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MRI Texture Analysis for Preoperative Prediction of Lymph Node Metastasis in Patients with Nonsquamous Cell Cervical Carcinoma

Published:February 09, 2022DOI:https://doi.org/10.1016/j.acra.2022.01.005

      Highlights

      • The predictive factors of lymph node metastasis (LNM) in adenocarcinoma components are different from those in squamous cell cervical carcinoma (SCC).
      • The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models showed good discrimination ability in predicting LNM in patients with cervical non-SCC.
      • The T2WI+DWI-based, T2WI+DWI+CE-T1WI-based and T2WI+DWI+LNS-MRI-based models performed better than positive LN morphological criteria on MRI and yielded similar discrimination abilities in predicting LNM in patients with cervical non-SCC.

      Rationale and Objectives

      To preoperatively predict lymph node metastasis (LNM) in patients with cervical nonsquamous cell carcinoma (non-SCC) based on magnetic resonance imaging (MRI) texture analysis.

      Materials and Methods

      This retrospective study included 104 consecutive patients (mean age of 47.2 ± 11.3 years) with stage IB–IIA cervical non-SCC. According to the ratio of 7:3, 72, and 32 patients were randomly divided into the training and testing cohorts. A total of 272 original features were extracted. In the process of feature selection, features with intraclass correlation coefficients (ICCs) less than 0.8 were eliminated. The Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were applied to reduce redundancy, overfitting, and selection biases. Further, a support vector machine (SVM) with linear kernel function was applied to select the optimal feature set with a high discrimination power.

      Results

      The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI (LN status on MRI)-based SVM models yielded an AUC and accuracy of 0.78 and 0.79; 0.79 and 0.69; 0.79 and 0.81 for predicting LNM in the training cohort, and 0.82 and 0.78; 0.82 and 0.69; 0.79 and 0.72 in the testing cohort. The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models performed better than morphologic criteria of LNS-MRI and yield similar discrimination abilities in predicting LNM in the training and testing cohorts (all p-value > 0.05). In addition, the T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the AC and ASC subgroups (all p-value > 0.05).

      Conclusion

      The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI+DWI+LNS-MRI-based SVM models showed similar good discrimination ability and performed better than the morphologic criteria of LNS-MRI in predicting LNM in patients with cervical non-SCC. The inclusion of the CE-T1WI sequence and morphologic criteria of LNS-MRI did not significantly improve the performance of the T2WI + DWI-based model. The T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the subgroup analysis.

      Key Words

      Abbreviations:

      SCC (Squamous cell carcinoma), ASC (Adenosquamous carcinoma), FS-T2WI (Fat-saturated T2-weighted imaging), CE-T1WI (Contrast-enhanced T1-weighted imaging), FIGO (International Federation of Gynecology and Obstetrics), LNS-MRI (Lymph node status on MRI), DSI (Depth of stromal invasion), ROI (Region of interest), ANOVA (Analysis of variance), LDA (Linear discriminator analysis), NB (Naive bayes), LASSO (Least absolute shrinkage and selection operator), ROC (Receiver operating characteristic curve), PPV (Positive predictive value), AC (Adenocarcinoma), LNM (Lymph node metastasis), DWI (Diffusion-weighted imaging), LVSI (Lymph vascular space invasion), ICC (Intraclass correlation coefficient), PCC (Pearson correlation coefficient), SVM (Support vector machine), LR (Logistic regression), AUC (Area under the curve), NPV (negative predictive value)
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      Reference

