Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

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...

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Bibliographic Details
Main Authors: Rima Hajjo, Dima A. Sabbah, Sanaa K. Bardaweel, Alexander Tropsha
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Diagnostics
Subjects:
biomarkers
imaging
machine learning
MRI
oncology
Online Access:https://www.mdpi.com/2075-4418/11/5/742
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spelling doaj-806dcfbe1b064b26a25368ca96ee56b52021-04-21T23:06:34ZengMDPI AGDiagnostics2075-44182021-04-011174274210.3390/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
collection DOAJ
language English
format Article
sources DOAJ
author Rima Hajjo
Dima A. Sabbah
Sanaa K. Bardaweel
Alexander Tropsha
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
author_facet Rima Hajjo
Dima A. Sabbah
Sanaa K. Bardaweel
Alexander Tropsha
author_sort Rima Hajjo
title Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)
title_short 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_sort identification of tumor-specific mri biomarkers using machine learning (ml)
publisher MDPI AG
series Diagnostics
issn 2075-4418
publishDate 2021-04-01
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.
topic biomarkers
imaging
machine learning
MRI
oncology
url https://www.mdpi.com/2075-4418/11/5/742
work_keys_str_mv AT rimahajjo identificationoftumorspecificmribiomarkersusingmachinelearningml
AT dimaasabbah identificationoftumorspecificmribiomarkersusingmachinelearningml
AT sanaakbardaweel identificationoftumorspecificmribiomarkersusingmachinelearningml
AT alexandertropsha identificationoftumorspecificmribiomarkersusingmachinelearningml
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