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综述
MRI评估直肠癌新辅助放化疗后肿瘤反应的研究进展
刘丹 张胜潮

Cite this article as: Liu D, Zhang SC. Progress of MRI in predicting of the tumor response after neoadjuvant chemoradiotherapy for rectal cancer[J]. Chin J Magn Reson Imaging, 2022, 13(9): 163-166.本文引用格式:刘丹, 张胜潮. MRI评估直肠癌新辅助放化疗后肿瘤反应的研究进展[J]. 磁共振成像, 2022, 13(9): 163-166. DOI:10.12015/issn.1674-8034.2022.09.039.


[摘要] 局部晚期直肠癌患者无法直接切除病灶,在实施新辅助放化疗(neoadjuvant chemoradiotherapy, nCRT)后,部分人群反应较敏感,会出现完全的肿瘤反应,因此,对于此类患者局部切除或“观察等待”疗法有望取代手术切除,从而可以保留患者肛门和避免不必要的手术并发症。因而需要一种无创且可靠的评估方法判断nCRT后的肿瘤反应。MRI在直肠癌的初次分期和重新评估肿瘤对nCRT的反应方面起着至关重要的作用。目前评估手段主要有常规MRI、功能MRI(functional magnetic resonance imaging, fMRI)以及基于MRI的人工智能预测模型。本文就以上三种评估方式在预测局部晚期直肠癌nCRT后肿瘤反应的研究进展进行综合阐述。
[Abstract] Because patients with locally advanced rectal cancer cannot directly remove the lesions, after neoadjuvant chemoradiotherapy (nCRT), some people have a more sensitive response, and a complete tumor response will occur. Therefore, local resection or "watch and wait" approach is expected to replace surgical resection for such patients, thereby preserving the patient's anus and avoiding unnecessary surgical complications. Therefore, a noninvasive and reliable evaluation method is needed to determine tumor response after nCRT. MRI plays a crucial role in the initial staging of rectal cancer and in reassessing tumor response to nCRT. At present, the evaluation methods mainly include conventional MRI, functional MRI and MRI based on artificial intelligence prediction model. This paper comprehensively elaborated the research progress of the above three evaluation methods in predicting tumor response after neoadjuvant therapy for locally advanced rectal cancer.
[关键词] 直肠癌;新辅助放化疗;肿瘤反应;病理学完全缓解;磁共振成像;功能磁共振成像;人工智能预测模型;综述
[Keywords] rectal cancer;neoadjuvant chemoradiotherapy;tumor response;pathological complete response;magnetic resonance imaging;functional magnetic resonance imaging;artificial intelligence prediction model;review

刘丹 1   张胜潮 2*  

1 山西医科大学,太原 030001

2 太原市第二人民医院磁共振室,太原 030002

*张胜潮,E-mail:tyzsc163@qq.com

作者利益冲突声明:全部作者均声明无利益冲突。


收稿日期:2022-05-05
接受日期:2022-08-26
中图分类号:R445.2  R735.37 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.09.039
本文引用格式:刘丹, 张胜潮. MRI评估直肠癌新辅助放化疗后肿瘤反应的研究进展[J]. 磁共振成像, 2022, 13(9): 163-166. DOI:10.12015/issn.1674-8034.2022.09.039

       结直肠癌是最常见的消化道恶性肿瘤之一,其发病率逐年上升,是第二位致死癌症[1]。其中,70%是局部晚期直肠癌(cT3-4Nx或者cTxN+)[2]。新辅助放化疗(neoadjuvant chemoradiotherapy, nCRT)后继以全直肠系膜切除术(total mesorectal excision, TME)已成为局部晚期直肠癌患者的标准治疗方法[3],术前放化疗可使瘤体退缩,降低肿瘤分期;减少局部肿瘤复发风险,改善患者生存质量[4]。但不同患者nCRT后肿瘤反应不同,一部分表现出对nCRT的耐药性,另约10%~30%的患者可能出现病理学完全缓解(pathological complete response, pCR)[5]。如果对pCR患者实施TME术式可能会导致严重并发症,甚至增加发生吻合口瘘的风险[6]。因此,提前预测nCRT疗效,有助于为患者选择更合适的治疗方案。常规MRI能多平面、多序列、多方位成像,可观察直肠癌病灶本身的特点及其对周围结构的侵犯情况[7]。功能MRI(functional MRI, fMRI)通过参数体现组织水分子扩散或血流灌注情况,间接反映组织成分的变化,有助于区分残余肿瘤和纤维化,从而提高MRI识别肿瘤反应的能力[8]。基于人工智能的新技术,如影像组学和深度学习,显示了基于MRI衍生参数的巨大潜力,能够提取肉眼无法识别的组织微观异质性[9]。以下对目前MRI评估nCRT后肿瘤反应的研究现状进行阐述。

