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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Gynecological Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1400109

Hematological Indicator-Based Machine Learning Models for Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Provisionally accepted
Huan Zhao Huan Zhao 1玉玲 王 玉玲 王 2Yilin Sun Yilin Sun 3Yongqiang Wang Yongqiang Wang 1Bo Shi Bo Shi 1Jian Liu Jian Liu 2*Sai Zhang Sai Zhang 1*
  • 1 Bengbu Medical University, Bengbu, China
  • 2 The first affiliated hospital of Bengbu medical university, Bengbu, China
  • 3 The first affiliated hospital of bengbu medical university, Bengbu, China

The final, formatted version of the article will be published soon.

    accuracy (0.831, 95% CI: 0.702-0.960), specificity (0.835, 95% CI: 0.708-0.962), sensitivity (0.831, 95% CI: 0.702-0.960), and F1-score (0.829, 95% CI: 0.696-0.962). RF had the highest AUC in the testing set (AUC = 0.854).RF based on preoperative hematological indicators that are easily available in clinical practice showed superior performance in the preoperative prediction of CC LNM.However, investigations on larger external cohorts of patients are required for further validation of our findings.

    Keywords: cervical cancer, lymph node metastasis, machine learning, Hematological indicators, Preoperative prediction

    Received: 13 Mar 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Zhao, 王, Sun, Wang, Shi, Liu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jian Liu, The first affiliated hospital of Bengbu medical university, Bengbu, China
    Sai Zhang, Bengbu Medical University, Bengbu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.