The impact of intensity discretisation and filtration on the performance of the radiomic and machine learning models in brain metastasis patients treated with gamma knife radiosurgery.

Journal: Radiography (London, England : 1995)
Published Date:

Abstract

INTRODUCTION: Image preprocessing is crucial for optimizing radiomics feature extraction, however, inconsistencies in the implementation process and a lack of universally accepted methods lead to diverse approaches. This study evaluates the impact of radiomics and machine learning (ML) performance in brain metastasis.

Authors

  • A Umaru
    Diagnostic Imaging and Radiotherapy, CODTIS, Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia; Department of Medical Radiography, Ahmadu Bello University Zaria, Nigeria.
  • H A Manan
    Department of Radiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia.
  • R K A Kumar
    Gamma Knife Centre, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • S K Hamsan
    Gamma Knife Centre, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
  • N Yahya
    Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia. Electronic address: azrulyahya@ukm.edu.my.

Keywords

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