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DCE-MRI定量技术专题
定量DCE-MRI在乳腺良恶性病变诊断中的临床价值与病理对照研究
窦瑞雪 杨丽 黄宁 时高峰

窦瑞雪,杨丽,黄宁,等.定量DCE-MRI在乳腺良恶性病变诊断中的临床价值与病理对照研究.磁共振成像, 2015, 6(8): 592-598. DOI:10.3969/j.issn.1674-8034.2015.08.007.


[摘要] 目的 探讨动态增强磁共振成像(dynamic contrast-enhanced MRI,DCE-MRI)定量参数对乳腺良恶性病变鉴别诊断价值及其与微血管密度(microvessel density,MVD)和血管内皮生长因子(vascular endothelial growth factors,VEGF)的相关性。材料与方法 收集河北医科大学第四医院2014年11月至2015年2月67例乳腺病变患者行T1-DCE-MRI扫描,总扫描时相70次,第1时相17.3 s,以后单时相扫描时间4.58 s,总时间约5 min 33 s。测量如下参数:容量转移常数(volume transfer constant,Ktrans)、速率常数(rate constant,Kep)、血管外细胞外间隙容积比(extravascular extracellular volume fraction,Ve)。检查后1周内均经手术取得病理,采用单因素方差分析和LSD法两两比较正常组、良性组及恶性组间定量参数的差异,并绘制ROC曲线分析良、恶性组间的差异。用Pearson相关分析,分析恶性组MVD、VEGF表达与定量参数的相关性。结果 正常组Ktrans、Kep、Ve值分别为(0.012±0.003) min-1、(0.439±0.083) min-1、(0.045±0.013);良性组Ktrans、Kep、Ve值分别为(0.049±0.007) min-1、(0.588±0.073) min-1、(0.107±0.022);恶性组Ktrans、Kep、Ve值分别为(0.187±0.045) min-1、(1.205±0.517) min-1、(0.133±0.049)。Kep、Ktrans在良、恶性组间差异有统计学意义(P=0,P=0.041);Kep、Ktrans在正常、恶性组间差异有统计学意义(P=0,P=0.008)。以最大约登指数为最佳诊断切入点,Ktrans、Kep的敏感性分别为86.8%、67.9%。Ktrans、Kep特异性分别为89.5%、94.7%,曲线下面积分别为0.931、0.819。30例乳腺癌患者的Ktrans、Kep、Ve值分别为(0.183±0.031) min-1、(1.192±0.063) min-1、(0.127±0.031),MVD计数为(34.96±9.86),VEGF积分为(5±1)分,均与Ktrans、Kep、Ve呈正相关(P<0.05)。Kep、Ktrans与VEGF相关性最强(r=0.759, r=0.771),与MVD相关性较强(r=0.613, r=0.607)。结论 定量参数Ktrans、Kep对乳腺良恶性病变鉴别诊断有临床价值,且有较高的诊断效能。乳腺癌Ktrans、Kep与VEGF、MVD相关性较强,有望成为无创性评价乳腺肿瘤微循环的新方法。
[Abstract] Objective: To evaluate the value of T1 quantitative parameters of Dynamic Contrast Enhanced MRI (DCE-MRI) at high temporal resolution in the diagnosis of breast lesions and analyze their correlations with MVD, VEGF for breast carcinoma.Materials and Methods: Sixty-seven patients with breast lesions were enrolled from Nov. 2014 to Feb. 2015 in one hospital, underwent the DCEMRI sequence using TWIST with 70 phases. All were confirmed with pathology within one week. Temporal resolution was 4.58 s per phase except for the first phase 17.3 s. And total scanning time was 5 min 33 s. The following quantitative parameters were calculated: volume transfer constant (Ktrans), rate constant (Kep) and extravascular extracellular volume fraction (Ve). The Ktrans, Kep and Ve among malignant, benign and normal glandular tissues, and all kinds of ductal carcinoma were calculated and compared by one-way ANOVA and LSD method. Additionally, the areas under the ROC curve of Ktrans, Kep and Ve between malignant and benign lesions were compared. The correlations between quantitative DCEMRI parameters and the microvessel density(MVD), vascular endothelial growth factor(VEGF)of malignant lesions were performed using Pearson correlation analysis.Results: The mean Ktrans, Kep and Ve of normal glandular tissue were (0.012±0.003) min-1, (0.439±0.083) min-1, (0.045±0.013). The mean Ktrans, Kep and Ve of benign lesions were (0.049±0.007) min-1, (0.588±0.073) min-1, (0.107±0.022). The mean Ktrans, Kep and Ve of malignant lesions was (0.187±0.045) min-1, (1.205±0.517) min-1, (0.133±0.049). The difference of Kep, Ktrans between malignant and benign lesions was statistically significant (P=0, P=0.041). The difference of Kep, Ktrans between malignant lesions and normal glandular was statistically significant (P=0, P=0.008). The sensitivity of Ktrans and Kep were 86.8% and 67.9%. The specificity of Kep and Ktrans were 94.7% and 89.5% using the maximum Youden’ index as the cut-off value. The area under the ROC curve of Ktrans and Kep were 0.931 and 0.819 respectively. The mean Ktrans, Kep and Ve of the 30 patients were (0.183±0.031) min-1, (1.192±0.063) min-1, (0.127±0.031). The amount of MVD was (34.96±9.86) and the score of VEGF was (5±1). The quantitative parameters Ktrans, Kep and Ve were positively correlated with MVD and VEGF (P<0.05). Kep and Ktrans showed significantly statistical correlations with anti-VEGF (r=0.759, r=0.771) and anti-CD34 (r=0.613, r=0.607).Conclusion: The differential diagnosis of benign and malignant breast lesions by Ktrans, Kep were applicable. Ktrans, Kep of breast carcinoma were strongly correlated with MVD and VEGF. They could be used as non-invasive biomarkers to evaluate the microcirculation status of breast carcinoma in vivo.
[关键词] 乳腺肿瘤;磁共振成像;动态增强;微血管密度;血管内皮生长因子
[Keywords] Breast neoplasms;Magnetic resonance imaging;Dynamic enhancement;Microvessel density;Vascular endothelial growth factors

