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基于T2加权成像及弥散加权成像的图像纹理分析预测宫颈鳞状细胞癌放化疗疗效的临床价值

苏佰燕 戚亚菲 管慧 何泳蓝 薛华丹 金征宇

苏佰燕, 戚亚菲, 管慧, 何泳蓝, 薛华丹, 金征宇. 基于T2加权成像及弥散加权成像的图像纹理分析预测宫颈鳞状细胞癌放化疗疗效的临床价值[J]. 协和医学杂志, 2021, 12(5): 713-720. doi: 10.12290/xhyxzz.2021-0380
引用本文: 苏佰燕, 戚亚菲, 管慧, 何泳蓝, 薛华丹, 金征宇. 基于T2加权成像及弥散加权成像的图像纹理分析预测宫颈鳞状细胞癌放化疗疗效的临床价值[J]. 协和医学杂志, 2021, 12(5): 713-720. doi: 10.12290/xhyxzz.2021-0380
SU Baiyan, QI Yafei, GUAN Hui, HE Yonglan, XUE Huadan, JIN Zhengyu. Texture Analysis of Sequential Images of T2-weighted Imaging and Diffusion-weighted Imaging for Predicting the Efficacy of Chemoradiotherapy in Cervical Squamous Cell Carcinoma[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 713-720. doi: 10.12290/xhyxzz.2021-0380
Citation: SU Baiyan, QI Yafei, GUAN Hui, HE Yonglan, XUE Huadan, JIN Zhengyu. Texture Analysis of Sequential Images of T2-weighted Imaging and Diffusion-weighted Imaging for Predicting the Efficacy of Chemoradiotherapy in Cervical Squamous Cell Carcinoma[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 713-720. doi: 10.12290/xhyxzz.2021-0380

基于T2加权成像及弥散加权成像的图像纹理分析预测宫颈鳞状细胞癌放化疗疗效的临床价值

doi: 10.12290/xhyxzz.2021-0380
基金项目: 

中国医学科学院中央级公益性科研院所基本科研业务费专项资金 2020-RW320-005

详细信息
    通讯作者:

    何泳蓝  电话:010-69159610,E-mail:heyonglan@pumch.cn

  • 中图分类号: R445; R737.33

Texture Analysis of Sequential Images of T2-weighted Imaging and Diffusion-weighted Imaging for Predicting the Efficacy of Chemoradiotherapy in Cervical Squamous Cell Carcinoma

Funds: 

Basic Research Operating Expenses of the Central Public Welfare Scientific Research Institute of Chinese Academy of Medical Sciences 2020-RW320-005

