分享:
分享到微信朋友圈
X
临床研究
磁共振扩散峰度成像评价宫颈癌放疗早期疗效的应用价值
郑祥 沈芳敏 郑德春 陈文娟

Cite this article as: ZHENG X, SHEN F M, ZHENG D C, et al. The application of magnetic resonance diffusion kurtosis imaging in efficacy evaluation of early radiotherapy of cervical carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(2): 68-72, 82.本文引用格式:郑祥, 沈芳敏, 郑德春, 等. 磁共振扩散峰度成像评价宫颈癌放疗早期疗效的应用价值[J]. 磁共振成像, 2023, 14(2): 68-72, 82. DOI:10.12015/issn.1674-8034.2023.02.012.


[摘要] 目的 探讨磁共振扩散峰度成像(diffusion kurtosis imaging, DKI)评估宫颈癌放疗早期疗效的应用价值。材料与方法 本研究瞻性纳入经病理证实为宫颈癌的患者21例,年龄(57.24±10.35)岁,其中宫颈鳞癌19例、腺癌1例、腺鳞癌1例。全部被试均于放疗前、放疗10次以及放疗结束同日采用3.0 T磁共振进行常规序列和DKI序列扫描。放疗疗效评判根据实体瘤疗效评价标准(Response Evaluation Criteria in Solid Tumors, RECIST)1.1版本,对放疗10次时间点疗效进行评估。其中完全缓解(complete remission, CR)和部分缓解(partial remission, PR)为有效组,疾病稳定(stable disease, SD)和疾病进展(progressive disease, PD)为无效组。分析对比两组肿瘤放疗过程不同阶段DKI各参数的变化,并绘制DKI各参数的受试者工作特征(receiver operating characteristic, ROC)曲线。结果 相较于放疗前的数值,放疗后病灶平均扩散系数(mean diffusivity, MD)值增高(P<0.001),平均扩散峰度(mean kurtosis, MK)值降低(P=0.019)。此外,有效组在放疗前的MD值较无效组高(P=0.016),放疗后则无显著差异(P>0.05)。有效组MK放疗前后较无效组低(P<0.05)。放疗前的MD和MK预测放疗早期疗效的曲线下面积(area under the curve, AUC)分别为0.817和0.822,放疗前的MD和MK联合预测放疗早期疗效的AUC为0.923,三者的AUC没有显著差异(Z=1.264,P=0.206)。但是联合诊断的约登指数较大,即敏感度(87.5%)和特异度(92.3%)较高。结论 DKI具有预测宫颈癌放疗早期疗效的能力,并且联合应用MD和MK比单独应用具有更好的预测能力。
[Abstract] Objective To investigate the application value of MR diffusion kurtosis imaging (DKI) in evaluating the early efficacy of radiotherapy for cervical cancer.Materials and Methods The study included 21 patients with age of (57.24±10.35) years old. All patients were pathologically confirmed as cervical cancer, including 19 cases of cervical squamous cell carcinoma, one case of adenocarcinoma and one case of adenosquamous carcinoma. All patients underwent conventional MRI and DKI scanning with 3.0 T MR machine before radiotherapy, 10 fractions of radiotherapy and the same day after radiotherapy. The efficacy of radiotherapy was evaluated at 10 fractions according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST version 1.1). Among them, complete remission (CR) and partial remission (PR) were response groups. Stable disease (SD) and progressive disease (PD) were the non-response groups. The changes of each parameter of DKI at different stages of radiotherapy were analyzed and compared between the two groups and the receiver operating characteristic (ROC) curve of each parameter of DKI was drawn.Results Compared with the values before radiotherapy, the mean diffusivity (MD) values of lesions after radiotherapy were increased (P<0.001), and the mean kurtosis (MK) values were decreased (P=0.019) after radiotherapy. In addition, the response group had a significantly higher MD value than the non-response group before radiotherapy (P=0.016), but there was no significant difference after radiotherapy (P>0.05). The response group had a significantly lower MK level than the non-response group before and after radiotherapy (P<0.05). Before radiotherapy the area under the curve (AUC) of MD and MK for predicting the early efficacy of radiotherapy was 0.817 and 0.822 respectively, while the AUC of the combination of MD and MK was 0.923, and there was no significant difference in the AUC of the three methods (Z=1.264, P=0.206). However, the Youden index of combined diagnosis was higher, that is, the sensitivity (87.5%) and specificity (92.3%) were higher.Conclusions DKI has the ability to predict the early efficacy of radiotherapy for cervical carcinoma, and the combination of MD and MK has a better predictive ability than single application.
[关键词] 宫颈癌;磁共振成像;扩散峰度成像;平均扩散系数;平均扩散峰度;放射治疗;疗效评估
[Keywords] cervical cancer;magnetic resonance imaging;diffusion kurtosis imaging;mean diffusivity;mean kurtosis;radiation therapy;efficacy evaluation

