PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Sep 19, 2023

Abstract

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

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.
  • Cheng Chen
    Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, 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.
  • Yangchun Chen
    Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Huazheng Shi
    Shanghai Universal Cloud Medical Imaging Diagnostic Center, 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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