分享:
分享到微信朋友圈
X
临床研究
DKI在鉴别脑胶质瘤复发与假性进展中的应用价值研究
党佩 王立东 黄雪莹 刘静静 吕瑞瑞 杨治花 王晓东

Cite this article as: Dang P, Wang LD, Huang XY, et al. Application value of DKI in distinguishing recurrence and pseudoprogression of glioma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 28-33.本文引用格式:党佩, 王立东, 黄雪莹, 等. DKI在鉴别脑胶质瘤复发与假性进展中的应用价值研究[J]. 磁共振成像, 2022, 13(5): 28-33. DOI:10.12015/issn.1674-8034.2022.05.006.


[摘要] 目的 探讨扩散峰度成像(diffusional kurtosis imaging,DKI)技术在鉴别脑胶质瘤复发与假性进展中的临床应用价值。材料与方法 回顾性分析宁夏医科大学总医院2018年10月至2020年12月间40例术后行放、化疗并行DKI序列扫描的脑胶质瘤患者资料。通过二次手术病理或经增强MRI扫描随访>6个月,分复发组(24例)与假性进展组(16例)。采用独立样本t检验或Mann-Whitney U检验,受试者工作特征曲线比较两组患者增强病灶和瘤周水肿中DKI参数值:平均扩散峰度(mean kurtosis,MK)、平均扩散系数(mean diffusivity,MD)、径向扩散峰度(radial kurtosis,RK)、轴向扩散峰度、各向异性分数。以患者无进展生存期(gression free survival,PFS)作为事件的观察终点,Cox比例风险模型用于多因素分析。结果 复发组较假性进展组增强病灶的相对平均扩散峰度(ratio of MK,rMK)、相对径向扩散峰度(ratio of RK,rRK)升高(P<0.05),相对平均扩散系数(ratio of MD,rMD)降低(P<0.05),rMK、rRK、rMD的曲线下面积(area under the curve,AUC)分别0.94、0.83、0.70 (P<0.05)。复发组较假性进展组瘤周水肿的rMK升高、rMD降低(P<0.05),rMK、rMD的AUC分别0.82、0.73 (P<0.05)脑室下区受累、增强病灶的rMK、rRK、rMD和瘤周水肿的rMK、rMD均与PFS具有相关性(P<0.05)。结论 DKI可用于鉴别胶质瘤复发与假性进展,参数值MK可作为较好的影像学标记,增强病灶的MK值是PFS的独立危险因素。
[Abstract] Objective To investigate the value of DKI technology in differentiating glioma recurrence and pseudoprogression in clinical.Materials and Methods Retrospectively collect of 40 patients with glioma who underwent surgery, radiotherapy, chemotherapy and DKI scanning from October 2018 to December 2020 in the General Hospital of Ningxia Medical university. Patients was divided into the recurrence group (24 cases) and the pseudoprogression group (16 cases) by pathology or enhanced MRI scan followed up for more than 6 months. Data were compared by independent samples t-test, Mann-Whitney U-test and receiver operating characteristic to compare the DKI parameter values in enhancing lesions and peritumoral edema in the two groups of patients: Mean kurtosis (MK), mean diffusivity (MD), radial kurtosis (RK), axial kurtosis, fractional anisotropy. Using patient gression free survival (PFS) as the observation end point for events, cox proportional hazards model was used for multivariate analysis.Results Compared with the pseudoprogressive group, the ratio of MK (rMK) and ratio of RK (rRK) of the enhanced lesions in the recurrence group were increased, and ratio of MD (rMD) was decreased (P<0.05). The AUCs of rMK, rRK, and rMD were 0.94, 0.83, and 0.70, respectively (P<0.05). Compared with the pseudoprogressive group, the rMK of peritumoral edema was increased in the recurrence group and rMD was decreased (P<0.05). The area under the ROC curve of rMK and rMD were 0.82, 0.73, respectively (P<0.05). Involvement of the subventricular zone, rMK, rRK and rMD in enhanced lesions and rMK, rMD in peritumoral edema were correlated with PFS (P<0.05).Conclusions DKI can be used to distinguish recurrence and pseudoprogression of glioma, and the parameter value MK can be used as a better imaging marker, the MK value of enhancing lesions is an independent risk factor for PFS.
[关键词] 脑胶质瘤;复发;假性进展;扩散峰度成像;磁共振成像;瘤周水肿
[Keywords] glioma;recurrence;pseudoprogression;diffusion kurtosis imaging;magnetic resonance imaging;peritumoral edema