郑祥 1*   沈芳敏 1   郑德春 1   陈文娟 2  

1 福建医科大学肿瘤临床医学院,福建省肿瘤医院放诊科,福州 350014

2 福建医科大学肿瘤临床医学院,福建省肿瘤医院妇科,福州 350014

*通信作者:郑祥,E-mail:skipskip@sina.com

作者贡献声明::郑祥设计本研究的方案,起草和撰写稿件,获取、分析或解释本研究的数据,对稿件重要的智力内容进行了修改,并获得了福建省自然科学基金基金和福建省肿瘤医院院内基金的资助;沈芳敏、郑德春、陈文娟获取、分析或解释本研究的数据,对稿件重要的智力内容进行了修改;全体作者都同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确性和诚信。


基金项目: 福建省自然科学基金 2021J01426 福建省肿瘤医院院内资助项目 2021YN13
收稿日期:2022-10-08
接受日期:2023-02-01
中图分类号:R445.2  R737.33 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2023.02.012
本文引用格式:郑祥, 沈芳敏, 郑德春, 等. 磁共振扩散峰度成像评价宫颈癌放疗早期疗效的应用价值[J]. 磁共振成像, 2023, 14(2): 68-72, 82. DOI:10.12015/issn.1674-8034.2023.02.012.

0 前言

       宫颈癌是最常见的妇科恶性肿瘤之一,威胁着全世界女性的生命健康。据报道,2022年我国预计新增宫颈癌患者11.2万例,死亡6.1万例[1]。对于失去手术机会的宫颈癌患者,放疗是其主要治疗手段。随着计算机技术及影像学的快速发展,调强放疗(intensity modulated radiation therapy, IMRT)已经成为宫颈癌放疗的主要手段。然而仍有一部分患者病灶对射线相对不敏感,成为进一步提高宫颈癌放疗效果的瓶颈。也正因如此,宫颈癌总的五年生存率一直徘徊在48%~51%左右[2]。由此可知及时评估宫颈癌放疗疗效将有利于更好地预测患者的预后,指导临床医师对患者采取个性化治疗方案,从而提高宫颈癌的放疗效果以及患者的生存率。

       扩散峰度成像(diffusion kurtosis imaging, DKI)是在传统扩散加权成像(diffusion weighted imaging, DWI)基础上发展而来的扩散成像新序列。该序列基于水分子扩散运动呈非高斯分布的假设[3, 4],由于非高斯分布更符合水分子在人体内的实际扩散方式,因此DKI可提供更为精确的信息[5, 6]。DKI具有多个参数值,其中平均扩散系数(mean diffusivity, MD)值为组织真实扩散系数,比DWI的表观扩散系数(apparent diffusion coefficient, ADC)更准确反映水分子在肿瘤组织内的实际扩散情况[7];平均扩散峰度(mean kurtosis, MK)值则代表组织各向同性程度的参数,反映肿瘤微环境情况[8]。因此DKI能够较好评估肿瘤细胞密度、细胞外间隙等微环境改变[9, 10]。研究发现DKI在宫颈癌病理学分型、分级以及分期方面具有一定的应用价值[11, 12, 13],在宫颈癌淋巴脉管间隙侵犯的预测上也具有较好的提示意义[14]。目前已有动物实验证实了DKI评估宫颈癌放疗效果的可行性[15],但是关于DKI在宫颈癌患者放疗疗效评估方面的临床研究则未见报道。

       因此,本研究用DKI对宫颈癌患者放疗前、放疗早期(放疗10次)以及放疗后三个时间点进行扫描,计算DKI参数MD和MK在放疗过程中的变化情况与患者早期放疗疗效,旨在探讨利用DKI早期评估宫颈癌放疗疗效的应用价值。

1 材料与方法

1.1 研究对象

       本前瞻性研究遵守《赫尔辛基宣言》,并经福建省肿瘤医院伦理委员会批准(批文号:SQ2021-075-01),所有被试均签订知情同意书,所有被试信息均被完全保密。纳入标准:(1)初诊、经活检病理确诊为宫颈癌的患者;(2)国际妇产科联盟(Federation International of Gynecology and Obstetrics, FIGO)分期为Ⅰb2、Ⅱb~Ⅲc期;(3)未经任何抗肿瘤治疗。排除标准:(1)具有MRI扫描禁忌证或钆对比剂过敏;(2)不能配合完成MRI检查的患者;(3)治疗中途失访的患者。本研究包括21例经病理学证实为宫颈癌并未进行任何抗肿瘤治疗的患者,年龄44~82岁,中位年龄55岁。根据FIGO分期标准[16],患者中包括Ⅰb2期2例、Ⅱb期3例,Ⅲa期4例,Ⅲb期5例、Ⅲc期7例。病理学分型上包括腺癌1例、腺鳞癌1例、鳞癌19例。

