Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.

Journal: European journal of radiology
Published Date: Jun 25, 2020

Abstract

PURPOSE: Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the presence of a micropapillary or a solid component is identified as an independent predictor of prognosis, suggesting a more extensive resection. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC.

Authors

  • Xing Wang
    Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Lei Tang
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Gaoxiang Chen
    The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Shi Yan
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
  • Shaolei Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
  • Yue Yang
    Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China.
  • Yue Kang
    Linkdoc AI Research (LAIR), Building A, Sinosteel International Plaza, No.8 Haidian Street, Haidian District, Beijing, China.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.

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