党佩 1   王立东 2   黄雪莹 1   刘静静 3   吕瑞瑞 4   杨治花 5   王晓东 1*  

1 宁夏医科大学总医院放射科,银川 750004

2 银川市中医医院放射科,银川 750001

3 西安市胸科医院放射科,西安 710061

4 宁夏医科大学临床医学院,银川 750004

5 宁夏医科大学总医院放疗科,银川 750004

王晓东,E-mail:xdw80@yeah.net

作者利益冲突声明:全体作者均声明无利益冲突。


基金项目: 宁夏回族自治区重点研发计划 2019BEG03037
收稿日期:2021-12-17
接受日期:2022-04-01
中图分类号:R445.2  R730.264 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.05.006
本文引用格式:党佩, 王立东, 黄雪莹, 等. DKI在鉴别脑胶质瘤复发与假性进展中的应用价值研究[J]. 磁共振成像, 2022, 13(5): 28-33. DOI:10.12015/issn.1674-8034.2022.05.006.

       胶质瘤是成人最常见的原发性脑肿瘤,患者的病死率和复发率高[1]。目前标准化治疗方案为手术全切除肿瘤后辅以放疗并同步行替莫唑胺化疗[2]。增强MRI检查是胶质瘤患者标准化治疗后主要的随访形式,常引起临床困惑的是,胶质瘤复发与假性进展均会在术区及周围出现新的或扩大的异常强化病灶或瘤周水肿,而且两者的发生时间窗均在术后6个月内[3]。研究表明假性进展反映的是放、化疗引起的炎症增加和血脑屏障破坏,归因于治疗效果而非胶质瘤复发[4]。胶质瘤复发患者须二次手术或进一步放化、疗治疗,以改善临床症状和提高整体生存率[5]。若假性进展误判为胶质瘤复发不仅增加患者的焦虑情绪,还会导致标准化一线治疗的中断和不必要的手术。准确、及时区分胶质瘤复发和假性进展有利于为胶质瘤患者提供最有效的治疗。

       目前,MR功能成像在鉴别脑胶质瘤复发与假性进展的应用主要有扩散加权成像、代谢成像以及灌注加权成像技术等[6, 7, 8]。扩散成像技术中扩散张量成像(diffusion tensor imaging,DTI)可以描述胶质瘤内呈高斯分布的水分子受限程度以及脑白质纤维束的完整性,由于肿瘤细胞的异质性,DTI提供的证据可靠性是有限的[9]。酰胺质子转移成像(amide proton transfer,APT)是一种无创且定量测定胶质瘤内源性蛋白质及多肽含量的代谢成像技术,能间接反映细胞增殖,显示肿瘤代谢最活跃的部分,但由于很多活体内代谢物质的饱和频率非常接近,很容易发生相互交叉饱和从而影响APT成像的特异性[10]。灌注加权成像技术中的动脉自旋标记技术将人体的内源性水分子作为示踪剂而无需对比剂,对肾脏造成的负担较少,但图像的信噪比、空间分辨力较低[11]。扩散峰度成像(diffusional kurtosis imaging,DKI)是在DTI基础上延伸的新兴扩散成像技术,反映生物组织中水分子非高斯扩散特性,成像依赖于扩散敏感梯度方向及扩散敏感因子b值,一次扫描可获得平均扩散峰度(mean kurtosis,MK)、平均扩散系数(mean diffusivity,MD)、径向扩散峰度(radial kurtosis,RK)、轴向扩散峰度(axial kurtosis,AK)、各向异性分数(fractional anisotropy,FA)五个参数[12]。已有研究将DKI应用于脑肿瘤的鉴别诊断、脑胶质瘤的分级诊断、预测胶质瘤患者IDH基因分型等[13, 14, 15, 16]

