Radiogenomics in personalized management of lung cancer patients: Where are we? Review
| Authors: | Araujo-Filho, J. A. B.; Mayoral, M.; Horvat, N.; Santini, F. C.; Gibbs, P.; Ginsberg, M. S. |
| Review Title: | Radiogenomics in personalized management of lung cancer patients: Where are we? |
| Abstract: | With the rise of artificial intelligence, radiomics has emerged as a field of translational research based on the extraction of mineable high-dimensional data from radiological images to create “big data” datasets for the purpose of identifying distinct sub-visual imaging patterns. The integrated analysis of radiomic data and genomic data is termed radiogenomics, a promising strategy to identify potential imaging biomarkers for predicting driver mutations and other genomic parameters. In lung cancer, recent advances in whole-genome sequencing and the identification of actionable molecular alterations have led to an increased interest in understanding the complex relationships between imaging and genomic data, with the potential of guiding therapeutic strategies and predicting clinical outcomes. Although the integration of the radiogenomics data into lung cancer management may represent a new paradigm in the field, the use of this technique as a clinical biomarker remains investigational and still necessitates standardization and robustness to be effectively translated into the clinical practice. This review summarizes the basic concepts, potential contributions, challenges, and opportunities of radiogenomics in the management of patients with lung cancer. © 2022 |
| Keywords: | treatment response; gene mutation; review; validation process; cancer patient; biomarkers; lung cancer; patient monitoring; prediction; standardization; genomics; image processing; biological organs; diseases; translational research; personalized medicine; image segmentation; clinical outcome; feature extraction; integrated analysis; cancer patients; clustering algorithms; workflow; genomic data; human; whole genome sequencing; radiogenomics; feature selection; radiogenomic; high dimensional data; radiomics; large dataset; radiomic; radiological images; visual imaging |
| Journal Title: | Clinical Imaging |
| Volume: | 84 |
| ISSN: | 0899-7071 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2022-04-01 |
| Start Page: | 54 |
| End Page: | 60 |
| Language: | English |
| DOI: | 10.1016/j.clinimag.2022.01.012 |
| PUBMED: | 35144039 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Review -- Export Date: 1 March 2022 -- Source: Scopus |
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235Ginsberg -
22Santini -
101Horvat -
33Gibbs -
21Araujo Filho
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