Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.

Journal: BMC medical imaging
PMID:

Abstract

OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.

Authors

  • Wensong Shi
    Department of Thoracic Surgery, The fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
  • Yuzhui Hu
    Department of Geratology, Ninth People's Hospital of Zhengzhou, Zhengzhou, China.
  • Guotao Chang
    Department of Thoracic Surgery, The fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
  • He Qian
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: qianh@tsinghua.edu.cn.
  • Yulun Yang
    Department of Thoracic Surgery, The fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
  • Yinsen Song
    Translational Medicine Research Center (Key Laboratory of Organ Transplantation of Henan Province), The Fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.
  • Zhengpan Wei
    Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Liang Gao
    State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Hang Yi
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, PR China.
  • Sikai Wu
    Department of Hepatobiliary and Vascular Surgery, The First Affiliated Hospital, School of Clinical Medicine, Chengdu Medical College, Chengdu, Sichuan Province, P. R. China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Huandong Huo
    Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Shuaibo Wang
    Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
  • Yousheng Mao
    Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China.
  • Siyuan Ai
    Department of Thoracic Surgery, Liangxiang Hospital, Beijing, China.
  • Liang Zhao
    Graduate School of Advanced Integrated Studies in Human Survivability (Shishu-Kan), Kyoto University, Kyoto, Japan.
  • Xiangnan Li
    1 The Nursing College of Zhengzhou University, Zhengzhou 450052, China ; 2 Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
  • Huiyu Zheng
    Department of Thoracic Surgery, The fifth Clinical Medical College of Henan University of Chinese Medicine (Zhengzhou People's Hospital), Zhengzhou, China.