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Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

  • Yun Qin
  • , Li Hua Zhu
  • , Wei Zhao
  • , Jun Jie Wang*
  • , Hao Wang*
  • *Corresponding author for this work
  • Beihang University
  • Peking University

Research output: Contribution to journalReview articlepeer-review

Abstract

By breaking the traditional medical image analysis framework, precision medicine–radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.

Original languageEnglish
Article number913683
JournalFrontiers in Oncology
Volume12
DOIs
StatePublished - 9 Aug 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • deep learning
  • dosiomics
  • machine learning
  • radiomics
  • rectal cancer

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