Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

Journal: BMC bioinformatics
Published Date: Feb 6, 2023

Abstract

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations. The purpose of our study was to improve survival prediction in patients with NSCLC by incorporating prognostic information from F-18 fluorodeoxyglucose positron emission tomography (FDG PET) images into a traditional survival prediction model using clinical data.

Authors

  • Seungwon Oh
    Department of Mathematics and Statistics, Chonnam National University, Gwangju, Republic of Korea.
  • Sae-Ryung Kang
    Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun 58128, Korea.
  • In-Jae Oh
    Department of Internal Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun 58128, Korea.
  • Min-Soo Kim

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