1 常规MRI

       常规MRI是目前直肠癌诊疗首选的检查手段之一,可准确显示肿瘤位置、浸润深度以及壁外血管、环周切缘、淋巴结受累情况。直肠癌作为一种实体瘤,可通过肿瘤体积的减小作为治疗反应的标志,如实体瘤反应评估标准。Lutsyk等[10]借助正电子发射计算机体层成像(positron emission computed tomography, PET)-MRI逐层勾画肿瘤体积预测pCR效果较好。Liu等[11]也证明了原发肿瘤最大体积(primary gross tumor volume, pGTV)不仅是重要的预后指标,而且较小的pGTV是肿瘤反应良好的独立预测因素。但当肿瘤表现为边缘不规则或呈浸润性生长时,难以准确勾画肿瘤轮廓,会导致误差出现,因此该指标具有一定的局限性。除了分析肿瘤大小,根据MRI的肿瘤消退程度(MRI assessment of tumor regression grading, mrTRG)亦可以判断nCRT的疗效,其原理是评估T2WI上肿瘤大小和信号强度变化,当肿瘤体积明显缩小、信号强度降低呈低信号,即病灶表现为纤维化且无肿瘤信号残余时,认定直肠癌患者达到pCR状态[12]。mrTRG 1~2级意味着nCRT后肿瘤反应好,基本无肿瘤细胞残留,mrTRG 3~5级代表着疗效差,肿瘤基本无消退。Jang等[13]研究表明,mrTRG 1级对诊断pCR有较高的特异度,但敏感度较低。Achilli等[14]研究也显示,预测肿瘤完全反应mrTRG与病理退缩分级(pathological tumor regression grade, pTRG)一致性较差,准确度仅为41%。原因可能是仅依靠T2WI难以区分残余肿瘤与肿瘤治疗后的变化(肿瘤坏死、纤维化或黏蛋白产生)[15];大量反应性增生纤维可能会包绕小的肿瘤细胞,使残余活肿瘤的识别变得更加困难。

       如上所述,常规MRI能较直观显示病灶大体形态及信号的改变,一定程度上反映nCRT后肿瘤的变化,但对于治疗后一些具体细节的展示仍旧欠佳,尤其是在残余肿瘤的鉴别方面有一定难度。因此能在细胞水平上更好地反映肿瘤生理和生物学特征的fMRI被用于直肠癌治疗后疗效的评估。

2 fMRI

2.1 扩散加权成像

       扩散加权成像(diffusion weighted imaging, DWI)基于水分子的扩散特性,可以提供细胞密度、细胞膜完整性等病理生理改变信息[16]。有研究[17, 18, 19]表明,常规MRI结合DWI,能提高MRI对肿瘤反应的评估能力。nCRT后肿瘤细胞会出现坏死、纤维化,其扩散受限程度下降,表现为类似于固有肌层的低信号,区别于存活肿瘤细胞的稍高信号,因此DWI有助于识别残余肿瘤和部分治疗反应。Yuan等[20]发现,借助DWI勾画肿瘤体积,能清楚描绘nCRT前后肿瘤的边界,较好识别良好反应和不良反应。Bates等[21]认为DWI较高的b值可能会增加nCRT后残留肿瘤检测的敏感性。nCRT后病灶和正常肠壁之间的对比度会下降,增加了识别肿瘤反应的难度。小视野(reduced-field of views, rFOV)DWI由于技术上的更新,可有效减少伪影,获得更高清晰度和分辨率的图像,能更清晰地描绘小病灶,从而更清楚地揭示nCRT后直肠癌的细节。Jang等[22]研究表明在识别pCR方面,rFOV DWI较常规DWI能提供更高的诊断准确性。表观扩散系数(apparent diffusion coefficient, ADC)是DWI的定量参数,ADC值与肿瘤细胞数量成反比,与细胞外空间成正比,nCRT后肿瘤细胞坏死和细胞外间隙增加,将导致ADC值增加。Zhao等[23]的研究显示治疗前ADC值和ADC值变化百分比是接受nCRT后肿瘤反应的潜在预测因子。最新一项Meta分析[24]亦证实,ADC值变化百分比能可靠地评估直肠癌患者nCRT后的pCR。上述研究[23, 24]均认为治疗前ADC值较低,nCRT后ADC值将升高。而Xu等[25]的研究表明pCR组和非pCR组治疗前、后的ADC值无差异。可能是由于DWI没有考虑血流灌注情况对扩散的影响,难以区分真实的水分子扩散,因而导致了研究结果的差异。

