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A Review of Radiomics and Genomics Applications in Cancers: the Way Towards Precision Medicine

Overview
Journal Radiat Oncol
Publisher Biomed Central
Specialties Oncology
Radiology
Date 2022 Dec 30
PMID 36585716
Authors
Affiliations
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Abstract

The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

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References
1.
Pinker K, Chin J, Melsaether A, Morris E, Moy L . Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology. 2018; 287(3):732-747. DOI: 10.1148/radiol.2018172171. View

2.
Zwanenburg A, Leger S, Agolli L, Pilz K, Troost E, Richter C . Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019; 9(1):614. PMC: 6345842. DOI: 10.1038/s41598-018-36938-4. View

3.
Nematzadeh S, Kiani F, Torkamanian-Afshar M, Aydin N . Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases. Comput Biol Chem. 2022; 97:107619. DOI: 10.1016/j.compbiolchem.2021.107619. View

4.
Chen M, Yin F, Yu Y, Zhang H, Wen G . CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma. Cancer Imaging. 2021; 21(1):42. PMC: 8220848. DOI: 10.1186/s40644-021-00412-8. View

5.
He H, Razlighi Q . Landmark-guided region-based spatial normalization for functional magnetic resonance imaging. Hum Brain Mapp. 2022; 43(11):3524-3544. PMC: 9248321. DOI: 10.1002/hbm.25865. View