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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Surgical Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1393990

Nomograms to predict lung metastasis in malignant primary osseous spinal neoplasms and cancer-specific survival in lung metastasis subgroup

Provisionally accepted
Yong Jiang Yong Jiang 1Yapeng Zhu Yapeng Zhu 1*Yongli Ding Yongli Ding 1*Xinchang Lu Xinchang Lu 2*
  • 1 Department of Orthopaedic Surgery, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
  • 2 Department of Orthopaedic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

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

    Purpose To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis, and predicting cancer-specific survival (CSS) in the lung metastasis subgroup.Methods A total of 1298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing Sarcoma and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms.Results Histologic type, grade, lymph nodes involvement, tumor size, tumor extension and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827 respectively. Age, histologic type, surgery at primary site and grade were identified as the prognostic factors for the CSS. The AUC for the 3-and 5-year CSS were 0.790 and 0.740 respectively. Calibration curves revealed good agreements and Hosmer and Lemeshow test identified the models were well fitted. DCA curves demonstrated that nomograms were clinically useful.The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis, and make a personalized CSS evaluation for the lung metastasis subgroup.

    Keywords: spinal tumors, SEER database, Lung metastasis, survival analysis, nomogram

    Received: 31 Mar 2024; Accepted: 30 Jul 2024.

    Copyright: © 2024 Jiang, Zhu, Ding and Lu. 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:
    Yapeng Zhu, Department of Orthopaedic Surgery, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
    Yongli Ding, Department of Orthopaedic Surgery, First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan Province, China
    Xinchang Lu, Department of Orthopaedic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan Province, 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.