More Information
  • 摘要:   目的  探讨MRI检查T2加权成像(T2-weighted imaging, T2WI)及弥散加权成像(diffusion-weighted imaging, DWI)图像纹理参数与宫颈鳞状细胞癌放化疗疗效的相关性。  方法  回顾性纳入2015年2月至2016年1月北京协和医院接受放化疗的宫颈鳞状细胞癌患者,并根据其预后分为疾病进展组和疾病稳定组。采用TexRAD软件对两组患者放化疗前T2WI、DWI序列图像进行纹理分析,得到空间尺度滤波器(spatial scale filter,SSF)半径值为2、4、6的图像纹理参数。比较两组患者图像纹理参数差异,采用多因素Cox回归分析图像纹理参数与宫颈鳞状细胞癌患者放化疗疗效的相关性。采用受试者工作特征(receiver operating characteristic, ROC)曲线分析各图像纹理参数预测宫颈鳞状细胞癌放化疗后疾病进展的性能。  结果  共121例符合纳入和排除标准的宫颈鳞状细胞癌患者入选本研究。其中疾病进展组46例,疾病稳定组75例。T2WI序列图像中,疾病进展组与疾病稳定组患者的图像纹理参数均值(SSF2、SSF4、SSF6)、偏度(SSF2、SSF4)、熵(SSF4、SSF6)均有显著性差异(P均<0.05);DWI序列图像中,疾病进展组与疾病稳定组患者的图像纹理参数均值(SSF2、SSF4、SSF6)、偏度(SSF4、SSF6)、峰度(SSF2、SSF4)均有显著性差异(P均<0.05)。多因素Cox回归分析结果显示,T2WI序列图像纹理参数均值(SSF2、SSF4、SSF6)及DWI序列图像纹理参数均值(SSF2、SSF6)、熵(SSF2、SSF4、SSF6)、偏度(SSF4、SSF6)与宫颈鳞状细胞癌放化疗疗效具有相关性(P<0.05)。ROC曲线分析结果显示,图像纹理参数均值(T2WI-SSF2、T2WI-SSF4、T2WI-SSF6、DWI-SSF2、DWI-SSF6)、偏度(DWI-SSF6)可预测宫颈鳞状细胞癌放化疗后的疾病进展,曲线下面积(area under the curve, AUC)为0.625~0.746。其中,均值(T2WI-SSF4)的预测效能最高(AUC:0.746),其次为均值(T2WI-SSF2,AUC:0.725)、均值(T2WI-SSF6,AUC:0.703)。  结论  基线MRI检查T2WI、DWI图像纹理参数与宫颈鳞状细胞癌放化疗疗效具有相关性,其均值、偏度可预测宫颈鳞状细胞癌放化疗后疾病进展,且以均值的预测效能最高。
    作者贡献:苏佰燕负责研究设计、数据分析、结果解读及论文撰写;戚亚菲、管慧参与研究设计、数据采集;何泳蓝、薛华丹、金征宇参与数据分析、研究实施、结果解读并指导论文修改。
    利益冲突:
  • 图  1  MRI检查DWI序列图像纹理分析示意图

    A.病灶ROI内的DWI图像及SSF 2、4、6图像纹理参数示意图;B.SSF 0、2、3、4、5、6图像纹理参数分析结果示意图
    DWI:弥散加权成像;ROI:感兴趣区;SSF:空间尺度滤波器

    图  2  图像纹理参数预测宫颈鳞癌放化疗后疾病进展的ROC曲线图

    T2WI:同表 1;DWI、SSF:同图 1;ROC:受试者工作特征

    表  1  疾病进展组与疾病稳定组T2WI序列图像纹理参数比较

    纹理参数 疾病进展组(n=46) 疾病稳定组(n=75) P
    SSF2
        均值 -10.49±39.80 23.18±44.64 0.000
        标准差 196.55±146.14 176.52±79.19 0.330
        熵 6.21±0.38 6.09±0.38 0.083
        正性像素均值 136.49±111.75 141.71±67.66 0.749
        偏度 -0.20±1.14 0.36±0.98 0.006
        峰度 3.56±5.68 3.47±4.63 0.921
    SSF4
        均值 -77.20±114.50 15.93±87.27 0.000
        标准差 274.70±209.93 228.59±115.02 0.121
        熵 6.42±0.43 6.23±0.44 0.019
        正性像素均值 158.35±131.36 179.13±134.13 0.406
        偏度 -0.66±0.77 -0.21±0.91 0.007
        峰度 1.94±1.93 1.95±2.29 0.990
    SSF6
        均值 -173.73±220.73 -38.99±138.36 0.000
        标准差 357.91±295.41 280.95±144.38 0.058
        熵 6.61±0.47 6.37±0.50 0.008
        正性像素均值 193.85±189.00 190.23±152.54 0.908
        偏度 -0.58±0.65 -0.49±0.80 0.511
        峰度 0.75±1.09 1.09±2.04 0.233
    T2WI:T2加权成像;SSF:同图 1
    下载: 导出CSV

