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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1431150
This article is part of the Research Topic Clinical Implementation of Precision Oncology Data to Direct Individualized and Immunotherapy-Based Treatment Strategies View all 3 articles

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO Regression

Provisionally accepted
Yimeng Guo Yimeng Guo 1Lihua Li Lihua Li 1Keao Zheng Keao Zheng 2Juan Du Juan Du 1Jingxu Nie Jingxu Nie 1Zanhong Wang Zanhong Wang 3*Zhiying Hao Zhiying Hao 1*
  • 1 Department of Pharmacy, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi Province, China
  • 2 School of Pharmacy, Shanxi Medical University, Taiyuan, Shanxi Province, China
  • 3 Department of Obstetrics and Gynecology, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China

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

    Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. Methods: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models.. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). Results: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous Variable selection was performed using univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis.

    Keywords: Non-small cell lung cancer, LASSO regression, nomogram, Prediction model, Random survival forest

    Received: 11 May 2024; Accepted: 19 Jul 2024.

    Copyright: © 2024 Guo, Li, Zheng, Du, Nie, Wang and Hao. 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:
    Zanhong Wang, Department of Obstetrics and Gynecology, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, 030032, Shanxi Province, China
    Zhiying Hao, Department of Pharmacy, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi 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.