1.2 放疗方案

       本研究采用大孔径4D-CT定位模拟机(Brilliance CT Big Bore,飞利浦,荷兰)对所有患者进行图像扫描,范围上界为T12椎体下缘,下界为外阴下3 cm,扫描层厚5 mm;将扫描数据传至Oncentra系统(Nucletron BV,荷兰)进行靶区勾画,然后运用飞利浦Pinacle3治疗计划系统进行宫颈癌旋转容积调强(volumetric modulated arc therapy, VMAT)技术计划设计:逆时针180°~181°及顺时针181°~180°,共2个弧。使用核通6X直线加速器放疗,宫颈计划靶区(planning target volume, PTV)≥95%。统一处方剂量为4860 cGy,每次180 cGy,分27次进行。危及器官限定条件:胃V45(45 Gy等剂量曲线所包绕的靶区体积,依此类推)<15%,脊髓V40<0%,肾脏V20<30%,肝脏V35<50%,膀胱V50<50%,直肠V50<30%,乙状结肠V50<50%,小肠V40<40%,股骨颈V50<5%,髂骨骨髓V40<40%。所有患者在放疗过程中均未接受其他形式的治疗。

1.3 MRI扫描

       所有被试均于放疗前、放疗10次以及放疗结束后行3.0 T MRI(Discovery MR 750w,GE,美国)检查,使用GEM BODY线圈,扫描序列包括常规序列和DKI序列。轴位T1WI序列扫描参数:使用快速扰相梯度双回波(fast spoiled gradient-recalled dual echo)序列,TR/TE 2.2/4.1 ms,层厚6 mm,层间距-3 mm(内插),视野480 mm×384 mm,矩阵320×256;轴位T2WI序列扫描参数:使用快速自旋回波(fast spin echo, FSE)序列,TR/TE 5078/90 ms,层厚4 mm,层间距0.5 mm,视野22 mm×22 mm,矩阵320×320;轴位DKI序列扫描参数:选取5个b值(0、500、1000、1500和2000 s/mm2)和15个方向,TR/TE 3600/81 ms,层厚4 mm,层间距0 mm,视野24 mm×24 mm,矩阵96×96。扫描时间3 min 50 s。

1.4 影像学分析

       所有影像学分析均于GE磁共振工作站AW Volume Share 5进行,由两名放射科医师(副主任医师,工作年限>15年)共同判定图像质量是否合格,以及是否可以进行数据测量。选取层面以病灶最大截面为中心,在T2WI形态学图像上采用手动勾画感兴趣区(region of interest, ROI)的方法沿着病灶周边进行ROI的勾画。所勾画的ROI尽量与病灶边缘适形,并尽量避开病灶内液化坏死区,然后将所勾画的ROI依次拷贝至D图、K图进行分析运算并自动生成相应的DKI参数值。随后在中心层面上下同等间隔各取一个层面进行测值,将三个层面的参数取平均值。参数MK是一个无单位数值,表示组织水分子扩散偏离正态分布的程度。K=0表示完全正态分布,K越大,表示偏离正态分布越明显。参数MD则是校正过的ADC值,表示非正态分布下水分子的扩散系数。

1.5 患者疗效评估

       根据实体肿瘤的疗效评价标准(Response Evaluation Criteria in Solid Tumors, RECIST)1.1版[17]。完全缓解(complete remission, CR):所有靶病灶消失;部分缓解(partial remission, PR):靶病灶直径之和比基线水平减少至少30%;疾病进展(partial remission, PR):以整个实验研究过程中所有测量的靶病灶直径之和的最小值为参照,直径和相对增加至少20%(如果基线测量值最小就以基线值为参照),除此之外,必须满足直径和的绝对值增加至少5 mm(出现一个或多个新病灶也视为疾病进展);疾病稳定(stable disease, SD):靶病灶减小的程度没达到PR,增加的程度也没达到PD水平,介于两者之间。根据以上标准,对入组的21名患者放疗10次后的放疗效果进行评估。将CR和PR的患者归入早期有效组(early response group, eRG),SD和PD的患者归入早期无效组(early non response group, eNRG)。

1.6 统计学分析

       统计学分析使用MedCalc 20.010和SPSS 20.0统计学软件进行,分为两个部分。第一部分是DKI参数动态变化情况分析。利用单因素方差分析(ANOVA)检测所有患者MD和MK在放疗前后的变化差异是否具有统计学意义;独立样本t检验分析放疗早期不同疗效患者MD和MK差异是否具有统计学意义。第二部分为检测MD和MK参数对于宫颈癌患者早期放疗效果预测能力。利用受试者工作特征(receiver operating characteristic, ROC)曲线检验分析MD、MK在10次放疗前后参数值的曲线下面积(area under the curve, AUC),以及MD放疗前联合MK放疗前的AUC以及各自的敏感度、特异度和约登指数。DeLong检验比较不同ROC曲线AUC的差异。P<0.05为差异具有统计学意义。