2.2 体素内不相干运动成像

       体素内不相干运动(intravoxel incoherent motion, IVIM)成像是DWI的衍生序列,可区分水分子的纯扩散运动和灌注相关运动,较DWI能更准确地反映组织微观环境,体现出毛细血管网络中的微循环灌注情况[26]。IVIM的定量参数包括真扩散系数(D)、假扩散系数(D*)和灌注分数(f)。D代表真实的组织细胞结构和扩散;D*和f代表微血管灌注,反映了微循环灌注引起的假扩散比例。nCRT后癌灶内扩散相关微环境与残余肿瘤状态明显相关,pCR组较非pCR组治疗后D值和D值变化百分比明显增高。Hu等[27]研究表明预测直肠癌pCR效能较高的参数是D值,其预测效能优于ADC值,这与Li等[28]的研究结果一致。而D*值和f值的评估价值在不同的研究中有所差异。血管含量越高,灌注相关参数值越大。Meyer等[29]发现f值与血管密度密切相关。Yang等[30]研究显示pCR组nCRT后的D*值和f值显著高于非pCR组,而Zhu等[31]、Liang等[32]发现二者在预测或识别pCR反应方面价值不大,原因可能是随着肿瘤恶性程度增高,肿瘤异质性及肿瘤内微血管复杂性增加。总之,IVIM参数的重复性和再现性欠佳,尤其是D*和f值,其预测效能仍需大量研究进一步证实。

2.3 扩散峰度成像

       扩散峰度成像(diffusion kurtosis imaging, DKI)基于非高斯模型建立,能敏感检测到肿瘤组织微观结构变化。水分子在人体内的运动遵循非高斯模型,其扩散受周围环境的限制程度越大,扩散的非高斯性越显著[33]。DKI的定量参数有很多,与直肠癌相关研究较多的参数包括平均峰度系数(mean kurtosis, MK)和平均扩散率(mean diffusion, MD)。MK值的大小取决于组织微观结构的复杂程度,而MD值代表水分子的扩散速率,是非高斯分布偏移校正后的表观扩散系数[34]。nCRT后肿瘤组织微观结构将变得复杂和具有异质性,因此DKI参数可能具有预测肿瘤反应的潜能。Hu等[35]发现nCRT后MK诊断价值更高,在评估pCR和非pCR方面表现出比常规扩散更高的敏感性和特异性。Bates等[36]认为MD值与TRG显著相关,nCRT后较高的MD值可能预示着更好的放化疗反应。Li等[37]研究表明,MD变化百分比可用于评估患者对放化疗的肿瘤耐药性,且具有很高的AUC值(0.939),认为其可用于评估预后和指导治疗。DKI作为一种非高斯模式获取生物组织复杂结构特征的多参数成像,较DWI更能反映真实组织的特征。但目前只有少数研究集中于DKI在识别完全肿瘤反应中的价值,且研究规模相对较小。因此,DKI在确定肿瘤完全反应中的价值仍需进一步证实。