窦瑞雪 航天中心医院超声科,北京 100039

杨丽 河北医科大学第四医院,石家庄 050011

黄宁 GE医疗中国,上海 201203

时高峰* 河北医科大学第四医院,石家庄 050011

通讯作者:时高峰,E-mail:gaofengs62@sina.com


收稿日期:2015-06-01
接受日期:2015-07-17
中图分类号:R445.2; R737.9 
文献标识码:A
DOI: 10.3969/j.issn.1674-8034.2015.08.007
窦瑞雪,杨丽,黄宁,等.定量DCE-MRI在乳腺良恶性病变诊断中的临床价值与病理对照研究.磁共振成像, 2015, 6(8): 592-598. DOI:10.3969/j.issn.1674-8034.2015.08.007.

       乳腺癌的发病率在全球范围内呈上升趋势[1],预计到2030年,全球乳腺癌的发病和死亡人数将分别达到2640万和1700万[2]。影像学检查在乳腺癌的早期诊断及预估预后中起着举足轻重的作用[3]。Peters等[4]做的meta分析报告显示乳腺MRI的敏感性和特异性分别为90%和72%,较其他影像学方法有明显的优势。乳腺癌是血管依赖性肿瘤,以往的乳腺MR的研究多是基于动态增强的定性分析、半定量分析。定性分析是观察不同类型的时间-信号曲线,但良恶性病变均可表现为II型曲线,有一定重叠。半定量分析是运用常规的多期动态增强扫描序列,完成双乳扫描时间分辨率较低,或者在高时间分辨率下仅能够完成肿瘤区的局部灌注,较为局限。且不能反映组织对比剂浓度,因此半定量参数不能准确反映MR含钆对比剂引起组织增强的病理生理过程。微血管密度(microvessel density,MVD)[5]和血管内皮生长因子(vascular endothelial growth factors,VEGF)[6]是反映肿瘤血管生成和血管通透性的最重要因素。然而这些指标需依赖切取乳腺组织才能测定。基于此,本研究采用高时间分辨率对双乳进行T1动态增强扫描,探讨定量参数对乳腺良恶性病变的鉴别能力及其与MVD、VEGF的相关性。

1 材料与方法

1.1 一般资料

       本研究收集我院2014年11月至2015年2月67例因临床触诊或超声检查或乳腺钼靶X线检查发现乳腺病变的初诊患者行磁共振高时间分辨率T1动态增强磁共振扫描,检查前签署知情同意书。入组标准:(1)未经手术、放射治疗、化学治疗、激素或靶向治疗;(2)无检查禁忌症;(3)图像没有明显运动及呼吸伪影;(4)均于检查后一周内手术,手术病理确诊。排除标准:(1)怀孕、哺乳期妇女;(2)患有严重心、肝、肾疾病、免疫性疾病及其他内外科疾病。

