Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases.

Journal: Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
Published Date: Jan 28, 2024

Abstract

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks.

Authors

  • Shanshan Shen
    Department of Gastroenterology, Nanjing Drum Tower Hospital, Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210008, China; Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210008, China.
  • Chunquan Li
  • Yaohua Fan
    Department of Oncology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000.
  • Shanfu Lu
  • Ziye Yan
  • Hu Liu
    School of Instrument Science and Opto-electronic Engineering, Beihang University, Beijing, 10091, China.
  • Haihang Zhou
    Department of Neurosurgery, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000.
  • Zijian Zhang
    School of Physics, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

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