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Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively.

Methods

During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27–85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B–E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model.

Results

The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone.

Conclusions

The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC.

Key Points

• A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier.

• The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.

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Abbreviations

CDC:

Clinical decisive curve

CI:

Confidence interval

EC:

Endometrial cancer

ER:

Estrogen receptor

IDI:

Integrated discrimination index

LNM:

Lymph node metastasis

NRI:

Net reclassification index

PLNM:

Pelvic lymph node metastasis

PR:

Progesterone receptor

SMOTE:

Synthetic minority oversampling technique

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Funding

This study has received funding from the National Natural Science Foundation of China (No. 81971579).

Author information

Author notes

  1. Bi Cong Yan and Ying Li contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China

    Bi Cong Yan, Ying Li & Jin Wei Qiang

  2. Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, 128 ShenYang Road, Shanghai, 200090, China

    Feng Hua Ma & Guo Fu Zhang

  3. Department of Radiology, Cancer Hospital of Nantong University, 30 North Tong Yang Road, 536 Chang Le Road, Nantong, 226361, Jiangsu, China

    Feng Feng

  4. Department of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 201204, China

    Ming Hua Sun

  5. Department of Radiology, Huadong Hospital of Fudan University, Fudan University, 221 West Yan’an Road, Shanghai, 200040, China

    Guang Wu Lin

Authors
  1. Bi Cong Yan
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Corresponding author

Correspondence to Jin Wei Qiang.

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Guarantor

The scientific guarantor of this publication is Jin Wei Qiang.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was not required for this study because of the retrospective nature of the study.

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Local institutional Review Board approval was obtained.

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• retrospective

• diagnostic study

• multicenter study

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Yan, B.C., Li, Y., Ma, F.H. et al. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol 31, 411–422 (2021). https://doi.org/10.1007/s00330-020-07099-8

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  • DOI: https://doi.org/10.1007/s00330-020-07099-8

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