Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

Journal: JCO clinical cancer informatics
PMID: 39854670

Abstract

PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.

Authors

  • Jesutofunmi A Fajemisin
    Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
  • John M Bryant
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
  • Payman G Saghand
    Object Computing Inc, St Louis, MO.
  • Matthew N Mills
    Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
  • Kujtim Latifi
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Eduardo G Moros
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Vladimir Feygelman
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Jessica M Frakes
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Sarah E Hoffe
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Kathryn E Mittauer
    Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL.
  • Tugce Kutuk
    Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL.
  • Rupesh Kotecha
    Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, FL.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
  • Stephen A Rosenberg
    Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.

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