Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.

Journal: Journal of cancer research and clinical oncology
Published Date: Jun 17, 2025

Abstract

BACKGROUND: With the improvement of imaging, the screening rate of Pulmonary nodules (PNs) has further increased, but their identification of High-Risk Prognostic Pathological Components (HRPPC) is still a major challenge. In this study, we aimed to build a multi-parameter machine learning predictive model to improve the discrimination accuracy of HRPPC.

Authors

  • Dingxin Wang
    Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
  • Jianhao Qiu
    Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
  • Rongyang Li
    Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China.
  • Hui Tian
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.

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