       符合入组标准的67例患者共发现70个病灶(3例为双病灶),患者均为女性。良性组年龄29~ 51岁,中位年龄为39岁,16例19个病灶,包括导管内乳头状瘤3个,上皮不典型增生5个,纤维腺瘤8个,乳腺增生性腺病3个。恶性组年龄28~ 62岁,中位年龄为47岁,51例51个病灶,包括导管内癌(含伴微浸润)5个,浸润性导管癌40个,髓样癌3个,浸润性微乳头状癌2个,叶状肿瘤II级1个。随机选取57例非脂肪型乳腺作为正常腺体组。恶性组30例患者测量MVD、VEGF表达。本研究方案已经我院伦理委员会审核通过。

1.2 检查方法、步骤

       采用3.0 T磁共振扫描仪(Magnetom Skyra,Siemens,德国),专用八通道双侧乳房相控线圈。患者取俯卧位,双侧乳房自然悬垂于线圈内,头先进。扫描范围包含双侧乳腺组织,双侧腋窝,向后包括主动脉。

       横轴位tirm T2WI:TR 4060,TE 70,TI 230,层厚3.0 mm,层间距1.5 mm,FOV 340 mm×340 mm,采集矩阵358 × 448;横轴位DWI:TR 5700 ms,TE 66 ms,b值分别取0 s/mm-2和800 s/mm-2

       T1多翻转角:参数为TR 5.64 ms,TE 2.46 ms,FOV 333 mm × 380 mm,矩阵196 × 320,层厚2.5 mm,层间距0.5 mm,翻转角度5°、10°、12°、15°。

       T1动态增强序列:翻转角采用12°,其余扫描参数同T1多翻转角。扫描应用时间分辨随机轨道成像(time resolved angiography with interleaved stochastic trajectories,TWIST)技术,注射磁共振对比剂(钆双胺,GE,美国)0.1 mmol/kg体重,注射流率2.5 ml/s,完毕后追加15 ml生理盐水同样流速注射。无间歇扫描,总扫描时相70次。第1时相17.3 s,以后单时相扫描时间4.58 s,总时间约5 min 33 s。

1.3 MRI数据处理与分析

       将动态增强图像调入Omni-Kinetics软件(GE医疗)进行后处理。结合T2WI、DWI及灌注图像确定病变位置。避开血管、钙化、脂肪、空洞和坏死组织,选择肿物的实质成分强化最明显的部位作为感兴趣区,每个病变测量3次定量参数并取平均值;手动选取对侧正常乳腺相对致密的腺体为感兴趣区,避开腺体边缘、血管、脂肪等结构,每个患者测量3次取平均值。选取病灶感兴趣区层面的主动脉作为输入动脉获得动脉输入函数,运用Extended Tofts线性模型,通过计算得到以下参数:(1)容量转移常数(Ktrans),对比剂从血管内扩散到血管外的速率常数,单位为min-1;(2)血管外细胞外间隙容积比(Ve),是血管外细胞外间隙占整个体素的容积比;(3)速率常数(Kep),组织间对比剂重新回到血管内的速率常数,单位为min-1。三者间关系为Ve=Ktrans/Kep。

1.4 病理学评价

       参照2003版WHO乳腺病理分型分为良性组、恶性组。根据经Elston和Ellis改良的Bloom-Richardson分级法确定组织学分级。VEGF、CD34[7]的免疫组化染色采用试剂盒推荐的PV两步法。微血管计数是利用免疫组化技术针anti-CD34和血管内皮细胞抗原结合对肿瘤血管进行定量分析,阳性呈棕黄色,参照Weidner[8]法。VEGF阳性棕黄色颗粒多位于细胞质,综合着色细胞百分数和染色强度,参照Mattern[9]积分法。

1.5 统计学方法

       应用SPSS 13.0统计软件对数据结果进行统计分析,先进行正态分布检验及单因素方差分析。采用LSD法两两比较正常组、良性组及恶性组间定量参数的差异;最后绘制ROC曲线并计算曲线下面积,根据最大约登指数(Youden index:约登指数=敏感性+特异性-1)分析乳腺良、恶性组间的差异。30例乳腺癌患者各定量参数与MVD、VEGF的相关性采用Pearson相关性分析。计量资料以±s表示,检验水准均为0.05。