        • Arbyn M
        • Weiderpass E
        • Bruni L
        • et al.
        Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis.
        Lancet Glob Health. 2020; 8: e191-e203
        • Baek MH
        • Park JY
        • Kim D
        • et al.
        Comparison of adenocarcinoma and adenosquamous carcinoma in patients with early-stage cervical cancer after radical surgery.
        Gynecol Oncol. 2014; 135: 462-467
        • Vinh-Hung V
        • Bourgain C
        • Vlastos G
        • et al.
        Prognostic value of histopathology and trends in cervical cancer: a SEER population study.
        BMC Cancer. 2007; 7: 164
        • Yokoi E
        • Mabuchi S
        • Takahashi R
        • et al.
        Impact of histological subtype on survival in patients with locally advanced cervical cancer that were treated with definitive radiotherapy: adenocarcinoma/adenosquamous carcinoma versus squamous cell carcinoma.
        J Gynecol Oncol. 2017; 28: e19
        • Lai CH
        • Hsueh S
        • Hong JH
        • et al.
        Are adenocarcinomas and adenosquamous carcinomas different from squamous carcinomas in stage IB and II cervical cancer patients undergoing primary radical surgery?.
        Int J Gynecol Cancer. 1999; 9: 28-36
        • Sasieni P
        • Adams J.
        Changing rates of adenocarcinoma and adenosquamous carcinoma of the cervix in England.
        Lancet. 2001; 357: 1490-1493
        • Gien LT
        • Beauchemin MC
        • Thomas G.
        Adenocarcinoma: a unique cervical cancer.
        Gynecol Oncol. 2010; 116: 140-146
        • Mabuchi S
        • Okazawa M
        • Matsuo K
        • et al.
        Impact of histological subtype on survival of patients with surgically-treated stage IA2-IIB cervical cancer: adenocarcinoma versus squamous cell carcinoma.
        Gynecol Oncol. 2012; 127: 114-120
        • Noh JM
        • Park W
        • Kim YS
        • et al.
        Comparison of clinical outcomes of adenocarcinoma and adenosquamous carcinoma in uterine cervical cancer patients receiving surgical resection followed by radiotherapy: a multicenter retrospective study (KROG 13-10).
        Gynecol Oncol. 2014; 132: 618-623
        • Galic V
        • Herzog TJ
        • Lewin SN
        • et al.
        Prognostic significance of adenocarcinoma histology in women with cervical cancer.
        Gynecol Oncol. 2012; 125: 287-291
        • Irie T
        • Kigawa J
        • Minagawa Y
        • et al.
        Prognosis and clinicopathological characteristics of Ib-IIb adenocarcinoma of the uterine cervix in patients who have had radical hysterectomy.
        Eur J Surg Oncol. 2000; 26: 464-467
        • Huang YT
        • Wang CC
        • Tsai CS
        • et al.
        Long-term outcome and prognostic factors for adenocarcinoma/adenosquamous carcinoma of cervix after definitive radiotherapy.
        Int J Radiat Oncol Biol Phys. 2011; 80: 429-436
        • He L
        • Wu L
        • Su G
        • et al.
        The efficacy of neoadjuvant chemotherapy in different histological types of cervical cancer.
        Gynecol Oncol. 2014; 134: 419-425
        • Mizuno T
        • Kojima Y
        • Yonemori K
        • et al.
        HER3 protein expression as a risk factor for post-operative recurrence in patients with early-stage adenocarcinoma and adenosquamous carcinoma of the cervix.
        Oncol Lett. 2020; 20: 38
        • Gadducci A
        • Guerrieri ME
        • Cosio S.
        Adenocarcinoma of the uterine cervix: Pathologic features, treatment options, clinical outcome and prognostic variables.
        Crit Rev Oncol Hematol. 2019; 135: 103-114
        • Cao L
        • Wen H
        • Feng Z
        • et al.
        Distinctive clinicopathologic characteristics and prognosis for different histologic subtypes of early cervical cancer.
        Int J Gynecol Cancer. 2019; 29: 1244-1251
        • Bourgioti C
        • Chatoupis K
        • Rodolakis A
        • et al.
        Incremental prognostic value of MRI in the staging of early cervical cancer: a prospective study and review of the literature.
        Clin Imaging. 2016; 40: 72-78
        • Bhatla N
        • Aoki D
        • Sharma DN
        • et al.
        Cancer of the cervix uteri.
        Int J Gynaecol Obstet. 2018; 143: 22-36
        • Lv K
        • Guo HM
        • Lu YJ
        • et al.
        Role of 18F-FDG PET/CT in detecting pelvic lymph-node metastases in patients with early stage uterine cervical cancer: Comparison with MRI findings.
        Nucl Med Commun. 2014; 35: 1204-1211
        • Wang HY
        • Sun JM
        • Tang J.
        Sentinel lymph nodes detection in patients with cervical cancer undergoing radical hysterectomy.
        Zhonghua Fu Chan Ke Za Zhi. 2004; 39: 7-9
        • Manganaro L
        • Lakhman Y
        • Bharwani N
        • et al.
        Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging.
        Eur Radiol. 2018; https://doi.org/10.1007/s00330-020-07632-9
        • Xiao M
        • Yan B
        • Li Y
        • Lu J
        • Qiang J.
        Diagnostic performance of MR imaging in evaluating prognostic factors in patients with cervical cancer: a meta-analysis.
        Eur Radiol. 2020; 30: 1405-1418
        • Yan BC
        • Li Y
        • Ma FH
        • et al.
        Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study.
        Eur Radiol. 2021; 31: 411-422
        • Abu-Rustum NR
        • Yashar CM
        • Bean S
        • et al.
        NCCN Guidelines Insights: Cervical Cancer, Version 1.2020.
        J Natl Compr Canc Netw. 2020; 18: 660-666
        • Xiao M
        • Ma F
        • Li Y
        • et al.
        Multiparametric MRI-based radiomics nomogram for predicting lymph node metastasis in early-stage cervical cancer.
        J Magn Reson Imaging. 2020; 52: 885-896
        • Wang T
        • Gao T
        • Yang J
        • et al.
        Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging.
        Eur J Radiol. 2019; 114: 128-135
        • Li Z
        • Li H
        • Wang S
        • et al.
        MR-based radiomics nomogram of cervical cancer in prediction of the lymph-vascular space invasion preoperatively.
        J Magn Reson Imaging. 2019; 49: 1420-1426
        • Zhang Y
        • Zhang KY
        • Jia HD
        • et al.
        Feasibility of predicting pelvic lymph node metastasis based on IVIM-DWI and texture parameters of the primary lesion and lymph nodes in patients with cervical cancer.
        Acad Radiol. 2021; 12 (S1076-6332(21)00387-1)
        • He M
        • Song Y
        • Li H
        • et al.
        Histogram analysis comparison of monoexponential, advanced diffusion-weighted imaging, and dynamic contrast-enhanced MRI for differentiating borderline from malignant epithelial ovarian tumors.
        J Magn Reson Imaging. 2020; 52: 257-268
        • Wormald BW
        • Doran SJ
        • Ind TE
        • et al.
        Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy.
        Gynecol Oncol. 2020; 156: 107-114
        • Yin P
        • Mao N
        • Zhao C
        • et al.
        A triple-classification model for the differentiation of primary chordoma, giant cell tumor, and metastatic tumor of sacrum based on T2-weighted and contrast-enhanced T1-weighted MRI.
        J Magn Reson Imaging. 2019; 49: 752-759
        • Song Y
        • Zhang J
        • Zhang YD
        • et al.
        FeAture Explorer (FAE): A tool for developing and comparing radiomics models.
        PLoS One. 2020; 15e0237587
        • Kim JY
        • Park JE
        • Jo Y
        • et al.
        Incorporating diffusion- and perfusion-weighted MRI into a model improves diagnostic performance for pseudoprogression in glioblastoma patients.
        Neuro Oncol. 2019; 21: 404-414
      1. Zhou JL. Wavelet radiomics improving performance in prediction from response of neoadjuvant chemotherapy based on breast MRI. In: The 18th National Magnetic Resonance Conference of Radiology Branch of Chinese Medical Association, Shanghai, 2019.

