Mohammad Farhadi Rad,1,*Hossein Azadinejad,2Mahmood Mohammadi Sadr,3Mohammad Ghaderian,4Mahboobeh soleimanpoor,5
1. Department of Radiology and nuclear medicine, School of paramedical, Kermanshah University of Medical Sciences, Kermanshah, Iran 2. Department of immunology, school of medicine, Kermanshah university of medical sciences , Kermanshah, Iran 3. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 4. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 5. Department of Radiology Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Introduction: Precision medicine aims to increase the quality of healthcare by tailoring the healthcare process to each patient's uniquely evolving health condition. This work spans a wide range of scientific disciplines, including drug discovery, genetics, radiomics, etc...
Imaging plays a vital role not only in diagnosing and staging cancer but also in planning radiation therapy and assessment of treatment response. Furthermore, the role of imaging in precision medicine cannot be ignored.
Radiomics is a quantitative approach to medical imaging that aims to relate large-scale extracted imaging information to biological and clinical endpoints. Disease detection, diagnosis, prognosis, and therapy response assessment/prediction are its applications. the possibility of translating data science research into more personalized cancer treatments has been opened up by the expansion of quantitative imaging methods along with machine learning. As radiation therapy aims for more personalized treatment, radiomics can play a key role at various stages before, during, and after treatment.
Methods: To conduct this article Scopus, PubMed, and google scholar databases have been searched in the period from 2018 to 2022 using "Radiomics", "precision medicine" and "radiotherapy" keywords. Duplicated titles were removed by endnote software and after checking abstracts related articles were reviewed.
Results: Radiation therapy offers unlimited possibilities for cancer treatment. There is a growing and urgent need to implement individualized radiotherapy strategies thus, radiomics has been extensively studied in radiotherapy and clinical studies have shown the role of radiomic features analysis as a source of information with the potential to impact the radiation oncology practice. These features can be used as a biomarker to predict patient prognosis, treatment response, and underlying genetic changes. precise and robust machine learning, deep learning algorithms, or statistical techniques by creating classification or predictive models make radiomic features more useful for clinical applications.
Conclusion: Radiomic can be used by Artificial intelligence to develop individualized radiotherapy. The trend of many clinical studies has shown that the future is the era of precision medicine using artificial intelligence. Despite the progress in precision medicine, there is still a long way to achieve a precisely personalized treatment of cancer. Furthermore, radiomic are able to be linked with genomic, metabolomic, and other information to improve the ability of precision medicine
Keywords: Radiomics, precision medicine, and radiotherapy