCa,并能用于评估PCa的侵袭性。

由于FT可以独特地显示神经血管束,DTI在临床PCa周围神经保留手术中的价值备受关注。Siracusano等[37]的研究发现:PCa根治术前后其周围神经血管束数量存在显著差异,术后并发症勃起功能障碍得分与FA呈负相关。该研究表明DTI在开展PCa周围神经保留手术中具有重要的指导意义。

4. 扩散峰度成像

常规DWI及DTI模型中假定自由水分子的扩散遵循高斯分布,但实际上由于生物组织结构的复杂性,水分子的扩散会不同程度地偏离高斯分布。扩散峰度成像(diffusion kurtosis imaging,DKI)技术是DTI技术的进一步延伸,用于测量水分子的非高斯分布的扩散,其参数扩散峰度(kurtosis,K)用于反映扩散的不均匀性,即与高斯分布的偏差,代表组织复杂性;参数D是指经非高斯分布矫正过的ADC[38]。DKI的荟萃分析[39]显示:K和D在区分良恶性疾病方面均具有良好的性能,在前列腺的研究中亦如此。PCa的K均高于非癌组织,D低于非癌组织[40-45],且参数的可重复性好。另外,已经有多项研究[41-42, 44-45]证实K和D在PCa风险分层中的价值。

但与传统DWI技术相比,DKI技术是否为PCa的诊断和分级增加了明显的价值尚无定论。一项包括285名PCa患者的回顾性研究显示:K与ADC呈高度负相关(r=-0.82,P<0.001),且K在鉴别前列腺良恶性疾病、GS≤3+3与GS≥3+4及GS≤3+4与GS≥4+3的PCa的效能方面并没有优于ADC,认为DKI在PCa的检测上与传统DWI相比并没有附加价值[44]。Wang等[45]通过对110例PCa进行全肿瘤直方图分析发现:K的第90个百分位数在区分低级别与高级别PCa时比第10个百分位的ADC具有更高的AUC与特异性。DKI与DWI价值的差异可能与b值选取、影像图像处理方法有关。相对于常规DWI,DKI可以探测组织中水分子更为真实的扩散情况,其参数K可以反映组织微结构的异质性和复杂性,但DKI需要付出额外的时间成本,因此验证或挖掘DKI超越时间成本的临床价值或缩短其扫描时间是现阶段及未来致力推广DKI临床应用的方向。

5. 动态增强MRI

动态增强MRI(dynamic contrast enhanced MRI,DCE-MRI)也是临床上应用最为广泛的功能MRI技术之一,其定量参数主要包括容积转移常数(volume transfer constant,Ktrans)、回流速率常数(Kep)以及细胞外血管外间隙体积分数(fractional volume of extravascular extracellular space,Ve),以此反映组织血管通透性及微循环血流灌注情况。

应用DCE-MRI的定量参数可明显提高PCa的检出率。Wei等[46]发现通过添加DCE-MRI的参数,第2版前列腺影像报告和数据系统(Prostate Imaging-Report And Data System,PI-RADS)检出PCa的灵敏度由56.6%提升到了89%以上。目前关于DCE-MRI定量参数评估PCa侵袭性的价值结论各异,可能与不同级别PCa的血供重叠、研究样本量大小、DCE-MRI参数不一致和研究中对PCa风险分层使用的分类不一致有关。PCa的发生、发展与新生血管形成密不可分,DCE-MRI作为能反映组织灌注微观信息的功能MRI技术,虽然在单独使用时评价PCa侵袭性的效用不统一,但与DWI联合使用可以提高目前DWI在PCa风险分层中的价值。Hotker 等[47]研究发现:与单独的ADC相比,添加Ktrans后DWI在区分GS≤3+4与GS≥4+3、GS≤4+3与GS≥4+4的PCa的性能方面有显著提升。

Ktrans和Kep通过反映PCa治疗后肿瘤微循环减少/增加在疗效评估及预测复发中具有重要价值。Wu等[48]研究表明:Ktrans和Kep值在有效放射治疗后明显下降。Akin等[49]研究发现在放射治疗后局部复发的PCa患者中,其Ktrans和Kep值与活检阴性患者相比明显增加。

6. MRI波谱成像

MRI波谱成像(magnetic resonance spectroscopy,MRS)是一项非侵入性检测活体组织代谢的功能MRI技术。正常的前列腺腺上皮细胞分泌并储存大量的枸橼酸盐(citrate,Cit),当发生癌变时腺上皮被癌细胞取代,因此PCa的Cit水平明显降低,同时癌细胞增殖导致了磷脂代谢的改变,胆碱(choline,Cho)化合物增加,而前列腺正常组织与癌组织的肌酸(creatine,Cr)水平没有明显变化[50]。上述PCa的生物化学改变为MRS的应用奠定了基础。目前MRS在PCa研究中广泛应用(Cho+Cr)/Cit即CC/C作为代谢指标。