    表  2  疾病进展组与疾病稳定组DWI序列图像纹理参数比较

    纹理参数 疾病进展组(n=46) 疾病稳定组(n=75) P
    SSF2
        均值 90.23±70.23 137.98±92.76 0.003
        标准差 177.82±99.68 184.03±73.59 0.715
        熵 5.42±0.67 5.35±0.52 0.544
        正性像素均值 178.08±100.63 208.95±98.85 0.100
        偏度 0.26±0.52 0.22±0.41 0.601
        峰度 0.96±2.02 0.32±0.79 0.047
    SSF4
        均值 259.14±188.83 356.65±210.77 0.011
        标准差 277.17±163.90 265.50±110.31 0.671
        熵 5.58±0.67 5.46±0.56 0.265
        正性像素均值 344.81±207.11 412.02±205.43 0.084
        偏度 0.23±0.62 0.03±0.48 0.047
        峰度 0.50±1.51 -0.01±0.70 0.036
    SSF6
        均值 428.56±288.62 544.30±273.42 0.029
        标准差 302.58±179.16 290.13±115.29 0.675
        熵 5.61±0.69 5.48±0.57 0.262
        正性像素均值 487.95±293.02 576.29±265.70 0.090
        偏度 0.12±0.49 -0.09±0.34 0.012
        峰度 -0.08±0.88 -0.32±0.57 0.104
    DWI、SSF:同图 1
    下载: 导出CSV

    表  3  图像纹理参数与宫颈鳞癌放化疗后疾病进展相关性的Cox回归分析阳性结果

    纹理参数 HR(95% CI) P
    T2WI-SSF2-均值 0.984(0.968~1.000) 0.045
    T2WI-SSF4-均值 0.996(0.991~1.000) 0.044
    T2WI-SSF6-均值 0.996(0.993~0.999) 0.019
    DWI-SSF2-均值 0.959(0.935~0.983) 0.001
    DWI-SSF2-熵 0.327(0.163~0.654) 0.002
    DWI-SSF4-熵 0.462(0.257~0.831) 0.010
    DWI-SSF4-偏度 1.897(1.019~3.531) 0.043
    DWI-SSF6-均值 0.988(0.978~0.999) 0.033
    DWI-SSF6-熵 0.488(0.275~0.867) 0.014
    DWI-SSF6-偏度 3.882(1.755~8.587) 0.001
    T2WI:同表 1;DWI、SSF:同图 1
    下载: 导出CSV
  • [1] Torre LA, Siegel RL, Ward EM, et al. Global cancer incidence and mortality rates and trends--An update[J]. Cancer Epidemiol Biomarkers Prev, 2016, 25: 16-27. doi:  10.1158/1055-9965.EPI-15-0578
    [2] Jr WS, Bacon MA, Bajaj A, et al. Cervical cancer: A global health crisis[J]. Cancer, 2017, 123: 2404-2412. doi:  10.1002/cncr.30667
    [3] Jonska-Gmyrek J, Gmyrek L, Zolciak-Siwinska A, et al. Adenocarcinoma histology is a poor prognostic factor in locally advanced cervical cancer[J]. Curr Med Res Opin, 2019, 35: 595-601. doi:  10.1080/03007995.2018.1502166
    [4] Davnall F, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?[J]. Insights Imaging, 2012, 3: 573-589. doi:  10.1007/s13244-012-0196-6
    [5] Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival[J]. Radiology, 2013, 266: 177-184. doi:  10.1148/radiol.12120254
    [6] Kim JH, Ko ES, Lim Y, et al. Breast cancer hetero-geneity: MR imaging texture analysis and survival outcomes[J]. Radiology, 2017, 282: 665-675. doi:  10.1148/radiol.2016160261
    [7] Zhang S, Chiang GC, Magge RS, et al. Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas[J]. Eur Radiol, 2019, 29: 2751-2759. doi:  10.1007/s00330-018-5921-1
    [8] Ditmer A, Zhang B, Shujaat T, et al. Diagnostic accuracy of MRI texture analysis for grading gliomas[J]. J Neurooncol, 2018, 140: 583-589. doi:  10.1007/s11060-018-2984-4
    [9] 张铮铮, 刘筱, 徐浩, 等. MRI图像纹理特征在评估宫颈癌新辅助化疗中的预测作用[J]. 徐州医科大学学报, 2018, 38: 592-595. doi:  10.3969/j.issn.1000-2065.2018.09.009

