Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.

Journal: Thoracic cancer
Published Date: May 14, 2023

Abstract

BACKGROUND: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information.

Authors

  • Xiaolei Zhang
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China; Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, China.
  • Xianling Dong
    Present Address: Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
  • M Iqbal Bin Saripan
    Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
  • Dongyang Du
    Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Yanjun Wu
    Institute of Software, Chinese Academy of Sciences.
  • Zhongxiao Wang
    Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
  • Zhendong Cao
    Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, China.
  • Dong Wen
    Center for Medical Informatics, Peking University, Beijing, China.
  • Yanli Liu
    Baodi Clinical College, Tianjin Medical University, 8 Guangchuan Road, Tianjin, 301800, China.
  • Mohammad Hamiruce Marhaban
    Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Seri Kembangan, Selangor 43400, Malaysia.

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