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Deep Learning: A Review for the Radiation Oncologist.

Luca Boldrini1, Jean-Emmanuel Bibault2, Carlotta Masciocchi1

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Deep Learning (DL) is revolutionizing radiation oncology by improving image segmentation and predicting patient outcomes. While promising, widespread clinical adoption of these advanced AI techniques is still under evaluation.

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Area of Science:

  • Radiation Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep Learning (DL), a subset of machine learning utilizing deep neural networks, offers advanced modeling capabilities.
  • Radiation oncology applications for DL include image segmentation, outcome prediction, and treatment planning.
  • The use of DL in radiation oncology has seen a significant surge, particularly from 2015 onwards.

Purpose of the Study:

  • To review the methods and applications of Deep Learning in radiation oncology.
  • To synthesize recent literature on DL in radiation oncology for a clinically-oriented audience.
  • To identify trends and the current status of DL implementation in the field.

Main Methods:

  • A literature review was conducted using PubMed/Medline with search terms 'radiotherapy' and 'deep learning'.
  • The search identified studies published between 1997 and 2018, with a focus on recent publications.
  • Reference lists of selected articles were hand-searched to ensure comprehensive coverage.

Main Results:

  • DL applications in image segmentation were found across various cancer sites, including Brain, Head and Neck, Lung, Abdominal, and Pelvic cancers.
  • DL has been utilized for predicting clinical outcomes, such as toxicity modeling, treatment response, survival, and aiding in treatment planning.
  • The majority of identified studies were published from 2015 to 2018, indicating rapid recent development.

Conclusions:

  • Deep Learning techniques show significant potential to enhance radiation oncology practices by improving efficiency and accuracy.
  • DL-based systems can assist clinicians by reducing segmentation time and variability, and by predicting treatment outcomes and toxicities.
  • Further evaluation is needed to determine the timeline for the routine clinical integration of these advanced DL technologies.