        • Li ZZ
        • Liu PF
        • An TT
        • et al.
        Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients.
        Transl Oncol. 2021; 14101065
        • Contag SA
        • Gostout BS
        • Clayton AC
        • et al.
        Comparison of gene expression in squamous cell carcinoma and adenocarcinoma of the uterine cervix.
        Gynecol Oncol. 2004; 95: 610-617
        • Chao A
        • Wang T-H
        • Lee Y-S
        • et al.
        Molecular characterization of adenocarcinoma and squamous carcinoma of the uterine cervix using microarray analysis of gene expression.
        Int J Cancer. 2006; 119: 91-98
        • Wright AA
        • Howitt BE
        • Myers AP
        • et al.
        Oncogenic mutations in cervical cancer: genomic differences between adenocarcinomas and squamous cell carcinomas of the cervix.
        Cancer. 2013; 119: 3776-3783
        • Tornesello ML
        • Buonaguro L
        • Buonaguro FM.
        Mutations of the TP53 gene in adenocarcinoma and squamous cell carcinoma of the cervix: a systematic review.
        Gynecol Oncol. 2013; 128: 442-448
        • Lee E-J
        • McClelland M
        • Wang Y
        • et al.
        Distinct DNA methylation profiles between adenocarcinoma and squamous cell carcinoma of human uterine cervix.
        Oncol Res Featuring Preclin Clin Cancer Ther. 2009; 18: 401-408