2 结果

2.1 宫颈癌病灶放疗前后大小变化及早期疗效评估

       所有宫颈癌患者接受放疗后病灶均出现不同程度退缩,且放疗前、放疗10次以及放疗后病灶总体退缩差异具有统计学意义(P<0.001)(表1)。根据RECIST标准,本研究患者归为eRG 13人,eNRG 8人。

表1  宫颈癌患者放疗过程中病灶长径及DKI参数的变化(n=21)
Tab. 1  Changes of lesion's diameters and DKI parameters of cervical cancer during radiotherapy (n=21)

2.2 宫颈癌DKI影像学参数在放疗前后的变化情况

       定义放疗前MD值为MD前、放疗10次为MD早期、放疗后为MD后、放疗10次与放疗前的变化值为MD变化,MK值以此类推。本研究中所有组别的MD和MK经Kolmogorov-Smirnov检验均符合正态分布。放疗后DKI参数MD以及MK变化详见表1,其中MD值在放疗10次后即出现显著升高(P<0.001),MK值出现显著降低(P=0.019)。eRG患者MD前显著高于eNRG患者(P=0.016)、而MD早期以及MD后则无显著差异(P=0.101和0.082)。eRG患者MK前、MD早期以及MD后则均显著低于eNRG患者(P=0.012、0.025和0.007)(表2)。图1所示为两例不同FIGO分期宫颈癌患者放疗前后病灶大小以及DKI参数的变化情况。

图1  两例不同国际妇产科联盟(FIGO)分期宫颈癌患者放疗前后病灶扩散峰度成像(DKI)参数变化情况。1A:女,58岁,宫颈鳞癌Ⅲc1r期,扩散加权成像(DWI)图病灶显示为不均匀高信号,放疗10次及放疗后病灶较前退缩,信号较前降低;平均扩散系数(MD)伪彩图显示病灶区放疗前、放疗10次和放疗后MD值分别为1.112×10-3 mm2/s、1.813×10-3 mm2/s和2.516×10-3 mm2/s;平均扩散峰度(MK)值分别为0.783、0.693和0.512。1B:女,55岁,宫颈鳞癌Ⅰb2期,DWI图病灶显示为不均匀高信号,放疗10次及放疗后病灶较前退缩,信号较前降低;MD伪彩图显示病灶区放疗前、放疗10次和放疗后MD值分别为1.315×10-3 mm2/s、1.544×10-3 mm2/s和1.616×10-3 mm2/s;MK值分别为0.879、0.635和0.638。
Fig 1  Changes of diffusion kurtosis imaging (DKI) parameters during radiotherapy in two patients with cervical cancer of different Federation International of Gynecology and Obstetrics (FIGO) stages. 1A: A 58 years old woman with stage Ⅲc1r cervical squamous cell carcinoma. Lesions show uneven hyperintensity in diffusion weighted imaging (DWI) picture, they shrink and the signal decrease early and after radiotherapy. The pseudo-color map of mean diffusivity (MD) show that the MD values of the lesion area are 1.112×10-3 mm2/s, 1.813×10-3 mm2/s and 2.516×10-3 mm2/s before, during and after radiotherapy, respectively. The mean kurtosis (MK) values are 0.783, 0.693 and 0.512, respectively. 1B: A 55 years old woman with stage Ⅰb2 cervical squamous cell carcinoma. Lesions show uneven hyperintensity in DWI picture, they shrink and the signal decrease early and after radiotherapy. The pseudo-color map of MD show that the MD values are 1.315×10-3 mm2/s, 1.544×10-3 mm2/s and 1.616×10-3 mm2/s before, during and after radiotherapy, respectively; MK values are 0.879, 0.635 and 0.638, respectively.
表2  放疗早期不同疗效宫颈癌患者放疗过程中DKI参数的变化
Tab. 2  Changes of DKI parameters of cervical cancer patients with different therapeutic effects in the early stage of radiotherapy during radiotherapy

2.3 DKI参数预测宫颈癌放疗早期效果的效能情况

       ROC检测显示,放疗前和放疗10次参数MD和MK以及两个时间点的参数变化值以MD前和MK前的AUC最大,相应值分别为0.817和0.822,均高于其他影像学指标(图2)。MD前的敏感度高于MK前,而MK前的特异度高于MD前(表3)。MD前联合MK前的AUC则高于单独MD或MK的AUC(表3图2),虽然DeLong检验显示差异没有显著性(Z=1.264,P=0.206),但是其约登指数明显高于单独指标(表3),即其敏感度和特异度较之于单一指标有了明显均衡性提高(敏感度87.5%,特异度92.3%)。

