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ORIGINAL RESEARCH article

Front. Pediatr.
Sec. General Pediatrics and Pediatric Emergency Care
Volume 12 - 2024 | doi: 10.3389/fped.2024.1417818

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database

Provisionally accepted
Zhiwei Yan Zhiwei Yan 1Yumeng Wu Yumeng Wu 2Yuehua Chen Yuehua Chen 3*Jian Xu Jian Xu 4*Xiubing Zhang Xiubing Zhang 4*Qiyou Yin Qiyou Yin 3*
  • 1 Department of Paediatric Surgery, Affiliated Hospital of Nantong University, Nantong, China
  • 2 Cancer Research Center Nantong, Nantong Tumor Hospital, Nantong, China
  • 3 Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Nantong, China
  • 4 Other, Nantong, China

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

    Background: Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisionsWe built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010-2018 on 1,542 patients with neuroblastoma. Seven machinelearning methods were employed to forecast the likelihood of neuroblastoma distant metastases.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models.. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases.The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05).Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively.. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma.The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

    Keywords: Neuroblastoma, distant metastasis, SEER database, machine learning, predictive model

    Received: 15 Apr 2024; Accepted: 03 Sep 2024.

    Copyright: © 2024 Yan, Wu, Chen, Xu, Zhang and Yin. 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:
    Yuehua Chen, Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Nantong, China
    Jian Xu, Other, Nantong, China
    Xiubing Zhang, Other, Nantong, China
    Qiyou Yin, Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Nantong, 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.