2.4 动态对比增强MRI

       动态对比增强MRI(dynamic contrast-enhanced MRI, DCE-MRI)是一种定量技术,利用静脉注射对比剂测量组织的血管灌注参数,并评估组织灌注和氧合,间接反映肿瘤血管分布[38]。血管生成是肿瘤生长和转移的关键因素,nCRT后肿瘤血管的变化和纤维化可能代表了两种结果:良好肿瘤反应或是术前存活肿瘤细胞的持续存在。DCE-MRI的常用参数包括:容量转移常数(volume transfer constant, Ktrans),是对比剂从血管进入组织间隙的速度,一定程度上体现肿瘤局部血流状态及表面渗透性;速率常数(rate constant, Kep),是对比剂往返速度;血管外细胞外间隙容积分数(extravascular extracellular clearance volume fraction, Ve),是组织内细胞外血管外间隙容积比[39]。Petrillo等[40]通过分析得出DCE-MRI诊断pCR的敏感度为81%,特异度为85%。Ciolina等[41]通过定量分析Ktrans、Ve、Kep对pCR的诊断价值,认为Ktrans是肿瘤nCRT后完全反应的可靠指标,反应良好组的患者具有更高的Ktrans值,代表着肿瘤细胞的血管网渗透性更强,利于氧气输送,继而使得肿瘤细胞对化疗药物敏感性增加。然而DCE衍生参数的结果在不同的研究中仍然存在争议。Gollub等[42]研究表明常规MRI与DCE联合评估肿瘤反应,并没有提高识别pCR的敏感性。Yeo等[43]发现DCE的衍生参数无法区分pCR组和非pCR组。尽管DCE的衍生参数具有评估直肠癌治疗后肿瘤反应的潜力,但其准确性仍需要验证。

       综上,fMRI可以通过定量参数提供更多有关残余肿瘤或者肿瘤坏死纤维化信息,为nCRT后肿瘤反应的评估提供更准确的依据。然而现在诸多研究尚处于初级阶段,均未形成统一的标准,如DWI、IVIM和DKI中合适b值的选择,图像伪影、信噪比的控制,图像采集协议标准化的不足,各观察者间的偏差,均会导致结果的差异及相对较差的重复性。因而需要借助人工智能手段从图像中提取更多隐藏的信息,更加客观地反映nCRT对直肠癌的作用。

3 基于MRI的人工智能预测模型

       人工智能是一门新的技术科学。以机器学习为代表的人工智能方法在医学领域的应用越来越广泛。近年来运用影像组学评估nCRT后肿瘤反应的临床研究逐渐增多,影像组学可以从形态和功能图像中提取定量信息,挖掘更多反映肿瘤组织病理学和生理特征的信息和特征[44],从而达到个体化诊疗的目的。传统的放射组学分析通常是在一个或多个图像模型上从感兴趣区域中提取和分析定量成像特征,最终目标是获得预测或预后模型。另一种类型的放射特征是通过转移学习从预先训练的卷积神经网络中提取的基于深度学习的特征。Fu等[45]研究显示,深度学习模型在预测直肠癌对nCRT的反应方面的性能优于手动勾画构建的模型。Yi等[46]利用深度学习模型提取T2WI的影像特征以评估nCRT后pCR的AUC是0.91,证明了基于影像组学的人工智能模型在评估肿瘤反应有很广阔的应用前景。精准医疗时代,单一的特征或模型已不能满足个体化治疗的要求,只有整合所有可能有用的信息进行分析,才能提高预测和诊断的准确性。Li等[47]创建了CT和MRI的多模态联合预测模型,验证集的AUC达到了0.93,增加了治疗效果预测的准确性。Wan等[48]整合了MRI放射组学和病理学建立的模型亦达到了很好的预测效果。Wang等[49]、Bordron等[50]利用从多参数MRI中提取的放射组学特征,再通过深度学习模型能够准确地区分不良反应者和良好反应者。集成多种算法的深度学习模型在预测直肠癌患者nCRT反应性方面也表现出更好的性能。在不久的将来,基于MRI的人工智能预测模型将为直肠癌患者的治疗提供更加系统化、精准化的医疗服务。

4 小结与展望

       直肠癌pCR的评估非常重要,当考虑直肠癌患者nCRT后达到肿瘤完全反应时,应采用严格的标准,参数的特异性尤其关键。肿瘤完全反应的误诊可能导致肿瘤复发和预后不良。常规MRI能直观反映nCRT后的肿瘤变化;fMRI能进一步提供nCRT后组织的微观变化,定量识别残余肿瘤与纤维化;基于MRI的人工智能模型可以通过反映大量微观组织的异质性来达到对疗效准确预测的目的。然而各种序列在实际运用中亦面临诸多问题,最棘手的问题是扫描参数的标准化设置和定量指标的参考价值;主要的挑战仍然是MRI在诊断肿瘤nCRT完全反应方面的准确性。随着MRI技术的进步、人工智能的不断发展和应用,二者不断地融合,将帮助MRI成为评估直肠癌肿瘤反应可靠的决策工具,为临床诊疗提供更具价值的参考信息。

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