Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From F-FDG PET/CT Based on Interpretable Machine Learning.

Journal: Academic radiology
PMID: 39665892

Abstract

PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). The model's interpretability was enhanced using Shapley additive explanations (SHAP).

Authors

  • Furui Duan
    PET/CT Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin, Heilongjiang, PR China.
  • Minghui Zhang
    Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
  • Chunyan Yang
    Research Institute of Extenics and Innovation Method, Guangdong University of Technology, Guangzhou, 510006, China.
  • Xuewei Wang
    Image Center Department, Affiliated Cancer Hospital of Harbin Medical University, 150 Haping Road, Nangang District, Harbin, Heilongjiang Province, PR China.
  • Dalong Wang
    PET/CT Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin, Heilongjiang, PR China. Electronic address: wangdalongbao@163.com.

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