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What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Me...

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Autores principales: Mirón Mombiela, Rebeca, Arildskov, Anne Rix, Bruun, Frederik Jager, Hasselbalch, Lotte Harries, Holst, Kristine Bærentz, Rasmussen, Sine Hvid, Borrás, Consuelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224495/
https://www.ncbi.nlm.nih.gov/pubmed/35742947
http://dx.doi.org/10.3390/ijms23126504
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author Mirón Mombiela, Rebeca
Arildskov, Anne Rix
Bruun, Frederik Jager
Hasselbalch, Lotte Harries
Holst, Kristine Bærentz
Rasmussen, Sine Hvid
Borrás, Consuelo
author_facet Mirón Mombiela, Rebeca
Arildskov, Anne Rix
Bruun, Frederik Jager
Hasselbalch, Lotte Harries
Holst, Kristine Bærentz
Rasmussen, Sine Hvid
Borrás, Consuelo
author_sort Mirón Mombiela, Rebeca
collection PubMed
description (1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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spelling pubmed-92244952022-06-24 What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies Mirón Mombiela, Rebeca Arildskov, Anne Rix Bruun, Frederik Jager Hasselbalch, Lotte Harries Holst, Kristine Bærentz Rasmussen, Sine Hvid Borrás, Consuelo Int J Mol Sci Review (1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients. MDPI 2022-06-10 /pmc/articles/PMC9224495/ /pubmed/35742947 http://dx.doi.org/10.3390/ijms23126504 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Mirón Mombiela, Rebeca
Arildskov, Anne Rix
Bruun, Frederik Jager
Hasselbalch, Lotte Harries
Holst, Kristine Bærentz
Rasmussen, Sine Hvid
Borrás, Consuelo
What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title_full What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title_fullStr What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title_full_unstemmed What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title_short What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
title_sort what genetics can do for oncological imaging: a systematic review of the genetic validation data used in radiomics studies
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9224495/
https://www.ncbi.nlm.nih.gov/pubmed/35742947
http://dx.doi.org/10.3390/ijms23126504
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