图2  平均扩散系数(MD)和平均扩散峰度(MK)以及联合MD和MK的受试者工作特征(ROC)曲线。2A:放疗前、放疗10次的MD以及二者的MD差值所绘制的ROC曲线图;2B:放疗前、放疗10次的MK以及二者的MK差值所绘制的ROC曲线图;2C:放疗前MD、MK以及放疗前MD联合放疗前MK所绘制的ROC曲线图。
Fig. 2  Receiver operating characteristic (ROC) curves of mean diffusivity (MD) and mean kurtosis (MK) and the combination of MD and MK. 2A: ROC curves graph of MD before and after 10 times of radiotherapy and ∆MD; 2B: ROC curves plots of MK before and after 10 times of radiotherapy and ∆MK; 2C: ROC curves plots of MD and MK before radiotherapy and the combination of MD and MK before radiotherapy.
表3  DKI参数预测宫颈癌放疗早期(放疗10次)效果的诊断效能比较
Tab. 3  Comparison of the diagnostic efficacy of DKI parameters in predicting the early effect of radiotherapy (10 radiotherapy sessions) for cervical cancer

3 讨论

       本研究利用DKI对宫颈癌患者放疗早期的疗效进行评估,我们发现放疗有效组宫颈癌患者放疗前的DKI参数(MD、MK)与放疗无效组之间存在显著的差异,而放疗后两组之间的MK也存在显著的差异。放疗前的MD和MK值在预测宫颈癌早期放疗疗效方面具有一定的应用价值,且二者联合应用效果更好。

3.1 DKI与肿瘤疗效评估

       2022版宫颈癌美国国立综合癌症网络(National Comprehensive Cancer Network, NCCN)指南明确提出宫颈癌原发灶的影像学评估首选MRI检查[18]。然而作为一种基于形态学的检查,常规的MRI只能等病症体积上出现变化之后才能检测相应的变化,对于病灶体积改变之前,灶内微环境以及微结构的变化却无能为力。因此如果能够在病灶体积变化之前就检测出病灶对放疗的反应程度,就可以弥补常规MRI形态学检查的不足,更及时为临床医师提供准确的信息,利于针对患者进行个性化治疗方案制订,将有利于提高宫颈癌的总体治疗效果。

       现有临床研究发现宫颈癌放疗后病灶ADC值升高[19, 20]。此外,鼻咽癌裸鼠移植瘤放疗过程中DKI参数MD值整体呈现上升趋势,MK值呈现下降趋势[21]。通常放疗过程中肿瘤坏死将导致细胞外间隙增宽[22],进而导致水分子扩散受限程度降低,MD值升高;而放疗导致组织间质、细胞膜结构等产生破坏而使微环境复杂程度降低,即不均质性降低,MK值也跟着降低。本研究发现无论是早期放疗有反应还是无效组,其参数MD和MK的变化均出现类似的情况,与理论基础及基本预期相符合。

       与传统DWI不同,DKI以水分子在体内扩散遵循非高斯分布为假设,这种假设更符合水分在体内的实际扩散情况,因此DKI获得的拟合参数可以更准确地反映肿瘤微循环、细胞密度、细胞外间隙等。研究发现DKI鉴别直肠KRAS基因突变和肾的良恶性肿瘤具有比DWI更好的应用价值[23, 24]。这是因为D值反映的是水分子在组织中的真实扩散状况,比ADC更准确[25],而K值反映了肿瘤组织的各向同性情况。本研究中放疗早期有效组放疗前的MD值总体高于无效组,而MK值则放疗前后均高于无效组,这一结果正是基于以上的理论基础。参数MD和MK相结合能更为真实全面地反映肿瘤良恶性、侵袭性强弱等特点[26],从而更准确地判断肿瘤的治疗效果。

