Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

Journal: Medical physics
Published Date: Sep 2, 2021

Abstract

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution.

Authors

  • Michele Avanzo
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Vito Gagliardi
    Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, Italy.
  • Joseph Stancanello
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Oliver Blanck
  • Giovanni Pirrone
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.
  • Alberto Revelant
    Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy.
  • Giovanna Sartor
    Division of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081, Aviano, PN, Italy.

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