       本研究拟测量胶质瘤复发与假性进展患者增强病灶与瘤周水肿中的DKI参数值并进行标准化处理,用于探讨DKI技术鉴别脑胶质瘤复发与假性进展中的临床应用价值。

1 材料与方法

1.1 临床资料

       回顾性分析宁夏医科大学总医院2018年10月至2020年12月期间行手术并接受放、化疗治疗的脑胶质瘤术后患者资料。术前所有患者、术后22例患者均在72 h内行增强MRI检查,放疗期间所有患者均行增强MRI检查随访,随访期为6~10个月,随访截止时间为2020年12月31日,主要记录无进展生存期(gression free survival,PFS)。纳入标准:(1)术后经病理确诊脑胶质瘤并同步行放化疗患者;(2)行增强MRI检查随访中有新发或进行性增大的强化病灶;(3)图像质量佳。符合以下标准之一诊断为复发[17]:(1)脑组织病理活检或二次手术后组织学检查可见肿瘤细胞;(2)增强检查随访期间强化病灶逐渐扩大,瘤周水肿及占位效应逐渐加重,积极治疗后临床表现逐渐恶化。符合以下标准之一诊断为假性进展[17]:(1)组织学检查未见有活性肿瘤细胞;(2)增强检查随访期间强化病灶最终无变化或逐渐缩小,瘤周水肿及占位效应逐渐减轻,积极治疗后临床表现稳定或逐渐好转。本研究经宁夏医科大学总医院伦理委员会批准,免除受试者知情同意,批准文号KYLL-2021-540。

1.2 检查方法

       采用PHILIPS Medical Systems/Ingenia 3.0 T扫描仪,标准8通道头线圈采集数据。常规序列包括轴位T1加权成像(T1 wighted imaging,T1WI)、T2加权成像(T2 wighted imaging,T2WI)和T2液体衰减反转恢复(fluid attenuated inversion recovery,FLAIR)以及轴位T1WI增强序列;具体参数为:T1WI序列:TR/TE 2000 ms/20 ms;T2WI序列:TR/TE 4000 ms/107 ms;T2 FLAIR序列:TR/TE 7000 ms/120 ms;轴位T1WI增强序列:TR/TE 2000 ms/20 ms;各常规序列其他参数分别为:层厚 6 mm,层间距 1 mm,扫描层数 21层,FOV 230 mm×230 mm,矩阵 288×167。DKI序列采用单次激发自旋回波序列,扩散敏感梯度场参数(b值)设置为0、1000、2000 s/mm2,每个梯度场施加32个扩散方向,其他参数为:FOV 220 mm×220 mm,矩阵84×84,激励次数1次,TR/TE 3809 ms/98 ms,层厚6 mm,层间距1 mm,扫描层数21,扫描时间390 s。使用双筒高压注射器经前臂静脉注入对比剂钆双胺注射液(通用医疗保健,爱尔兰),剂量为0.1 mmol/kg,注射流率为3.0 mL/s。

1.3 图像后处理

       将DKI数据导入Diffiisional Kurttosis Estimator (DKE,https://www.nitrc.org/projects/dke)中,生成DKI的各参数图:MK、RK、AK、MD、FA。由两位具有15年工作经验的磁共振医师采用盲法并独立使用IT-KSNAP软件(http://www.itksnap.org/pmwiki/pmwiki.php)分别在增强病灶(T1轴位增强序列)、瘤周水肿(T2 FLAIR序列)的最大层面进行感兴趣区(region of interest,ROI)的勾画和测值,ROI勾画时避开坏死、囊变、出血和邻近的正常组织。ROI范围根据肿瘤实质大小决定,重复测3次取平均值,数据均保留小数点后两位。为了保证实验结果的可靠性,减少由于患者大脑内不同区域所致的参数差异影响,将病变值除以镜像正常脑白质值得到标准化后参数值,分别为相对平均扩散峰度(ratio of MK,rMK)、相对轴向扩散峰度(ratio of AK,rAK)、相对径向扩散峰度(ratio of RK,rRK)值和相对扩散各向异性分数(ratio of FA,rFA)、相对平均扩散系数(ratio of MD,rMD)。

