PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Journal: Nature communications
Published Date: Nov 18, 2023

Abstract

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Authors

  • Yifan Zhong
    Division of Thyroid Surgery, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine On Differentiated Thyroid Carcinoma, China-Japan Union Hospital Of Jilin University, Changchun, 130000, People's Republic of China.
  • Chuang Cai
    College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China.
  • Tao Chen
    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
  • Hao Gui
    Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.
  • Jiajun Deng
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Minglei Yang
    Biomedical Engineering, CT Collaboration of Siemens Healthineers, No. 278, Zhouzhu Road, Pudong New District, Shanghai, 201318, People's Republic of China.
  • Bentong Yu
    Department of Thoracic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
  • Yongxiang Song
    From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song).
  • Tingting Wang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Xiwen Sun
    Department of Radiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Jingyun Shi
    Dept of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Yangchun Chen
    Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Dong Xie
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Chang Chen
    Biomass Energy and Environmental Engineering Research Center, College of Chemical Engineering, Beijing University of Chemical Technology, 505 Zonghe Building A, 15 North 3rd Ring East Road, Beijing, 100029, China. chenchang@mail.buct.edu.cn.
  • Yunlang She
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.

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