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Review
. 2021 Apr 21;11(5):742.
doi: 10.3390/diagnostics11050742.

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

Rima Hajjo  1   2   3 , Dima A Sabbah  1 , Sanaa K Bardaweel  4 , Alexander Tropsha  2
Affiliations
Review

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

Rima Hajjo et al. Diagnostics (Basel). .

Abstract

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.

Keywords: MRI; biomarkers; imaging; machine learning; oncology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow for prioritizing ML MRI biomarkers.
Figure 2
Figure 2
Column chart showing the number of MRI articles based on the ML method used. (A) The total number of PubMed MRI articles based on the applied ML method. (B) The total number of PubMed Oncology MRI articles based on the applied ML method.
See this image and copyright information in PMC

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