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Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been i...

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Autores principales: Kang, Wendi, Qiu, Xiang, Luo, Yingen, Luo, Jianwei, Liu, Yang, Xi, Junqing, Li, Xiao, Yang, Zhengqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10481579/
https://www.ncbi.nlm.nih.gov/pubmed/37674169
http://dx.doi.org/10.1186/s12967-023-04437-4
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author Kang, Wendi
Qiu, Xiang
Luo, Yingen
Luo, Jianwei
Liu, Yang
Xi, Junqing
Li, Xiao
Yang, Zhengqiang
author_facet Kang, Wendi
Qiu, Xiang
Luo, Yingen
Luo, Jianwei
Liu, Yang
Xi, Junqing
Li, Xiao
Yang, Zhengqiang
author_sort Kang, Wendi
collection PubMed
description The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a “digital biopsy”. As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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spelling pubmed-104815792023-09-07 Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis Kang, Wendi Qiu, Xiang Luo, Yingen Luo, Jianwei Liu, Yang Xi, Junqing Li, Xiao Yang, Zhengqiang J Transl Med Review The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a “digital biopsy”. As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment. BioMed Central 2023-09-06 /pmc/articles/PMC10481579/ /pubmed/37674169 http://dx.doi.org/10.1186/s12967-023-04437-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Kang, Wendi
Qiu, Xiang
Luo, Yingen
Luo, Jianwei
Liu, Yang
Xi, Junqing
Li, Xiao
Yang, Zhengqiang
Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title_full Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title_fullStr Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title_full_unstemmed Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title_short Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
title_sort application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10481579/
https://www.ncbi.nlm.nih.gov/pubmed/37674169
http://dx.doi.org/10.1186/s12967-023-04437-4
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