F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.

Journal: Journal of cancer research and clinical oncology
PMID: 40153023

Abstract

PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to predict treatment response in elderly patients with diffuse large B-cell lymphoma (DLBCL).

Authors

  • Jincheng Zhao
    Department of Hematology, School of Basic Medicine and Clinical Pharmacy, Nanjing Drum Tower Hospital, China Pharmaceutical University, Nanjing, China.
  • Wenzhuo Zhao
    The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Man Chen
    Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China.
  • Jian Rong
    Boston University and NHLBI's Framingham Study, MA (J.R., M.G.L., V.X., R.S.V.).
  • Yue Teng
    Haidian Maternal & Child Health Hospital Nutrition Clinic, Beijing 100080, China.
  • Jianxin Chen
    Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: cjx@bucm.edu.cn.
  • Jingyan Xu
    Department of Radiology, Johns Hopkins University, United States of America.

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