Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

Journal: Journal of computer assisted tomography
Published Date: May 26, 2023

Abstract

OBJECTIVE: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification.

Authors

  • Ling Yun Yeow
    Signal and Image Processing Group, Institute of Bioengineering and Bioimaging, 02-02, Helios,11, Biopolis Way, Singapore, 138667, Singapore.
  • Yu Xuan Teh
    Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University.
  • Xinyu Lu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Arvind Channarayapatna Srinivasa
    From the Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR).
  • Eelin Tan
    Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore.
  • Timothy Shao Ern Tan
    Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore.
  • Phua Hwee Tang
    Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore.
  • Bhanu Prakash Kn
    Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore. bhanu_prakash@bii.a-star.edu.sg.

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