3.2 DKI预测宫颈癌放疗疗效

       有学者研究发现DKI参数MK值在肝癌组织学分级评估以及大鼠卵巢癌化疗疗效早期评估中均优于传统ADC值或形态学变化[27, 28],证明了DKI在肿瘤疗效评估中作为无创便捷的影像学评估指标具有比常规MRI和传统DWI更为明显的优势。肿瘤微环境极其复杂,影响因素不一而足[29]。MD值作为校正后的ADC值,其作用在各研究中均得到了证实,但是对于MK的作用则有不同的意见。部分研究者认为MK在肿瘤治疗前后的变化差异没有统计学意义[30, 31],部分则认为MK具有比MD更有价值[32, 33, 34]。这可能与不同研究的研究对象、干预措施以及DKI的参数不同有关,具体原因则需要我们进一步深入探究。LI等[35]发现MD联合MK鉴别膀胱癌肌层侵犯的能力均优于单独MD、MK以及ADC。GUO等[36]亦证实了联合磁共振体素内不相干运动(intravoxel incoherent motion, IVIM)序列的参数组织扩散系数(tissue diffusion, Dt)、灌注分数(perfusion fraction, fp)与DKI参数MK在评估骨恶性肿瘤放疗后血管生成变化的能力比任何单一参数均强。这说明多个参数联合使用具有比单一参数更为优秀的表现,这一结论与本研究的结论高度类似。本研究的ROC曲线显示放疗前MD和MK在预测宫颈癌放疗早期疗效的预测效能比其他时间点以及参数变化值均更高。而联合MD和MK指标的ROC曲线AUC比MD和MK单独的AUC大,提示联合应用两个指标的诊断效能比单独使用更高。虽然它们AUC差別并没有统计学意义,但是联合应用的敏感度和特异度显然比单独指标更为均衡。本项研究仍然存在一定局限性,主要是因为本次入组的数量相对较少,可能会使结果存在一定的偏差。我们将会在接下来的研究中进一步扩大入组患者数量,从而尽量减少因样本量导致的误差。

4 结论

       总之,我们认为DKI在宫颈癌患者早期放疗疗效预测方面具有较好的临床价值,有望为宫颈癌的个体化治疗提供新的疗效评估参考。而联合应用MD和MK则比单独应用这两个指标具有更好的预测能力。

