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Nodal infiltration in endometrial cancer: a prediction model using best subset regression

  • Urogenital
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Abstract

Objectives

To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors.

Methods

In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available.

Results

The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient β values). The AUCs were 0.85 (95%CI: 0.77–0.92) and 0.86 (0.75–0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68–0.89) and 0.87 (0.76–0.96), respectively.

Conclusions

The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies.

Clinical relevance statement

The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment.

Key Points

•Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information.

•The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance.

•Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

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Abbreviations

AUC:

Area under the curve

BVS:

Bivariate variable selection method

CA:

Cancer antigen

CE-MRI:

Contrast-enhanced MRI

CI:

Confidence interval

CSI:

Cervical stromal invasion

DMI:

Deep myometrial invasion

EC:

Endometrial cancer

FDG PET-CT:

Fluorine-18–2-deoxy-d-glucose PET-CT

FIGO:

International Federation of Gynecology and Obstetrics

FN:

False negative

LN(M):

Lymph node (metastasis)

NPV:

Negative predictive value

PAN(M):

Para-aortic lymph node (metastasis)

PEN(M):

Pelvic lymph node (metastasis)

PPV:

Positive predictive value

r-LNM:

Regional lymph node metastasis

ROC-AUC:

Receiver operating characteristic curve area under the curve

SLN:

Sentinel lymph node

VIF:

Variance inflation factors

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Acknowledgements

The proposed LNM prediction model system is accessible on https://kuhp-drad-gyne-pred1.shinyapps.io/LN_model_with_MRI/

The authors thank Dr. Satoshi Morita for his statistical advice.

Funding

This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 20K16748.

Author information

Authors and Affiliations

  1. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan

    Yuka Kuriyama Matsumoto, Yuki Himoto, Satoshi Otani, Koji Fujimoto, Yasuhisa Kurata, Yusaku Moribata, Aki Kido & Yuji Nakamoto

  2. Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan

    Mizuho Nishio

  3. Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan

    Nao Kikkawa & Kimiteru Ito

  4. Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Koji Yamanoi & Masaki Mandai

  5. Department of Gynecology, National Cancer Center Hospital, Tokyo, Japan

    Tomoyasu Kato

  6. Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan

    Hiroshi Yoshida

  7. Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Sachiko Minamiguchi

Authors
  1. Yuka Kuriyama Matsumoto
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Corresponding author

Correspondence to Yuki Himoto.

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Guarantor

The scientific guarantor of this publication is Yuki Himoto.

Conflict of interest

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 (Dr. Mizuho Nishio M.D., Ph.D.) has significant expertise in machine learning.

Prof. Satoshi Morita, Ph.D. (Kyoto University Graduate School of Medicine, Department of Biomedical Statistics and Bioinformatics), kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained. This study was approved by the Kyoto University Graduate School and Faculty of Medicine, Ethics Committee (Approval number: R2747), and National Cancer Center Ethics Committee (Approval number 2020–480).

Study subjects or cohorts overlap

Among 364 patients, 183 patients have been previously reported in the paper titled: “Otani S, Himoto Y, Nishio M et al (2022) Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists’ decisions of deep myometrial invasion. Magn Reson Imaging 85:161–167. https://doi.org/10.1016/j.mri.2021.10.024” (PMID: 34687853). The previous article dealt with radiomic machine learning classifiers for the pretreatment assessment of comprehensive risk factors. This study focused on building a clinically practical prediction model for lymph node metastasis.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Matsumoto, Y.K., Himoto, Y., Nishio, M. et al. Nodal infiltration in endometrial cancer: a prediction model using best subset regression. Eur Radiol 34, 3375–3384 (2024). https://doi.org/10.1007/s00330-023-10310-1

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