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Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients

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Abstract

Objective

To investigate the feasibility of T2WI-based radiomics nomogram analysis to non-invasively predict normal-sized pelvic lymph node (LN) metastasis (LNM) in cervical cancer patients.

Methods

Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T2WI (model 1), model 1 combined with LN radiomics features (model 2), and model 2 combined with tumor score (model 3). Diagnostic performance was assessed and compared.

Results

Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15).

Conclusions

T2WI-based radiomics nomogram incorporating the LN radiomics signature with the clinical morphological LN features is promising for predicting the normal-sized pelvic LNM in cervical cancer patients. The original tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

Key Points

• The combination of LN radiomics signature with LN clinical morphological features on T2WI could discriminate LNM relatively well.

• The tumor radiomics analysis did not significantly improve the differential diagnosis of LNM.

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Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

FIGO:

International Federation of Gynecology and Obstetrics

LASSO:

Least absolute shrinkage and selection operator

LNM:

Lymph nodes metastasis

mRMR:

Max-relevance and min-redundancy

ROC:

Receiver operating characteristic

ROI:

Regions of interest

T2WI:

T2-weighted imaging

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Acknowledgements

We thank Shaofeng Duan for the technological support of AK software and thank LetPub for its linguistic assistance.

Funding

The authors state that this work has not received any funding.

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Author notes

  1. Jiacheng Song and Qiming Hu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210029, China

    Jiacheng Song, Zhanlong Ma, Meng Zhao, Ting Chen & Haibin Shi

  2. Department of Obstetrics & Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China

    Qiming Hu

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  1. Jiacheng Song
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Correspondence to Ting Chen or Haibin Shi.

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The scientific guarantor of this publication is Haibin Shi.

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Song, J., Hu, Q., Ma, Z. et al. Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients. Eur Radiol 31, 6938–6948 (2021). https://doi.org/10.1007/s00330-021-07735-x

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  • DOI: https://doi.org/10.1007/s00330-021-07735-x

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