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

被引:0
|
作者
Wendi Kang
Xiang Qiu
Yingen Luo
Jianwei Luo
Yang Liu
Junqing Xi
Xiao Li
Zhengqiang Yang
机构
[1] Chinese Academy of Medical Sciences and Peking Union Medical College,Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital
[2] Fudan University,Obstetrics and Gynecology Hospital of
[3] Central South University,Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine
[4] Chinese Academy of Medical Sciences and Peking Union Medical College,Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital
来源
Journal of Translational Medicine | / 21卷
关键词
Multiomics combination; Radiomics; Biomarkers; Tumor microenvironment; Cancer prognosis;
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学科分类号
摘要
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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