1.4 统计学分析

       所得资料均采用SPSS 23.0统计软件分析,以患者PFS作为事件的观察终点。采用组内相关系数(intraclass correlation efficient,ICC)分析观察者内和观察者间的可重复性,ICC>0.75说明一致性较好。符合正态分布的计量资料用均数±标准差表示,不符合正态分布的计量资料用中位数(上下四分位数)表示。采用单样本K-S检验进行正态性检验,采用两独立样本t检验(方差齐)或Mann-Whitney U检验(方差不齐)检测组间差异,绘制不同参数值的受试者工作特征(receiver operating characteristic,ROC)曲线,计算曲线下面积(area under the curve,AUC)及最佳诊断阈值,Cox比例风险模型(cox proportional-hazards model)用于多因素分析,P<0.05为差异有统计学意义。

2 结果

2.1 入组病例资料

       共收集符合纳入标准患者资料40例,年龄21~65 (49.6±11.2)岁。16例诊断为假性进展,其中2例经二次手术病理证实,14例以RANO标准为诊断依据。24例诊断为肿瘤复发,其中3例经二次手术病理证实,21例以RANO标准为诊断依据,其余临床资料见表1

表1  复发组和假性进展组胶质瘤患者的临床特征分析
Tab. 1  Analysis of clinical characteristics of glioma patients in recurrence group and pseudoprogression group

2.2 增强病灶、瘤周水肿的一致性检验

       DKI各参数观察者间及观察者内一致性均较好,ICC值均>0.75,见表2

表2  DKI各参数观察者间及观察者内一致性检验
Tab. 2  Inter-observer and intra-observer consistency test of parameters of DKI

2.3 复发组与假性进展组增强病灶、瘤周水肿的DKI参数值比较

       复发组与假性进展组的增强病灶中,rMK、rRK、rMD值对比组间差异具有统计学意义,与假性进展组相比,复发组增强病灶的rMK、rRK升高,rMD降低;复发组与假性进展组的瘤周水肿中,rMK、rRK值对比组间差异具有统计学意义,与假性进展组相比,复发组瘤周水肿的rMK升高,rMD降低,具体见表3图1

图1  胶质瘤术后同步放化疗患者增强MRI图像。1A~1F:男,43岁,胶质瘤术后复发患者。1A (T1WI增强序列)可见右侧颞叶异常团块状强化病灶,增强病灶对应的部位在1B (MK伪彩图,MK值为0.97)、1C (RK伪彩图,RK值为0.83)显示为高信号,在1D (MD伪彩图,MD值为1.17)显示为低信号,病理图1E (HE ×10)、1F (HE ×200)可见癌细胞弥漫性分布,二次手术病理证实为复发。1G~1L:女,61岁,胶质瘤术后假性进展患者;1G (T1WI增强序列)可见右侧顶叶异常花环样强化病灶,增强病灶对应的部位在1H (MK伪彩图,MK值为0.78)、1I (RK伪彩图,RK值为0.63)显示为稍低信号,在1J (MD伪彩图,MD值为1.40)显示为稍高信号,病理图1K (HE ×10)、1L (HE ×200)未见肿瘤细胞,二次手术病理证实为假性进展。注:MK:平均扩散峰度;RK:径向扩散峰度;MD:平均扩散系数。
Fig. 1  Enhanced MRI images of patients with postoperative concurrent radiotherapy and chemotherapy for glioma. 1A-1F: 43-year-old patient with postoperative recurrence of glioma. 1A (T1WI enhanced sequence) shows an abnormal mass-like enhancement lesion in the right temporal lobe. The corresponding part of the enhancement lesion is shown as high signal in the 1B (MK pseudo-color image, the values of MK is 0.97) and 1C (RK pseudo-color image, the values of RK is 0.83), the 1D (MD pseudo-color image, the values of MD is 1.17) is low signal. 1E (HE ×10) and 1F (HE ×200) shows the diffuse distribution of cancer cells. Pathologically proved to be a progressive disease patient. 1G-1L: 61-year-old female patients with postoperative pseudoprogression of glioma. 1G (T1WI enhanced sequence) shows an abnormal rosette-like enhancement lesion in the right parietal lobe. The corresponding part of the enhancement lesion is shown as a slightly low signal in the 1H (MK pseudo-color image, the values of MK is 0.78) and 1I (RK pseudo-color image, the values of RK is 0.63), the 1J (MD pseudo-color image, the values of MD is 1.40) shows a slightly higher signal. 1K (HE ×10) and 1L (HE ×200) shows no tumor cells. Pathologically proved to be a pseudoprogressive patient. Note: MK: mean kurtosis; RK: radial kurtosis; MD: mean diffusivity.
表3  胶质瘤复发组与假性进展组间增强病灶、瘤周水肿差异分析结果
Tab. 3  The results of difference in enhanced lesions and peritumoral edema between glioma patients in recurrence group and pseudoprogression group