    Zhang ZZ, Liu X, Xu H, et al. Application of MRI texture analysis in evaluating the neoadjuvant chemotherapy response in uterine cervix carcinoma[J]. Xuzhou Yike Daxue Xuebao, 2018, 38: 592-595. doi:  10.3969/j.issn.1000-2065.2018.09.009
    [10] 郑明雪, 董江宁, 李翠平, 等. 表观扩散系数联合纹理特征评估宫颈鳞癌分化程度的价值[J]. 实用放射学杂志, 2020, 36: 592-595, 614. doi:  10.3969/j.issn.1002-1671.2020.04.021

    Zheng MX, Dong JN, Li CP, et al. The value of ADC values and texture analysis in evaluating the differentiation degree of cervical squamous cell carcinoma[J]. Shiyong Fangshexue Zazhi, 2020, 36: 592-595, 614. doi:  10.3969/j.issn.1002-1671.2020.04.021
    [11] 陈文林, 胥明婧, 李绍东. 磁共振扩散加权成像纹理分析在对宫颈癌术后早期复发的预测价值[J]. 广西医学, 2018, 40: 1440-1443. https://www.cnki.com.cn/Article/CJFDTOTAL-GYYX201813016.htm

    Chen WL, Xu MJ, Li SD. Predictive value of texture analysis based on MRI diffusion-weighted imaging for early recurrence of cervical cancer after surgery[J]. Guangxi Yixue, 2018, 40: 1440-1443. https://www.cnki.com.cn/Article/CJFDTOTAL-GYYX201813016.htm
    [12] 谢元亮, 杜丹, 谢伟, 等. DCE-MRI纹理分析鉴别宫颈鳞癌与腺癌及预测分级的价值[J]. 放射学实践, 2019, 34: 835-840. https://www.cnki.com.cn/Article/CJFDTOTAL-FSXS201908002.htm

    Xie YL, Du D, Xie W, et al. The value of texture analysis based on dynamic contrast-enhanced MRI for differentiating cervical adeno-carcinoma from squamous cell carcinoma and its prediction of stages[J]. Fangshexue Shijian, 2019, 34: 835-840. https://www.cnki.com.cn/Article/CJFDTOTAL-FSXS201908002.htm
    [13] 杜文壮, 蒲如剑, 梁洁, 等. DCE-MRI纹理分析鉴别AFP阴性肝细胞肝癌与肝局灶性结节增生的价值[J]. 磁共振成像, 2020, 11: 765-770. doi:  10.12015/issn.1674-8034.2020.09.009

    Du WZ, Pu RJ, Liang J, et al. The value of texture analysis of dynamic contrast-enhanced MRI in differentiating AFP negative hepatocellular carcinoma from focal nodular hyperplasia[J]. Cigongzhen Chengxiang, 2020, 11: 765-770. doi:  10.12015/issn.1674-8034.2020.09.009
    [14] 李梦双, 刘耀赛, 董丽娜, 等. MRI纹理分析在Ⅱ级和Ⅲ级脑胶质瘤鉴别诊断中的应用价值研究[J]. 浙江医学, 2020, 42: 580-582. doi:  10.12056/j.issn.1006-2785.2020.42.6.2019-3525

    Li MS, Liu YS, Dong LN, et al. Value of MRI texture analysis in differential diagnosis of grade Ⅱ and grade Ⅲ gliomas[J]. Zhejiang Yixue, 2020, 42: 580-582. doi:  10.12056/j.issn.1006-2785.2020.42.6.2019-3525
    [15] 郑茜, 鲁毅, 孙学进, 等. 常规磁共振成像纹理分析对良、恶性脑膜瘤鉴别诊断价值[J]. 实用放射学杂志, 2020, 36: 1192-1195. doi:  10.3969/j.issn.1002-1671.2020.08.004

