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Article History

Received: 16 January 2023

Revised: 17 July 2023

Accepted: 17 August 2023

First Online: 26 October 2023

Declarations

:

: The scientific guarantor of this publication is Yuki Himoto.

: 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.

: 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.

: Written informed consent was waived by the Institutional Review Board.

: 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).

: 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. <i>Magn Reson Imaging</i> 85:161–167. ” (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.

: • retrospective• diagnostic or prognostic study• multicenter study