MRS在外周带PCa与前列腺炎的鉴别上可作为辅助序列提供一定的参考价值。Zabihzadeh等[51]发现:PCa的CC/C明显高于前列腺炎(P<0.01),CC/C区分二者的敏感性、特异性分别为94.4%和80%。而Zhang等[52]对比43例单侧外周带PCa与35例单侧外周带前列腺炎的CC/C,发现二者之间的差异并没有统计学意义,但前列腺炎的Cho峰代谢没有变化,以此可作为与PCa的鉴别点。多项研究[51, 53-55]表明MRS可以用于评估PCa的侵袭性。Nagarajan等[55]研究发现:CC/C与GS呈明显正相关(r=0.90,P<0.001)。Leap-man等[56]发现第2版PI-RADS检测高级别PCa敏感性高而特异性低,结合MRS后其特异性提升了50%,表明结合MRS可明显提高其预测高级别PCa的能力。MRS预测PCa治疗后复发的价值尚需更多的研究。Westphalen等[57]报道:与单独T2加权像(T2 weighted imaging,T2WI)对比,结合MRS可以预测PCa放射治疗后的局部复发;但外照射后也会引起一些良性组织的Cho增高,因此如何区分放射治疗后的良恶性组织值得探讨。总之,MRS作为PCa检查曾经广泛使用的技术,虽因检查时间长现在已退至辅助序列,但作为目前临床上唯一能够表征肿瘤生物化学信息的技术,根据需求进行综合应用能更好地服务于PCa的诊疗。另外,影像代谢组学的兴起或许有助于挖掘MRS更多、更深入的价值[58]

7. MRI弹性成像

MRI弹性成像(magnetic resonance elastography,MRE)是一种基于MRI的新型功能MRI技术,于1995年由Muthupillai等[59]Science报道。PCa组织由于肿瘤细胞及肿瘤微血管的显著增加而变硬,同时癌灶周围正常组织因被侵袭损伤发生的类似修复的间质反应可导致胶原沉积增加,进一步加大了PCa的硬度,因此硬度是PCa诊断和病理分级重要的生物标志物之一[60]。硬度在MRE中常用弹性值代替。

由于前列腺本身解剖特征不利于MRE高分辨成像,近年来MRE在前列腺中的研究聚焦于MRE成像技术的选择,临床应用很少且规模很小,但整体肯定MRE在PCa诊断和侵袭性评估中的价值。Li等[61] 在10例前列腺炎与8例PCa患者中采取经耻骨联合途径成像的方式证实:PCa的平均弹性明显高于非癌组织,弹性值可以区分PCa与前列腺炎,且与GS呈明显正相关(r=0.913,P<0.01),但MRE的图像分辨率差。Arani等[62]采用经直肠成像使前列腺MRE图像质量有了较大的提升,研究纳入12名志愿者与1例PCa患者,PCa图像显示癌灶与高硬度区域一致,尽管志愿者对直肠腔内MRE成像耐受性尚可,但直肠MRE成像的侵入性不利于临床大规模开展。Sahebjavaher等[63]采用液压装置经会阴成像的方式实现了高分辨且无创的MRE成像,研究发现MRE诊断PCa的能力与ADC相当,联合MRE与DWI诊断PCa,可使相应的AUC提高至0.78~0.82。然而,经会阴成像的MRE准备工作复杂,将延长前列腺MRI检查时间。Dittmann等[64]最近开发了空气驱动装置的多频三维MRE,实现了高效、便捷、高分辨率、非侵入性的前列腺MRE成像,其可行性已经在健康志愿者及患者身上得到了证实。多频三维MRE用剪切波速度表征硬度,除此之外,黏度参数图还可得到衍生参数φ以反映组织流动性,进一步完善了肿瘤的机械特征[65]。MRE技术在前列腺的临床应用中目前尚处于起步阶段,但其提供的肿瘤机械信息有助于多参数MRI技术多方位地认识肿瘤,可以预见多频三维高频分辨率MRE在PCa研究中拥有广阔的应用前景。

8. 结 语

DWI与DCE-MRI作为临床上广泛应用的序列,贯穿PCa诊疗全程。IVIM、DTI及DKI作为在DWI基础上延伸的新技术,目前主要集中在验证其临床价值、评估并解决参数稳定性及开展新参数研究3个方面,此3种技术临床效用虽然暂时并未明显优于DWI序列,但其探测的肿瘤信息较DWI更为丰富。MRS、MRE从不同于DWI系列技术的角度提供了肿瘤的生物化学、机械特征信息,有助于综合提升对肿瘤发生、发展的认识。随着人工智能时代的到来,定量功能MRI技术蕴含的丰富信息将在未来进一步被挖掘,并能进一步应用于临床服务,改善临床诊疗的质量。

基金资助

湖南省科技厅计划项目(2018XK2304)。

This work was supported by the Plan Project of Hunan Science and Technology Department, China (2018XK2304).

利益冲突声明

作者声称无任何利益冲突。

原文网址

http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/202104414.pdf

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