2.4 DKI鉴别复发与假性进展组的ROC曲线分析

       增强病灶rMK、rRK、rMD的AUC分别0.94、0.83、0.70。瘤周水肿的rMK、rMD的AUC分别0.82、0.73,其中rMK值在增强病灶和瘤周水肿中的敏感度和特异度最高,具体见表4图2

图2  DKI参数值在鉴别胶质瘤复发与假性进展的增强病灶(2A)、瘤周水肿(2B)中ROC曲线分析。
Fig. 2  The ROC curve analysis of DKI parameter values in the enhancement lesions (2A) and peritumoral edema (2B) that distinguish the progressive disease and pseudoprogression of glioma.
表4  复发组和假性进展组增强病灶、瘤周水肿ROC曲线分析
Tab. 4  The enhanced lesions and peritumoral edema in recurrence group and pseudoprogressive group with ROC analysis

2.5 40例胶质瘤患者无进展生存期Cox模型因素分析

       单因素单变量Cox风险模型显示术前病灶侵犯脑室下区(subventricular zone,SVZ)、增强病灶的rMK、rRK、rMD以及瘤周水肿的rMK、rMD均与无进展生存期有关,多因素单变量Cox比例风险模型提示增强病灶的rMK是无进展生存期的独立危险因素(表5)。

表5  40例胶质瘤患者无进展生存期Cox模型因素分析
Tab. 5  Factors of progression-free survival in 40 glioma patients analysis by Cox

3 讨论

       本组资料显示累及SVZ区的胶质瘤病灶、IDH和MGMT基因型在复发与假性进展患者间存在差异,单变量Cox分析表明病灶累及SVZ区与PFS具有相关性,这可能与SVZ包含大量的神经原始细胞及肿瘤干细胞,而肿瘤干细胞具有自我更新、无限繁殖、多向分化和迁移特性有关[18]。2016年世界卫生组织(World Health Organization,WHO)[19]根据治疗方案和预后效果的差异,增加了胶质瘤的分子类型,特别是分子标志物的状态,其中IDH野生型患者易复发导致整体生存率低,MGMT甲基化可以增加化疗的敏感性,是胶质母细胞瘤患者重要的预后指标,这可能是本组资料中胶质瘤复发与假性进展患者间IDH和MGMT基因型差异有统计学意义的原因。两组间性别、年龄、术前KPS评分、WHO分级、放疗、化疗等差异均不具有统计学意义,可能与样本量太少有关,今后需要加大样本量。

3.1 DKI在鉴别胶质瘤复发与假性进展患者增强病灶中的差异分析

       胶质瘤复发与假性进展均会在增强MRI检查中出现异常或增大的团块状、结节状、环状等强化病灶,病理学上,胶质瘤复发时增强病灶的特征可能是肿瘤细胞增殖和血管增生等[20],而假性进展可能与放、化疗诱导的组织炎症、血管扩张、血脑屏障障碍有关[21, 22]。rMK值与水分子扩散受限程度成正比,rMD值与水分子扩散受限程度成反比[15],本组资料显示复发患者较假性进展患者的增强病灶rMK值升高呈高信号(伪彩图呈红色,图1),rMD值降低呈低信号(伪彩图呈绿色,图1),表明胶质瘤复发时肿瘤核异形明显、细胞外间隙减少,水分子运动更受限。不同的是,rMK值诊断效能高于rMD值,由于复发时肿瘤细胞增殖使得水分子扩散成非高斯分布,rMD只反映呈高斯分布的水分子[23],rMK可以很好地描述复发微环境中非高斯分布的水分子[15,24]。本研究中胶质瘤复发与假性进展患者增强病灶的组间rRK值差异有统计学意义,可能原因为rRK主要反映径向扩散的水分子运动,复发病灶较假性进展病灶的水分子径向受限程度更高[9,14]。与以往研究[25, 26]不同的是,本研究中两组间rAK值、rFA值差异无统计学意义,可能是样本量太小,rAK检测水分子轴向扩散的变化值以及rFA值显示纤维束受损的程度不具有统计学意义,今后需要加大样本量来证实这一结果。单变量Cox分析表明rMK、rRK和rMD与PFS有一定的相关性,但多变量分析表明增强病灶rMK值是胶质瘤PFS的独立危险因素,因为rMK是DKI序列最有代表的参数,它可以真实反映复发与假性进展增强病灶的肿瘤微环境间差异。

