Journal
CANCERS
Volume 13, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/cancers13050973
Keywords
radiogenomics; machine learning; deep learning; oncologic imaging; genomics
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Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics, which combines high-throughput quantitative data from medical imaging with molecular analysis. This approach is expected to advance personalized medicine but is still in its early stages with many challenges to overcome. The integration of image-derived features-radiomics and genomic profiles-genomics is a rapidly evolving field known as radiogenomics, with various studies dedicated to colorectal cancer showing potential for enhancing clinical decision-making.
Simple Summary: Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments. The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features-radiomics and genetic profile modifications-genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.
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