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

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 analys...

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Detalles Bibliográficos
Autores principales: Qin, Yun, Zhu, Li-Hua, Zhao, Wei, Wang, Jun-Jie, Wang, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395725/
https://www.ncbi.nlm.nih.gov/pubmed/36016617
http://dx.doi.org/10.3389/fonc.2022.913683
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author Qin, Yun
Zhu, Li-Hua
Zhao, Wei
Wang, Jun-Jie
Wang, Hao
author_facet Qin, Yun
Zhu, Li-Hua
Zhao, Wei
Wang, Jun-Jie
Wang, Hao
author_sort Qin, Yun
collection PubMed
description 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 (125)I CT-guided radioactive seed implant brachytherapy.
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spelling pubmed-93957252022-08-24 Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer Qin, Yun Zhu, Li-Hua Zhao, Wei Wang, Jun-Jie Wang, Hao Front Oncol Oncology 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 (125)I CT-guided radioactive seed implant brachytherapy. Frontiers Media S.A. 2022-08-09 /pmc/articles/PMC9395725/ /pubmed/36016617 http://dx.doi.org/10.3389/fonc.2022.913683 Text en Copyright © 2022 Qin, Zhu, Zhao, Wang and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Qin, Yun
Zhu, Li-Hua
Zhao, Wei
Wang, Jun-Jie
Wang, Hao
Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title_full Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title_fullStr Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title_full_unstemmed Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title_short Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer
title_sort review of radiomics- and dosiomics-based predicting models for rectal cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395725/
https://www.ncbi.nlm.nih.gov/pubmed/36016617
http://dx.doi.org/10.3389/fonc.2022.913683
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