Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 40075547

Abstract

PURPOSE: This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-small cell lung cancer (NSCLC). The results of robust features were also compared with conventional techniques without considering the robustness of radiomic features.

Authors

  • Seyyed Ali Hosseini
    Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, Québec, Canada.
  • Ghasem Hajianfar
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
  • Brandon Hall
    Translational Neuroimaging Laboratory, Douglas Hospital, The McGill University Research Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.
  • Stijn Servaes
    Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, Québec, Canada.
  • Pedro Rosa-Neto
    Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; McGill University Research Centre for Studies in Aging (MCSA), Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Douglas Research Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Neurology & Neurosurgery, McGill University, Montreal, Quebec, Canada. Electronic address: pedro.rosa@mcgill.ca.
  • Pardis Ghafarian
    Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran; PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical, Tehran, Iran. Electronic address: pardis.ghafarian@sbmu.ac.ir.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.
  • Mohammad Reza Ay
    Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran.

Keywords

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