Application of Radiomics in Radiotherapy: Challenges and Future Prospects
Background and Objective: Specific treatment for each patient based on their clinical data is one of the medical prospects of the future. Using data mining and machine learning techniques based on computer science in extracting the quantitative features of an image to improve the process of diagnosi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Babol University of Medical Sciences
2022-03-01
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| Series: | Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul |
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| Online Access: | http://jbums.org/article-1-10150-en.html |
| _version_ | 1828127418543505408 |
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| author | SH Mousavie Anijdan R Reiazi H Fallah Tafti D Moslemi AA Moghadamnia R Paydar |
| author_facet | SH Mousavie Anijdan R Reiazi H Fallah Tafti D Moslemi AA Moghadamnia R Paydar |
| author_sort | SH Mousavie Anijdan |
| collection | DOAJ |
| description | Background and Objective: Specific treatment for each patient based on their clinical data is one of the medical prospects of the future. Using data mining and machine learning techniques based on computer science in extracting the quantitative features of an image to improve the process of diagnosis, prognosis, prediction and response to cancer treatment is known as radiomics. This article examines the workflow, findings, challenges ahead, and the role of radiomics in precision medicine and individual therapy.
Methods: In this review article, we searched well-known indexes such as ISC, web of science, Google Scholar, Scopus, PubMed without time limit and based on the keywords radiomics, radiotherapy, cancer and quantitative imaging and relevant articles were collected.
Findings: Radiomics is a combination of everyday computer-aided diagnosis, machine learning methods, deep learning and human skills that can be used for quantitative description of the phenotypes of cancerous tumors. Image collection and processing, tumor segmentation, extraction of features, processing and modeling are some of the basic steps of the process of radiomics. Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound (US) methods are among the used images.
Conclusion: According to the results of this study, the prerequisite for the clinical implementation of radiomics is the elimination of deficiencies such as the dependence of the features on the imaging parameters, and the unrepeatability of the features. Therefore, a comprehensive approach should be adopted, stable and reproducible patterns should be developed to accept radiomics as a clinical prognostic tool. |
| first_indexed | 2024-04-11T15:49:21Z |
| format | Article |
| id | doaj.art-a19e6eaf7b184bbf8a36ca712bec05b2 |
| institution | Directory Open Access Journal |
| issn | 1561-4107 2251-7170 |
| language | English |
| last_indexed | 2024-04-11T15:49:21Z |
| publishDate | 2022-03-01 |
| publisher | Babol University of Medical Sciences |
| record_format | Article |
| series | Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul |
| spelling | doaj.art-a19e6eaf7b184bbf8a36ca712bec05b22022-12-22T04:15:21ZengBabol University of Medical SciencesMajallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul1561-41072251-71702022-03-01241127140Application of Radiomics in Radiotherapy: Challenges and Future ProspectsSH Mousavie Anijdan0R Reiazi1H Fallah Tafti2D Moslemi3AA Moghadamnia4R Paydar5 1.Radiological Technology Department, Faculty of Allied Medical Sciences, Babol University of Medical Sciences, Babol, I.R.Iran. 2.Princess Margaret Cancer Centre, Toronto University, Toronto, Ontario, Canada. 3.Shahid Rajaee Babolsar Radiotherapy Center, Babol University of Medical Sciences, Babol, I.R.Iran. 4.Department of Radiation Oncology, School of Medicine, Babol University of Medical Sciences, Babol, I.R.Iran. 5.Cancer Research Center, Health Research Institute, Babol University of Medical Sciences, Babol I.R.Iran. 6.Department of Radiation Sciences, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, I.R.Iran. Background and Objective: Specific treatment for each patient based on their clinical data is one of the medical prospects of the future. Using data mining and machine learning techniques based on computer science in extracting the quantitative features of an image to improve the process of diagnosis, prognosis, prediction and response to cancer treatment is known as radiomics. This article examines the workflow, findings, challenges ahead, and the role of radiomics in precision medicine and individual therapy. Methods: In this review article, we searched well-known indexes such as ISC, web of science, Google Scholar, Scopus, PubMed without time limit and based on the keywords radiomics, radiotherapy, cancer and quantitative imaging and relevant articles were collected. Findings: Radiomics is a combination of everyday computer-aided diagnosis, machine learning methods, deep learning and human skills that can be used for quantitative description of the phenotypes of cancerous tumors. Image collection and processing, tumor segmentation, extraction of features, processing and modeling are some of the basic steps of the process of radiomics. Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound (US) methods are among the used images. Conclusion: According to the results of this study, the prerequisite for the clinical implementation of radiomics is the elimination of deficiencies such as the dependence of the features on the imaging parameters, and the unrepeatability of the features. Therefore, a comprehensive approach should be adopted, stable and reproducible patterns should be developed to accept radiomics as a clinical prognostic tool.http://jbums.org/article-1-10150-en.htmlradiomicsradiotherapycancerquantitative imaging. |
| spellingShingle | SH Mousavie Anijdan R Reiazi H Fallah Tafti D Moslemi AA Moghadamnia R Paydar Application of Radiomics in Radiotherapy: Challenges and Future Prospects Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul radiomics radiotherapy cancer quantitative imaging. |
| title | Application of Radiomics in Radiotherapy: Challenges and Future Prospects |
| title_full | Application of Radiomics in Radiotherapy: Challenges and Future Prospects |
| title_fullStr | Application of Radiomics in Radiotherapy: Challenges and Future Prospects |
| title_full_unstemmed | Application of Radiomics in Radiotherapy: Challenges and Future Prospects |
| title_short | Application of Radiomics in Radiotherapy: Challenges and Future Prospects |
| title_sort | application of radiomics in radiotherapy challenges and future prospects |
| topic | radiomics radiotherapy cancer quantitative imaging. |
| url | http://jbums.org/article-1-10150-en.html |
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