We are analyzing https://link.springer.com/article/10.1186/s12967-023-04437-4.

Title:
Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis | Journal of Translational Medicine
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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Keywords {🔍}

pubmed, cancer, radiomics, google, scholar, features, tumor, tme, cas, central, cell, patients, imaging, prognosis, immune, cells, radiomic, predict, model, studies, data, liu, microenvironment, wang, analysis, study, multiomics, breast, mri, response, immunotherapy, clinical, pathomics, research, prediction, lung, zhang, chen, expression, learning, molecular, transcriptomics, heterogeneity, tumorinfiltrating, based, machine, assessment, prognostic, tumors, genomics,

Topics {✒️}

gd-eob-dtpa-enhanced mri imaging immune-related interferon-gamma pathway dual-time-point fdg pet advanced driver-gene wild-type tumor-infiltrating cd20 b-cells fdg-pet/ct-based radiomics tumor-infiltrating cd8 t-cells contrast-enhanced computed tomography art full-slide scanner malignant lymph node triple-negative breast cancer pre-treatment mris captured tumour-infiltrating lymphocyte cytokine‒cytokine interaction pathway identified n7-methylguanosine modification dynamic contrast-enhanced mri digitize full-slide images tumor-infiltrating lymphocyte therapy small-cell lung cancer tumour-infiltrating lymphocytes scrna-seq analysis showed preoperative mri-based radiomics pd1 tumor-infiltrating lymphocytes myeloid-derived suppressor cells double-edged sword role single-cell rna sequencing deep learning-based radiomics mri-based radiomic features radiomics-based multiomics combinations predicted cytotoxic t-lymphocyte mri-based radiomics signature xgboost-based radiomics modeling long-chain noncoding rna evaluating tumor-infiltrating lymphocytes magnetic resonance radiomics supporting clinical decision-making evaluating tumor-infiltrating cd8 ct-based radiomics signature low-scoring subgroup differed evaluate tumor-infiltrating cd8 enhancing clinical decision-making tumor-infiltrating cd8 cells high-throughput sequencing results retrospective multi-cohort study e-stained biopsy slides predict pd-l1 status comprehensive multi-omics analysis pd-l1/pd-1 expression anti-pd-l1 immunotherapy tertiary lymphoid structures

Questions {❓}

Tumor microenvironment, radiology, and artificial intelligence: should we consider tumor periphery?

Schema {🗺️}

WebPage:
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         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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      headline:Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
      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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      name:Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, China
      name:Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
      name:Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
      name:Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
      name:Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
      name:Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
      name:Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

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