Multimodal deep learning model on interim [F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma.

Journal: European radiology
PMID: 36029345

Abstract

OBJECTIVES: The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as a prominent means for improving front-line outcomes. Using interim F-fluoro-2-deoxyglucose ([F]FDG) positron emission tomography/computed tomography (PET/CT) imaging data, we aimed to construct multimodal deep learning (MDL) models to predict possible PTF in low-risk DLBCL.

Authors

  • Cheng Yuan
    Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.
  • Qing Shi
    Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, People's Republic of China. Centre for Advanced Biomedical Sciences, TWInS, Waseda University, Tokyo,162-8480, Japan.
  • Xinyun Huang
    Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Yang He
    Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhenjiang Province, China.
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Weili Zhao
    Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. zhao.weili@yahoo.com.
  • Dahong Qian

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