Volume 19 Issue 12
Dec.  2021
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Article Navigation >  Chinese Journal of General Practice  > 2021  >  19(12): 2088-2092
WANG Hai-bo, CUI Wei, YANG Wei-li. Multi-sequence MRI-based radiomics predicting lymph-vascular space invasion in early-stage cervical cancer[J]. Chinese Journal of General Practice, 2021, 19(12): 2088-2092. doi: 10.16766/j.cnki.issn.1674-4152.002244
Citation: WANG Hai-bo, CUI Wei, YANG Wei-li. Multi-sequence MRI-based radiomics predicting lymph-vascular space invasion in early-stage cervical cancer[J]. Chinese Journal of General Practice, 2021, 19(12): 2088-2092. doi: 10.16766/j.cnki.issn.1674-4152.002244
shu

Multi-sequence MRI-based radiomics predicting lymph-vascular space invasion in early-stage cervical cancer

doi: 10.16766/j.cnki.issn.1674-4152.002244
  • 1.

    Department of Radiology, the Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang 315100, China

Funds:

 2019RC274

  • Received Date: 2021-04-13
    Available Online: 2022-03-02
    Abstract
  •   Objective  To explore the clinical value of multi-sequence MRI-based radiomics in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer.  Methods  A total of 134 patients (91 cases in the training group and 43 cases in the validation group) with pathological early-stage cervical cancer in the Affiliated People's Hospital of Ningbo University from January 2015 to February 2020, were retrospectively collected. All patients underwent MRI plain scan, contrast-enhanced MRI (CE-MRI) and diffusion-weighted imaging (DWI) before surgery. MRI images of each sequence were obtained, and the region of interest was drawn. Radiomic features were extracted by using the least absolute shrinkage and selection operator (LASSO) and nomogram method to construct the predictive model. The training group was used to extract feature, establish signature and construct the predictive model. The validation group was used to verify the predictive model. Receiver operating characteristic curve was used to analyse the predictive effect of each sequence MRI-based radiomic model on LVSI in early-stage cervical cancer.  Results  The predictive LVSI features were extracted from T2WI-FS, CE-MRI and DWI sequence images in patients with early-stage cervical cancer by LASSO regression. WavEnLH_s-4 and Horzl_LngREmph were all screened out. Results showed that the diagnostic efficiency of multi-sequence MRI imaging models constructed by the Logistic regression model was high for LVSI in early-stage cervical cancer. AUC of T2WI-FS, CE-MRI and DWI was 0.810, 0.803 and 0.781, respectively, in the training group and 0.785, 0.761 and 0.752, respectively, in the verification group. The AUC of multi-sequence MRI-based radiomic model constructed by nomogram was 0.893 in the training group and 0.859 in the verification group.  Conclusion  As an objective image analysis method, the nomographic model based on T2WI-FS, CE-MRI and DWI sequence has a high predictive effect and certain clinical application value in LVSI of early-stage cervical cancer.

     

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