2 结果

       单因素方差分析显示,恶性组、良性组与正常组间定量参数值均呈正态分布且方差齐同。采用LSD法比较两两间差异,Kep、Ktrans在良、恶性病变间有差异统计学意义(P<0.05),见表1。恶性病变、良性病变及正常腺体的Ktrans、Kep、Ve均值依次减低,各定量参数伪彩图分别显示为混杂色,红色代表高强化,蓝色代表强化较低,黄色介于两者之间(图1图2)。绘制Ktrans、Kep、Ve的ROC曲线,可知Ktrans、Kep的敏感性较高,Kep的特异性最高(表2图3)。

       30个乳腺癌病灶的Ktrans、Kep、Ve值分别为(0.183±0.031) min-1、(1.192±0.063) min-1、(0.127±0.031)。MVD计数为(34.96±9.86) ,VEGF积分为(5±1)分。Pearson相关分析Ktrans、Kep与免疫组化因子呈正相关(P<0.05),见表3

图1  右乳浸润性导管癌III级。A:增强图像;B~ D:Ktrans、Kep、Ve的伪彩图,其数值分别为0.178 min-1、1.462 min-1、0.163;E:CD34标记的MVD高表达(× 200,SP);F:VEGF高表达(× 200,SP)
Fig. 1  Invasive ductal carcinoma of Grade III in the right breast. A: T1WI contrast-enhanced image; B-D: Pseudo-color image of Ktrans, Kep and Ve map, the values are 0.178 min-1, 1.462 min-1, 0.163; E: High CD34 stained MVD (×200, SP); F: High expression VEGF (×200, SP).
图2  左乳纤维腺瘤。A:增强图像;B~ D:Ktrans、Kep、Ve的伪彩图,其数值分别为0.050 min-1、0.601 min-1、0.114;E:病理结果图片(HE × 200)
图3  Ktrans、Kep和Ve鉴别良恶性的ROC曲线
Fig. 2  Fibroa-denoma in the left. A: T1WI contrast-enhanced image; B-D: Pseudo-color image of Ktrans, Kep and Ve map, the values are 0.050 min-1, 0.601 min-1, 0.114; E: Pathological image (HE ×200).
Fig. 3  Receiver operating characteristic (ROC) curves for Ktrans, Kep and Ve in differential diagnosis.
表1  良性组、恶性组与正常腺体组定量参数结果比较(±s)
Tab. 1  Difference analysis of quantitative parameters among normal glandular tissue, benign lesions and malignant lesions (±s)
表2  定量参数对乳腺良恶性肿瘤诊断效能
Tab. 2  Area under ROC curve, the best diagnosis value, sensitivity, and specificity of quantitative parameters between benign lesions and malignant lesions
表3  定量参数与MVD、VEGF的相关性
Tab. 3  Correlation analysis between the quantitative parameters and MVD, VEGF

3 讨论

       肿瘤的发生、发展与转归与血管生成密切相关[10,11]。肿瘤新生血管的特点为:(1)肿瘤血管数量巨大,微循环血容量增大、流速增加;(2)新生血管结构紊乱,血管内皮细胞幼稚,基底膜减少,裂隙性血管网尚无舒缩功能,渗漏性增高。以上构成了肿瘤微循环在空间和时间上的不均衡性。乳腺恶性肿瘤[5,12,13]符合以上特点,而良性肿瘤血管形态相对成熟,微血管密度等于或者略高于正常乳腺实质,相对于恶性肿瘤渗透性较低。

       T1加权的DCE-MRI利用异常的肿瘤微循环系统,动态监测对比剂在体内的吸收、代谢等药代动力学过程,获得直接代表造影剂浓度的定量参数。Medeiros[14]、李瑞敏[15]等报道Kep、Ktrans在正常组与恶性组、良性组与恶性组鉴别中均有统计学差异,与本研究结果相同。而Ktrans、Kep在正常组与良性组间无统计学差异,这与病理生理基础密切相关。乳腺癌细胞生长旺盛,新生血管迅速增多,血管结构紊乱、动静脉瘘等造成血管管径增粗,内皮不完整,血管壁薄而脆,基底膜减少等导致血管壁通透性增高,对比剂交换剂量增多、速度加快。良性病变与正常乳腺由于缺乏高通透性的血管,血流较平稳,对比剂均呈缓慢填充,且良性病变中胶原纤维增生导致细胞外血管外间隙结构致密,对比剂流通受阻。