[1]
XIA C F, DONG X S, LI H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants[J]. Chin Med J (Engl), 2022, 135(5): 584-590. DOI: 10.1097/CM9.0000000000002108.
[2]
YANG E, HUANG S Y, RAN X T, et al. The 5-year overall survival of cervical cancer in stage IIIC-r was little different to stage Ⅰ and Ⅱ: a retrospective analysis from a single center[J/OL]. BMC Cancer, 2021, 21(1): 203 [2022-10-05]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912513. DOI: 10.1186/s12885-021-07890-w.
[3]
GRANATA V, FUSCO R, BELLI A, et al. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when[J/OL]. Infect Agent Cancer, 2022, 17(1): 25 [2022-10-05]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185934. DOI: 10.1186/s13027-022-00441-3.
[4]
TAHA H T, CHAD J A, CHEN J J. DKI enhances the sensitivity and interpretability of age-related DTI patterns in the white matter of UK biobank participants[J]. Neurobiol Aging, 2022, 115: 39-49. DOI: 10.1016/j.neurobiolaging.2022.03.008.
[5]
GOGHARI V M, KUSI M, SHAKEEL M K, et al. Diffusion kurtosis imaging of white matter in bipolar disorder[J/OL]. Psychiatry Res Neuroimaging, 2021, 317: 111341 [2022-9-05]. https://www.sciencedirect.com/science/article/abs/pii/S0925492721000937. DOI: 10.1016/j.pscychresns.2021.111341.
[6]
DENG X Y, DUAN Z Q, FANG S B, et al. Advances in the application and research of magnetic resonance diffusion kurtosis imaging in the musculoskeletal system[J/OL]. J Magn Reson Imaging, 2022 [2022-10-07]. https://pubmed.ncbi.nlm.nih.gov/36200754. DOI: 10.1002/jmri.28463.
[7]
GUO J, DONG C, WU Z J, et al. Diffusion kurtosis imaging assessment of the response to radiotherapy in a VX2 bone tumor model: an animal study[J]. Acta Radiol, 2022, 63(2): 182-191. DOI: 10.1177/0284185121989519.
[8]
XIE S H, LANG R, LI B, et al. Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI[J]. Neuroradiology, 2023, 65(1): 55-64. DOI: 10.1007/s00234-022-03000-0.
[9]
PENG Q, TANG W, HUANG Y, et al. Diffusion kurtosis imaging: correlation analysis of quantitative model parameters with molecular features in advanced lung adenocarcinoma[J]. Chin Med J (Engl), 2020, 133(20): 2403-2409. DOI: 10.1097/CM9.0000000000001074.
[10]
LO Y C, LI T J T, LIN T C, et al. Microstructural evidence of neuroinflammation for psychological symptoms and pain in patients with fibromyalgia[J]. J Rheumatol, 2022, 49(8): 942-947. DOI: 10.3899/jrheum.211170.
[11]
HOU M Y, SONG K, REN J P, et al. Comparative analysis of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in evaluating the histological grade of cervical squamous carcinoma[J/OL]. BMC Cancer, 2022, 22(1): 87 [2022-10-05]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780242. DOI: 10.1186/s12885-022-09205-z.
[12]
WANG M D, PERUCHO J A U, CHAN Q, et al. Diffusion kurtosis imaging in the assessment of cervical carcinoma[J/OL]. Acad Radiol, 2020, 27(5): e94-e101 [2022-10-05]. https://pubmed.ncbi.nlm.nih.gov/31324577. DOI: 10.1016/j.acra.2019.06.022.
[13]
YAMADA I, OSHIMA N, WAKANA K, et al. Uterine cervical carcinoma: evaluation using non-Gaussian diffusion kurtosis imaging and its correlation with histopathological findings[J]. J Comput Assist Tomogr, 2021, 45(1): 29-36. DOI: 10.1097/RCT.0000000000001042.
[14]
MALEK M, RAHMANI M, POURASHRAF M, et al. Prediction of lymphovascular space invasion in cervical carcinoma using diffusion kurtosis imaging[J/OL]. Cancer Treat Res Commun, 2022, 31: 100559 [2022-10-05]. https://pubmed.ncbi.nlm.nih.gov/35460974. DOI: 10.1016/j.ctarc.2022.100559.
[15]
曹崑, 王帅, 赵博, 等. 磁共振扩散峰度成像评价宫颈癌放疗后变化的小鼠实验研究[J]. 磁共振成像, 2017, 8(9): 685-690. DOI: 10.12015/issn.1674-8034.2017.09.008.
CAO K, WANG S, ZHAO B, et al. MR diffusion kurtosis imaging versus standard diffusion imaging: changes with radiation in uterine cervical carcinoma xenografts[J]. Chin J Magn Reson Imaging, 2017, 8(9): 685-690. DOI: 10.12015/issn.1674-8034.2017.09.008.
[16]
BHATLA N, AOKI D, SHARMA D N, et al. Cancer of the cervix uteri: 2021 update[J]. Int J Gynaecol Obstet, 2021, 155(Suppl 1): 28-44. DOI: 10.1002/ijgo.13865.
[17]
徐微娜, 于丽娟, 杨之光, 等. 对比分析PERCIST1.0和RECIST1.1标准评价中晚期宫颈癌同步放化疗的治疗反应的初步研究[J]. 中国临床医学影像杂志, 2017, 28(10): 747-751. DOI: 10.3969/j.issn.1008-1062.2017.10.013.
XU W N, YU L J, YANG Z G, et al. Comparison of PERCIST1.0 and RECIST1.1 for the evaluation of concurrent chemoradiotherapy response in advanced cervical cancer[J]. J China Clin Med Imaging, 2017, 28(10): 747-751. DOI: 10.3969/j.issn.1008-1062.2017.10.013.
[18]
彭巧华, 吕卫国. 2022年第1版«NCCN子宫颈癌临床实践指南»解读[J]. 实用肿瘤杂志, 2022, 37(3): 205-214. DOI: 10.13267/j.cnki.syzlzz.2022.034.
PENG Q H, LV W G. Interpretation of NCCN guidelines for cervical cancer, version 1.2022[J]. J Pract Oncol, 2022, 37(3): 205-214. DOI: 10.13267/j.cnki.syzlzz.2022.034.
[19]
BU X H, ZHANG J, TIAN F Z, et al. Value of diffusion-weighted magnetic resonance imaging combined with miR-18a level in predicting radiosensitivity of cervical cancer[J]. Med Sci Monit, 2018, 24: 7271-7278. DOI: 10.12659/MSM.910990.
