Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

Journal: Tomography (Ann Arbor, Mich.)
PMID: 39330753

Abstract

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.

Authors

  • Jesutofunmi Ayo Fajemisin
    Department of Physics, University of South Florida, Tampa, FL 33620, USA.
  • Glebys Gonzalez
    Machine Learning Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
  • Stephen A Rosenberg
    Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
  • Ghanim Ullah
    Department of Physics, University of South Florida, Tampa, FL 33620, USA.
  • Gage Redler
    Radiation Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
  • Kujtim Latifi
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Eduardo G Moros
    Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, USA.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.

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