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

Published:February 09, 2022DOI:


      • 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.


      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).


      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


      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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