    Zheng Q, Lu Y, Sun XJ, et al. The value of conventional MRI texture analysis in differential diagnosis of benign and malignant meningiomas[J]. Shiyong Fangshexue Zazhi, 2020, 36: 1192-1195. doi:  10.3969/j.issn.1002-1671.2020.08.004
    [16] 翁炜, 吕秀玲, 张倩倩, 等. 基于磁共振影像组学技术对肝癌经肝动脉化疗栓塞术后短期疗效的预后价值分析[J]. 中华医学杂志, 2020, 100: 828-832. doi:  10.3760/cma.j.cn112137-20190705-01502

    Weng W, Lyu XL, ZHANG QQ, et al. Prediction of short-term prognosis of hepatocellular carcinoma after TACE surgery based on MRI texture analysis technology[J]. Zhonghua Yixue Zazhi, 2020, 100: 828-832. doi:  10.3760/cma.j.cn112137-20190705-01502
    [17] 薛珂, 丁莹莹, 李振辉, 等. 利用磁共振成像动态增强纹理特征预测不同分子亚型乳腺癌[J]. 实用放射学杂志, 2020, 36: 1235-1239. doi:  10.3969/j.issn.1002-1671.2020.08.015

    Xue K, Ding YY, Li ZH, et al. Dynamic contrast-enhanced MRI texture analysis for distinguishing different molecular subtypes of breast cancer[J]. Shiyong Fangshexue Zazhi, 2020, 36: 1235-1239. doi:  10.3969/j.issn.1002-1671.2020.08.015
    [18] Ganeshan B, Miles KA, Young RC, et al. Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival[J]. Acad Radiol, 2007, 14: 1520-1530. doi:  10.1016/j.acra.2007.06.028
    [19] Chen J, Wang HY, Ye HY. Research progress of texture analysis in tumor imaging[J]. Chin J Radiol, 2017, 51: 979-982.
    [20] Teruel JR, Heldahl MG, Goa PE, et al. Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemo-therapy in patients with locally advanced breast cancer[J]. NMR Biomed, 2014, 27: 887-896. doi:  10.1002/nbm.3132
    [21] Pyka T, Bundschuh RA, Andratschke N, et al. Textural features in pre-treatment[F18 ]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy[J]. Radiat Oncol, 2015, 10: 100. doi:  10.1186/s13014-015-0407-7
    [22] Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT[J]. Cancer Imaging, 2013, 13: 140-149. doi:  10.1102/1470-7330.2013.0015
    [23] 王俊, 孙阳, 张燕燕, 等. CT纹理分析鉴别诊断胰腺导管腺癌、胰腺神经内分泌肿瘤及实性假乳头状肿瘤[J]. 中国医学影像技术, 2020, 36: 554-558. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXX202004019.htm

    Wang J, Sun Y, Zhang YY, et al. CT texture analysis in differential diagnosis of pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumor and solid pseudopapillary tumor[J]. Zhongguo Yixue Yingxiang Jishu, 2020, 36: 554-558. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYXX202004019.htm
    [24] Kyriazi S, Collins DJ, Messiou C, et al. Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging-value of histogram analysis of apparent diffusion coefficients[J]. Radiology, 2011, 261: 182-192. doi:  10.1148/radiol.11110577
    [25] Bezy-Wendling J, Kretowski M, Rolland Y, et al. Toward a better understanding of texture in vascular CT scan simulated images[J]. IEEE Trans Biomed Eng, 2001, 48: 120-124. doi:  10.1109/10.900272
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  • 收稿日期:  2021-05-10
  • 录用日期:  2021-07-30
  • 刊出日期:  2021-09-30

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