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Year : 2018  |  Volume : 9  |  Issue : 4  |  Page : 131

Radiogenomics a New Marker on the Block

Department of Radiation Oncology, Nanavati Super Speciality Hospital, Mumbai, Maharashtra, India

Date of Web Publication12-Mar-2019

Correspondence Address:
Dr. Nagraj Gururaj Huilgol
Department of Radiation Oncology, Nanavati Super Speciality Hospital, Mumbai, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jrcr.jrcr_30_18

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How to cite this article:
Huilgol NG. Radiogenomics a New Marker on the Block. J Radiat Cancer Res 2018;9:131

How to cite this URL:
Huilgol NG. Radiogenomics a New Marker on the Block. J Radiat Cancer Res [serial online] 2018 [cited 2021 Jan 15];9:131. Available from: https://www.journalrcr.org/text.asp?2018/9/4/131/254003

Looking for a singular marker for prognostication, a unique marker to indicate the biological essence and markers for early detection are being perused. The clinical staging, histological morphology, grading, stratification, and expression of the membrane receptor are some of the ways to comprehend a given neoplasm. The composite of these variables has certainly helped creating the evidence and guidelines for therapeutics, which goes by the name “standard of care.” This has served us well but is far from being perfect. The idea of standard of care addresses a statistical mean and not the individual patient. Interesting research in genomics of neoplasm has opened up a possibility of individualizing the treatment for patients. Radiogenomics is the science of correlating imaging phenotype with genomics. Radiomics or radiogenomics involves the acquisition of clinical standard of care images such as computerized tomography, positron-emission tomography, and magnetic resonance imaging followed by manual or semi-automated segmentation. It is followed by the feature extraction such as shape and texture. Statistical analysis and predictive models are then evolved. The purported model undergoes internal and external validation.

Radiomics is rapidly evolving since over a decade. Newer developments in neural networks, data mining, and deep learning have helped dive into an unexplored mine of data. Imaging correlates with specific genotype or molecular phenotype of the tumor can help either prognosticate or individualize the treatment. Radiomic signatures can also help to classify tumors with a view to predicting outcomes following treatment. This can help optimize as well as individualize the treatment. The entire quest in radiomic or genomics is to develop models to help choose the most suitable modality, an ever-evolving part of precision medicine. Genome-guided oncology has ushered in a new era of cancer therapeutics. Next-generation sequencing (NGS), has unmasked new information regarding genomic drivers of carcinogenesis and progression. Machine learning and newer analytical methods help to make sense of the plethora of data in a clinical context.

There is an emergence of genomics in oncology. The therapeutics options such as trastuzumab for Her2 positive cancer and imatinib in the treatment of CML with BCR-ABL1 were only the beginning. Following are some of the actionable genes against which therapies have been developed. Some of them are EGFR, AIK, and ROS for lung cancer, BRAF in melanoma, GIST in gastrointestinal stromal cancer, and PDGFRA for leukemia. Recent developments in technology like Next-generation sequencing (NGS) platform have identified a plethora of genetic alterations of which only a few are either validated or actionable. This technology can reveal sequence mutations, small insertions and deletions, copy number alterations, structural rearrangements, and loss of heterozygosity in tumor DNA samples.[1] The genomics which is based on the association of genetic variants and tumor types and outcomes is still in its infancy.

Radiogenomics, as mentioned above, attempts to evaluate genomic phenotypes on the basis of texture analysis. The advantage of radiomics is its potential to look globally. The errors due to sampling are mitigated. Radiogenomics as a part of the multi-parametric method of the diagnosis and classification of tumors will be one more avenue in precision medicine. The new kid on the block of genomics has an optimistic future.

  References Top

Berger MF, Mardis ER. The emerging clinical relevance of genomics in cancer medicine. Nat Rev Clin Oncol 2018;15:353-65.  Back to cited text no. 1


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