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

Abstract

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.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer

“…The study revealed that the multimodal model achieved an AUC value of 0.80, surpassing the predictive power of any individual variable. Furthermore, Kang et al ( 2023 ). argue that multi-omics offers substantial advantages for conducting a comprehensive evaluation of tumor patients.…”

A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features

“…In the future, integrating different types of data, such as clinical data (such as tumor markers and genetic information) with radiomics and pathomics data, will be a crucial direction for research [ 37 , 38 ]. This comprehensive approach is expected to enhance the accuracy and predictive capabilities of models, providing more precise guidance for personalized therapy and disease management.…”

“…With continuous advancements in artificial intelligence and machine learning, researchers will be able to build more complex and sophisticated models. These models integrate various omics data, such as genomics, proteomics, radiomics, and pathomics, leading to a more comprehensive understanding of cancer biological characteristics and progression mechanisms [ 38 , 39 ]. The development of comprehensive predictive models will support personalized treatment strategies, offering more hope for patient prognosis.…”

Radiomics Models of Dynamic Contrast-Enhanced MRI for Evaluation of the Expression Levels of CD3+, CD4+ & CD8+ Tumor-Infiltrating Lymphocytes in Advanced Gastric Carcinoma

“…Imaging, on the other hand, can offer a comprehensive assessment of the overall anatomical structure and functional properties of tumor tissue(Borggreve et al 2019). Much earlier research has shown that radiomics may accurately predict the immune microenvironment in a variety of malignancies using various imaging modalities(Yu et al 2021;Kang et al 2023). DCE-MRI technology was used in our study to build a predictive model.…”