Volume 12 Issue 5
Sep.  2021
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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

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

doi: 10.12290/xhyxzz.2021-0380
Funds:

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

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  • Corresponding author: HE Yonglan  Tel: 86-10-69159610, E-mail: heyonglan@pumch.cn
  • Received Date: 2021-05-10
  • Accepted Date: 2021-07-30
  • Publish Date: 2021-09-30
  •   Objective  To investigate the correlation of the texture parameters of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) with the efficacy of chemoradiotherapy in cervical squamous cell carcinoma.  Methods  Patients with squamous cell carcinoma of the cervix that underwent chemoradiotherapy from February 2015 to January 2016 in Peking Union Medical College Hospital were included retrospectively, and were divided into the disease-progressive group and the disease-stable group according to their prognosis. Texture analysis of baseline T2WI and DWI images before chemoradiotherapy was carried out with Texrad software, and the texture parameters of spatial scale filter (SSF) with radius values of 2, 4 and 6 were obtained. The differences of texture parameters between the two groups were compared, and the correlation between the texture parameters and the curative of chemoradiotherapy in patients with cervical squamous cell carcinoma was analyzed by multivariate Cox regression. Receiver operating characteristic (ROC) curve was used to analyze the performance of texture parameters in predicting disease progression after chemoradiotherapy in patients with cervical squamous cell carcinoma.  Results  A total of 121 patients with squamous cell carcinoma of the cervix that met the inclusion and exclusion criteria were enrolled in this study. There were 46 cases in the disease-progressive group and 75 cases in the disease-stable group. In T2WI sequential images, there were significant differences in the texture parameters of means (SSF2, SSF4, SSF6), skewness (SSF2, SSF4), and entropy (SSF4, SSF6) between disease-progressive group and disease-stable group (all P < 0.05). In DWI sequential images, there were significant differences in the texture parameters of means (SSF2, SSF4, SSF6), skewness (SSF4, SSF6), and kurtosis (SSF2, SSF4) between the two groups (all P < 0.05). Multivariate Cox regression analysis showed that the texture parameter of means (SSF2, SSF4, SSF6) of T2WI and the texture parameters of means (SSF2, SSF6), entropy (SSF2, SSF4, SSF6) and skewness (SSF4, SSF6) of DWI were correlated with the efficacy of chemoradiotherapy in patients with cervical squamous cell carcinoma (P < 0.05). The Results of ROC analysis showed that the texture parameter of means (T2WI-SSF2, T2WI-SSF4, T2WI-SSF6, DWI-SSF2, DWI-SSF6) and skewness (DWI-SSF6) could predict the progression of cervical squamous cell carcinoma after chemoradiotherapy in patients with cervical squamous cell carcinoma. The area under the curve (AUC) was 0.625-0.746. Among them, the mean of T2WI-SSF4 was the most effective (AUC: 0.746), followed by the mean of T2WI-SSF2 (AUC: 0.725) and the mean of T2WI-SSF6 (AUC: 0.703).  Conclusions  The texture parameters of baseline T2WI and DWI sequences were correlated with the curative effect of chemoradiotherapy in patients with cervical squamous cell carcinoma. The parameters of means and skewness can predict the progression of cervical squamous carcinoma after chemoradiotherapy, and the mean has a higher predictive power.
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  • [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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