Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/5/742 |
| _version_ | 1827694497865138176 |
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| author | Rima Hajjo Dima A. Sabbah Sanaa K. Bardaweel Alexander Tropsha |
| author_facet | Rima Hajjo Dima A. Sabbah Sanaa K. Bardaweel Alexander Tropsha |
| author_sort | Rima Hajjo |
| collection | DOAJ |
| description | The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types. |
| first_indexed | 2024-03-10T12:06:49Z |
| format | Article |
| id | doaj.art-806dcfbe1b064b26a25368ca96ee56b5 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T12:06:49Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-806dcfbe1b064b26a25368ca96ee56b52023-11-21T16:32:28ZengMDPI AGDiagnostics2075-44182021-04-0111574210.3390/diagnostics11050742Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)Rima Hajjo0Dima A. Sabbah1Sanaa K. Bardaweel2Alexander Tropsha3Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, JordanDepartment of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, JordanDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Jordan, Amman 11942, JordanLaboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USAThe identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.https://www.mdpi.com/2075-4418/11/5/742biomarkersimagingmachine learningMRIoncology |
| spellingShingle | Rima Hajjo Dima A. Sabbah Sanaa K. Bardaweel Alexander Tropsha Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) Diagnostics biomarkers imaging machine learning MRI oncology |
| title | Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) |
| title_full | Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) |
| title_fullStr | Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) |
| title_full_unstemmed | Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) |
| title_short | Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML) |
| title_sort | identification of tumor specific mri biomarkers using machine learning ml |
| topic | biomarkers imaging machine learning MRI oncology |
| url | https://www.mdpi.com/2075-4418/11/5/742 |
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