Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Jan 12, 2024

Abstract

BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learning to predict OLNM preoperatively in SPILAC patients across multiple centers.

Authors

  • Weiwei Tian
    Academy for Engineering and Technology, Fudan University, No. 220 Handan Road, Shanghai, 200433, China.
  • Qinqin Yan
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
  • Xinyu Huang
    Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, Lübeck 23538, Germany. Electronic address: huang@imi.uni-luebeck.de.
  • Rui Feng
    Department of Pharmacy, The Fourth Hospital of Hebei Medical University Shijiazhuang 050000, Hebei, China.
  • Fei Shan
    Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
  • Daoying Geng
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China. GengdaoyingGDY@163.com.
  • Zhiyong Zhang

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