[20]
CUSUMANO D, RUSSO L, GUI B, et al. Evaluation of early regression index as response predictor in cervical cancer: a retrospective study on T2 and DWI MR images[J]. Radiother Oncol, 2022, 174: 30-36. DOI: 10.1016/j.radonc.2022.07.001.
[21]
ZHENG X, CHEN Y B, XIAO Y P, et al. Early diagnosis of radio-insensitive human nasopharyngeal carcinoma xenograft models by diffusion kurtosis imaging[J]. Magn Reson Imaging, 2019, 55: 128-132. DOI: 10.1016/j.mri.2018.08.001.
[22]
WANG J, HAN Y L, LI Y, et al. Targeting tumor physical microenvironment for improved radiotherapy[J/OL]. Small Methods, 2022, 6(11): e2200570 [2022-10-05]. https://onlinelibrary.wiley.com/doi/10.1002/smtd.202200570. DOI: 10.1002/smtd.202200570.
[23]
TANG C, LU G X, XU J M, et al. Diffusion kurtosis imaging and MRI-detected extramural venous invasion in rectal cancer: correlation with clinicopathological prognostic factors[J/OL]. Abdom Radiol (NY), 2022 [2023-01-06]. https://link.springer.com/article/10.1007/s00261-022-03782-0. DOI: 10.1007/s00261-022-03782-0.
[24]
CAO J F, LUO X, ZHOU Z M, et al. Comparison of diffusion-weighted imaging mono-exponential mode with diffusion kurtosis imaging for predicting pathological grades of clear cell renal cell carcinoma[J/OL]. Eur J Radiol, 2020, 130: 109195 [2022-10-05]. https://www.ejradiology.com/article/S0720-048X(20)30384-3. DOI: 10.1016/j.ejrad.2020.109195.
[25]
ZHANG A D, SU X H, WANG Y F, et al. Predicting the effects of radiotherapy based on diffusion kurtosis imaging in a xenograft mouse model of esophageal carcinoma[J/OL]. Exp Ther Med, 2021, 21(4): 327 [2023-01-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903468.
[26]
WANG W W, LV S Q, XUN J, et al. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer[J/OL]. Eur J Radiol, 2022, 154: 110392 [2022-10-05]. https://www.ejradiology.com/article/S0720-048X(22)00242-X. DOI: 10.1016/j.ejrad.2022.110392.
[27]
CAO L K, CHEN J, DUAN T, et al. Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade[J]. Quant Imaging Med Surg, 2019, 9(4): 590-602. DOI: 10.21037/qims.2019.02.14.
[28]
YUAN S J, QIAO T K, QIANG J W. Diffusion-weighted imaging and diffusion kurtosis imaging for early evaluation of the response to docetaxel in rat epithelial ovarian cancer[J/OL]. J Transl Med, 2018, 16(1): 340 [2022-09-05]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282389. DOI: 10.1186/s12967-018-1714-1.
[29]
KUMARI S, ADVANI D, SHARMA S, et al. Combinatorial therapy in tumor microenvironment: where do we stand?[J/OL]. Biochim Biophys Acta Rev Cancer, 2021, 1876(2): 188585 [2022-9-05]. https://www.sciencedirect.com/science/article/abs/pii/S0304419X21000822. DOI: 10.1016/j.bbcan.2021.188585.
[30]
郑德春, 张潇潇, 林浩, 等. 扩散峰度成像早期评估化学治疗鼻咽癌颈部淋巴结转移效果[J]. 中国医学影像技术, 2020, 36(2): 220-224. DOI: 10.13929/j.issn.1003-3289.2020.02.012.
ZHENG D C, ZHANG X X, LIN H, et al. Diffusion kurtosis imaging in early assessment on response of neoadjuvant chemotherapy for metastatic neck lymph nodes from nasopharyngeal carcinoma[J]. Chin J Med Imaging Technol, 2020, 36(2): 220-224. DOI: 10.13929/j.issn.1003-3289.2020.02.012.
[31]
YU J, XU Q, SONG J C, et al. The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Eur Radiol, 2017, 27(5): 1848-1857. DOI: 10.1007/s00330-016-4529-6.
[32]
MENG N, WANG X J, SUN J, et al. Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer[J]. Eur Radiol, 2020, 30(10): 5758-5767. DOI: 10.1007/s00330-020-06884-9.
[33]
LI X W, YANG L, WANG Q M, et al. Soft tissue sarcomas: IVIM and DKI correlate with the expression of HIF-1α on direct comparison of MRI and pathological slices[J]. Eur Radiol, 2021, 31(7): 4669-4679. DOI: 10.1007/s00330-020-07526-w.
[34]
TANG C L, QIN Y J, HU Q L, et al. Diagnostic value of multi-model high-resolution diffusion-weighted MR imaging in breast lesions: based on simultaneous multi-slice readout-segmented echo-planar imaging[J/OL]. Eur J Radiol, 2022, 154: 110439 [2022-10-05]. https://www.ejradiology.com/article/S0720-048X(22)00289-3. DOI: 10.1016/j.ejrad.2022.110439.
[35]
LI Q, CAO B H, TAN Q X, et al. Prediction of muscle invasion of bladder cancer: a comparison between DKI and conventional DWI[J/OL]. Eur J Radiol, 2021, 136: 109522 [2022-9-05]. https://linkinghub.elsevier.com/retrieve/pii/S0720-048X(21)00002-4. DOI: 10.1016/j.ejrad.2021.109522.
[36]
GUO J, SUN W K, DONG C, et al. Intravoxel incoherent motion imaging combined with diffusion kurtosis imaging to assess the response to radiotherapy in a rabbit VX2 malignant bone tumor model[J/OL]. Cancer Imaging, 2022, 22(1): 47 [2022-10-05]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9446876. DOI: 10.1186/s40644-022-00488-w.

上一篇 基于原发灶的磁共振ADC图、T2WI影像组学模型预测前列腺癌骨转移的价值
下一篇 基于临床-多参数磁共振影像组学特征预测宫颈癌脉管浸润和预后的研究
  
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