3.2 DKI在鉴别胶质瘤复发与假性进展患者瘤周水肿中的差异分析

       目前临床主要采用贝伐单抗类等抗血管内皮生长因子药物治疗胶质瘤复发患者[27],该药物导致部分复发的病灶不强化,最终造成假阴性的结果。故RANO指南T2 FLAIR序列显示瘤周水肿的体积增加也可能表明肿瘤的进展[28],因为瘤周水肿促进侵袭相关的细胞基质及黏附分子的运动加强,从而加快肿瘤的生长扩散,同时脑水肿渗出的蛋白为肿瘤生长提供基质和空间,导致胶质瘤术后的高复发率[29]。同时瘤周水肿范围增加也继发于假性进展患者中[30]。故通过DKI序列鉴别胶质瘤复发与假性进展患者瘤周水肿的差异具有重要的临床意义。本组资料T2 FLAIR序列显示复发患者与假性进展患者均出现瘤周水肿,统计发现复发患者的瘤周水肿中rMK值增高呈高信号(伪彩图呈红色,图1)、rMD值降低呈低信号(伪彩图呈绿色,图1),表明复发患者的瘤周水肿区微观结构比假性进展患者的瘤周水肿区微环境结构更复杂,同以往文献报道[15,31],因为复发患者的瘤周水肿中,肿瘤细胞的增殖以及复发病灶使得血脑屏障破坏而导致血管源性水肿并伴肿瘤细胞浸润,而假性进展患者病灶周围水肿反映了炎症反应,即毛细血管通透性增加导致的血管源性水肿而无肿瘤细胞的浸润[32]。复发与假性进展患者瘤周水肿的rMK值比rMD值具有更高的诊断效能,表明rMK值可以敏感地反映复发时瘤周水肿浸润相关的微环境,单变量Cox分析表明rMK和rMD与PFS有一定的相关性。rRK值、rAK值、rFA值在复发与假性进展组间的瘤周水肿中差异均无统计学意义,可能是瘤周水肿尚未对大脑白质的轴突、髓鞘及纤维束造成进一步破坏,但不除外DKI序列基于平面回波成像在信号读取中易受磁敏感效应的影响,最终导致数据测量时出现较大的偏倚。

       目前的研究存在局限性。第一,本研究收集的总体病例数较少,进一步调查可能需要更大的样本量。第二,ROI可能会受到位置或组织成分偏差的影响,最终导致所测得的具体数值出现偏差,为了尽量减少这种偏差,我们绘制了三次ROI并计算三次测量的平均值。

       综上所述,DKI技术在鉴别胶质瘤复发与假性进展中具有较好的敏感度和特异度,其中参数MK值的诊断效能最高,今后有可能作为较好的影像学标记,其中增强病灶的MK值是PFS的独立危险因素。

