The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with indi...
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doaj-dea48d36d3fb43dda231b28a9582eaff2021-01-26T08:09:59ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-01-011010.3389/fonc.2020.570465570465The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in OncologyLin Shui0Haoyu Ren1Xi Yang2Jian Li3Ziwei Chen4Cheng Yi5Hong Zhu6Pixian Shui7Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, GermanyDepartment of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Pharmacy, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, ChinaDepartment of Nephrology, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, ChinaDepartment of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, ChinaSchool of Pharmacy, Southwest Medical University, Luzhou, ChinaWith the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.https://www.frontiersin.org/articles/10.3389/fonc.2020.570465/fullprecision medicinedeep learningartificial intelligenceradiogenomicsradiological imaging |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lin Shui Haoyu Ren Xi Yang Jian Li Ziwei Chen Cheng Yi Hong Zhu Pixian Shui |
spellingShingle |
Lin Shui Haoyu Ren Xi Yang Jian Li Ziwei Chen Cheng Yi Hong Zhu Pixian Shui The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology Frontiers in Oncology precision medicine deep learning artificial intelligence radiogenomics radiological imaging |
author_facet |
Lin Shui Haoyu Ren Xi Yang Jian Li Ziwei Chen Cheng Yi Hong Zhu Pixian Shui |
author_sort |
Lin Shui |
title |
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology |
title_short |
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology |
title_full |
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology |
title_fullStr |
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology |
title_full_unstemmed |
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology |
title_sort |
era of radiogenomics in precision medicine: an emerging approach to support diagnosis, treatment decisions, and prognostication in oncology |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-01-01 |
description |
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies. |
topic |
precision medicine deep learning artificial intelligence radiogenomics radiological imaging |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2020.570465/full |
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