       Ktrans、Kep的曲线下面积分别为0.931、0.819,诊断效能较高。Ktrans、Kep的敏感性分别为86.8%、67.9%,Ktrans、Kep的特异性分别为89.5%、94.7%。Baek等[16]研究发现,在血管功能参数中Ktrans对良恶性病变的诊断鉴别意义最大,与本实验结果相符。因为Ktrans反映肿瘤局部微血管血流状态及渗透性,被认为是最能反映肿瘤组织渗透性的指标[17,18]。Ktrans、Kep、Ve的最佳诊断切入点为0.083 min-1、0.902 min-1、0.066。Kep体现出更高的良恶性鉴别准确率。与既往报道[15,16,17,18]不完全一致,分析原因可能是数学模型不同。本研究运用Extended Tofts线性模型,该模型把组织分为血管内及血管外细胞外间隙(extravascular extracellular space,EES)两个部分。血管内细胞外体积分数(即血浆)为中央室,而血管外细胞外体积分数为周边室,对比剂依赖浓度梯度在血管内与EES之间交换,更接近真实的肿瘤病生理过程。另外,本研究采用TWIST序列,该技术通过以螺旋轨道填充K空间提高扫描速度[19],时间分辨率很高(4.58 s每期),所获得的信息就更接近体素内真实的血流、血管通透性以及相关的生理学参数值。

       本实验Ve在各组病变间鉴别均无明显统计学差异。恶性病变的Ktrans值、Kep值同时增高,两者的比值Ve并无显著升高,或病变发展过程中Ve的变化较慢,因此与良性病变的Ve值存在重叠。Tofts[20]的研究结果表明Ve值较不稳定,可能与Ve受病变周围水肿的影响较大有关。也有学者[21,22]认为产生不同结果的原因可能为输入动脉不同。动脉输入函数是动脉内对比剂浓度随时间变化的函数关系。理论上最好选择病变组织的供血血管,这样计算出的定量参数更为准确,但是组织中这类血管非常细小、难以获得,易受部分容积效应或移动伪影的影响[23]。本研究选择显示清晰的主动脉。另外,注射速率、个体差异等也会影响Ve值。

       通过Pearson相关分析发现,定量参数均与免疫组化因子之间存在正相关关系,Kep与MVD相关性最强,Ktrans与VEGF相关性最强,与国内外研究[24,25,26]结果基本一致。Uzzan B[5]证实MVD可直接反映肿瘤血管新生的活跃程度。当肿瘤MVD增加时,肿瘤新生血管活跃,血容量增加且血管壁通透性好,EES增大,对比剂在血管内与EES间交换更快,故Ktrans、Kep、Ve值增加。MVD被公认为是判断血管生成的"金标准",然而不能直接反映血流灌注、血管通透性等情况,VEGF兼顾了肿瘤的生物学特性。由于肿瘤组织VEGF分泌持续性增多,VEGF作为高度特异的有丝分裂原与血管内皮细胞上的受体结合,刺激血管内皮细胞分裂、趋化,改变内皮细胞基因活化形式,抑制血管内皮细胞的凋亡,使肿瘤增生迅速区域血管分布丰富,则血流灌注量增大;诱导内皮细胞产生纤溶酶原激活物、间质胶原酶,增加毛细血管壁通透性,故Ktrans、Kep均会增高。丰富的血供为肿瘤的生长提供更多的营养物质和氧分,均会经过EES,故Ve值也会增高。故定量参数仅在乳腺癌的应用中一定程度上代替了组织学检查,成为评价乳腺癌的MVD、VEGF的检查方法。

       综上所述,本研究显示Ktrans、Kep对乳腺良恶性病变有鉴别意义,且与MVD、VEGF有较强的相关性,因此Ktrans、Kep用于无创评估肿瘤性质及预后成为可能,但Ve值的意义有待进一步探讨。

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上一篇 DCE-MRI定量渗透性参数联合ADC值在肺部良恶性病变中鉴别诊断价值
下一篇 定量动态增强MRI对肾透明细胞癌及乏脂肪肾血管平滑肌脂肪瘤的鉴别诊断价值
  
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