[1]
国家卫生健康委员会医政医管局. 脑胶质瘤诊疗规范(2018年版)[J]. 中华神经外科杂志, 2019, 35(3): 217-239. DOI: 10.3760/cma.j.issn.1001-2346.2019.03.001.
Medical Administration and Hospital Administration Bureau of the National Health Commission. Guidelines for the diagnosis and treatment of glioma (2018 edition)[J]. Chin J Neurosurg, 2019, 35(3): 217-239. DOI: 10.3760/cma.j.issn.1001-2346.2019.03.001.
[2]
Strauss SB, Meng A, Ebani EJ, et al. Imaging glioblastoma posttreatment: progression, pseudoprogression, pseudoresponse, radiation necrosis[J]. Radiol Clin North Am, 2019, 57(6): 1199-1216. DOI: 10.1016/j.rcl.2019.07.003.
[3]
Zikou A, Sioka C, Alexiou GA, et al. Radiation necrosis, pseudoprogression, pseudoresponse, and tumor recurrence: imaging challenges for the evaluation of treated gliomas[J]. Contrast Media Mol Imaging, 2018, 2018: 6828396. DOI: 10.1155/2018/6828396.
[4]
Kruser TJ, Mehta MP, Robins HI. Pseudoprogression after glioma therapy: a comprehensive review[J]. Expert Rev Neurother, 2013, 13(4): 389-403. DOI: 10.1586/ern.13.7.
[5]
Lu VM, Jue TR, McDonald KL, et al. The survival effect of repeat surgery at glioblastoma recurrence and its trend: a systematic review and meta-analysis[J]. World Neurosurg, 2018, 115: 453-459.e3. DOI: 10.1016/j.wneu.2018.04.016.
[6]
Prager AJ, Martinez N, Beal K, et al. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence[J]. AJNR Am J Neuroradiol, 2015, 36(5): 877-885. DOI: 10.3174/ajnr.A4218.
[7]
Li Y, Ma YQ, Wu ZJ, et al. Advanced imaging techniques for differentiating pseudoprogression and tumor recurrence after immunotherapy for glioblastoma[J]. Front Immunol, 2021, 12: 790674. DOI: 10.3389/fimmu.2021.790674.
[8]
Ma B, Blakeley JO, Hong XH, et al. Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas[J]. J Magn Reson Imaging, 2016, 44(2): 456-462. DOI: 10.1002/jmri.25159.
[9]
Wang S, Martinez-Lage M, Sakai Y, et al. Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI[J]. AJNR Am J Neuroradiol, 2016, 37(1): 28-36. DOI: 10.3174/ajnr.A4474.
[10]
郭子玄, 朱西琪. 酰胺质子转移成像应用的研究进展[J]. 医学影像学杂志, 2021, 31(10): 1789-1792.
Guo ZX, Zhu XQ. The progress of applications of amide proton transfer[J]. J Med Imaging, 2021, 31(10): 1789-1792.
[11]
潘锋, 吴晓, 苏中周, 等. 3D-ASL技术联合DWI在鉴别脑胶质瘤术后复发与假性进展中的应用价值[J]. 临床放射学杂志, 2018, 37(6): 904-908. DOI: 10.13437/j.cnki.jcr.2018.06.003.
Pan F, Wu X, Su ZZ, et al. 3D-ASL and DWI in the differential diagnosis of recurrent glioma and pseudo progression[J]. J Clin Radiol, 2018, 37(6): 904-908. DOI: 10.13437/j.cnki.jcr.2018.06.003.
[12]
Takahashi S, Takahashi W, Tanaka S, et al. Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging[J]. Int J Radiat Oncol Biol Phys, 2019, 105(4): 784-791. DOI: 10.1016/j.ijrobp.2019.07.011.
[13]
Gao EY, Gao AK, Kit Kung W, et al. Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements[J]. Eur J Radiol, 2022, 147: 110104. DOI: 10.1016/j.ejrad.2021.110104.
[14]
Falk Delgado A, Nilsson M, van Westen D, et al. Glioma grade discrimination with MR diffusion kurtosis imaging: a meta-analysis of diagnostic accuracy[J]. Radiology, 2018, 287(1): 119-127. DOI: 10.1148/radiol.2017171315.
[15]
Zhao J, Wang YL, Li XB, et al. Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status[J]. J Neurooncol, 2019, 141(1): 195-203. DOI: 10.1007/s11060-018-03025-7.
[16]
Figini M, Riva M, Graham M, et al. Prediction of isocitrate dehydrogenase genotype in brain gliomas with MRI: single-shell versus multishell diffusion models[J]. Radiology, 2018, 289(3): 788-796. DOI: 10.1148/radiol.2018180054.
