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A Survey on Deep Learning for Precision Oncology.

Ching-Wei Wang1,2, Muhammad-Adil Khalil2, Nabila Puspita Firdi1

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Deep learning is revolutionizing precision oncology by optimizing cancer treatments. This review covers over 150 studies on deep learning applications in personalized cancer care, from treatment planning to response prediction.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Precision oncology tailors cancer treatment to individual disease biology.
  • Deep learning (DL) has emerged as a primary methodology in precision oncology.
  • The clinical importance of precision oncology is rapidly growing.

Purpose of the Study:

  • To summarize recent deep learning approaches in precision oncology.
  • To review over 150 relevant articles published in the last six years.
  • To identify challenges and future research directions in DL for precision oncology.

Main Methods:

  • Categorization of DL approaches by precision oncology tasks.
  • Overview of studies based on anatomical areas.
  • Systematic literature review of DL applications in cancer treatment.

Main Results:

  • DL methods are applied to dose distribution estimation, survival analysis, risk estimation, treatment response prediction, and patient selection.
  • Studies span multiple anatomical regions including brain, lung, breast, and prostate.
  • Key challenges and future research avenues are identified.

Conclusions:

  • Deep learning is a pivotal tool in advancing precision oncology.
  • Further research is needed to address current challenges and enhance DL applications.
  • DL holds significant promise for improving patient outcomes in personalized cancer care.