[17]
Wen PY, MacDonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group[J]. J Clin Oncol, 2010, 28(11): 1963-1972. DOI: 10.1200/jco.2009.26.3541.
[18]
Jiang HH, Yu KF, Li MX, et al. Classification of progression patterns in glioblastoma: analysis of predictive factors and clinical implications[J]. Front Oncol, 2020, 10: 590648. DOI: 10.3389/fonc.2020.590648.
[19]
白洁, 程敬亮, 高安康, 等. 2016年WHO中枢神经系统肿瘤分类解读[J]. 中华放射学杂志, 2016, 50(12): 1000-1005. DOI: 10.3760/cma.j.issn.1005-1201.2016.12.025.
Bai J, Cheng JL, Gao AK, et al. Ankang.Interpretation of the 2016 WHO classification of tumors of the central nervous system[J]. Chin J Radiol, 2016, 50(12): 1000-1005. DOI: 10.3760/cma.j.issn.1005-1201.2016.12.025.
[20]
Lee J, Wang N, Turk S, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning[J]. Sci Rep, 2020, 10(1): 20331. DOI: 10.1038/s41598-020-77389-0.
[21]
Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors[J]. J Magn Reson Imaging, 2018, 48(3): 571-589. DOI: 10.1002/jmri.26171.
[22]
Balaña C, Capellades J, Pineda E, et al. Pseudoprogression as an adverse event of glioblastoma therapy[J]. Cancer Med, 2017, 6(12): 2858-2866. DOI: 10.1002/cam4.1242.
[23]
Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study[J]. Eur J Radiol, 2019, 112: 144-152. DOI: 10.1016/j.ejrad.2019.01.014.
[24]
Wu XF, Liang X, Wang XC, et al. Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging[J]. Eur J Radiol, 2021, 135: 109445. DOI: 10.1016/j.ejrad.2020.109445.
[25]
Abdalla G, Dixon L, Sanverdi E, et al. The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis[J]. Neuroradiology, 2020, 62(7): 791-802. DOI: 10.1007/s00234-020-02425-9.
[26]
Li SR, Zheng Y, Sun WB, et al. Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging[J]. Eur Radiol, 2021, 31(11): 8197-8207. DOI: 10.1007/s00330-021-07959-x.
[27]
Kim MM, Umemura Y, Leung D. Bevacizumab and glioblastoma: past, present, and future directions[J]. Cancer J, 2018, 24(4): 180-186. DOI: 10.1097/PPO.0000000000000326.
[28]
Gao XY, Wang YD, Wu SM, et al. Differentiation of treatment-related effects from glioma recurrence using machine learning classifiers based upon pre-and post-contrast T1WI and T2 FLAIR subtraction features: a two-center study[J]. Cancer Manag Res, 2020, 12: 3191-3201. DOI: 10.2147/CMAR.S244262.
[29]
Lee J, Wang N, Turk S, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning[J]. Sci Rep, 2020, 10(1): 20331. DOI: 10.1038/s41598-020-77389-0.
[30]
Patel SH, Poisson LM, Brat DJ, et al. T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project[J]. Clin Cancer Res, 2017, 23(20): 6078-6085. DOI: 10.1158/1078-0432.CCR-17-0560.
[31]
Li FY, Shi WQ, Wang DW, et al. Evaluation of histopathological changes in the microstructure at the center and periphery of glioma tumors using diffusional kurtosis imaging[J]. Clin Neurol Neurosurg, 2016, 151: 120-127. DOI: 10.1016/j.clineuro.2016.10.018.
[32]
Ismail M, Hill V, Statsevych V, et al. Shape features of the lesion habitat to differentiate brain tumor progression from pseudoprogression on routine multiparametric MRI: a multisite study[J]. AJNR Am J Neuroradiol, 2018, 39(12): 2187-2193. DOI: 10.3174/ajnr.A5858.

上一篇 多模态MRI在复发缓解型多发性硬化深部灰质的应用
下一篇 MR影像组学在鉴别腮腺良恶性肿瘤中的临床应用价值
  
诚聘英才 | 广告合作 | 免责声明 | 版权声明
联系电话:010-67113815
京ICP备19028836号-2