Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
Key points
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Imaging biomarkers offer the opportunity to move precision diagnostics forward, enabling better informed medical decision-making and tracking of biological changes before, during and after brain tumour treatment.
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Guidelines and standards for data acquisition, image processing and validation processes for the development and eventual implementation of imaging biomarkers are provided by the European Society of Radiology and the Radiological Society of North America.
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Radiomics is a rapidly emerging field of imaging research delivering an almost limitless supply of potential imaging biomarkers for improved patient and disease characterization.
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The currently available evidence on imaging biomarkers and radiomics is still mostly at the discovery level; rigorous technical, biological and clinical validation are needed for clinical application.
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M.S. declares speaker fees paid to her institution from GE Healthcare and honorarium paid to her institution from Parexel Ltd. for trial review of EORTC-1410.
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Related links
European Imaging Biomarkers Alliance (EIBALL): https://www.myesr.org/research/esr-research-committee
Open Source Initiative for Perfusion Imaging (OSIPI): https://www.osipi.org
Quantitative Imagine Biomarkers Alliance (QIBA): https://www.rsna.org/research/quantitative-imaging-biomarkers-alliance
Quantitative Imaging Biomarkers Alliance (QIBA) wiki: https://qibawiki.rsna.org/index.php/Main_Page
The Cancer Genome Atlas: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga
The Cancer Imaging Archive: https://www.cancerimagingarchive.net
Glossary
- Repeatability
-
The frequency with which the same measurement under the same conditions (for example, same scanner, participant and rater) provides the same result.
- Reproducibility
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The frequency with which the same measurement performed under different conditions (for example, on a different scanner or by a different rater) provides the same result.
- Phantom
-
An artificial construct, either physical or digital, that provides a reference standard for validation and calibration.
- Sensitivity
-
The proportion results from a given test that are true positives.
- Specificity
-
The proportion results from a given test that are true negatives.
- Brownian motion
-
The random motion of particles within a medium.
- Ki-67 labelling index
-
A marker of cellular proliferation based on immunohistochemical assessment of the expression of the Ki-67 protein.
- Mean kurtosis
-
An estimate of the non-gaussianity of water diffusion resulting from the presence of diffusion barriers and compartments within tissue structure; higher mean kurtosis indicates higher tissue microstructural complexity.
- Deep learning
-
A class of machine learning based on artificial neural networks that are inspired by biological networks of learning and information processing; ‘deep’ refers to the use of multiple layers in the network.
- Overfitting
-
A phenomenon that occurs when the match between the classification model and the data set is too perfect, resulting in a model that cannot be generalized to any other data set.
- Dimensionality
-
The number of dimensions included in a computational model; in radiomics, this term relates primarily to the number of imaging features, each feature being one dimension.
- Noise
-
The unexplained variation or randomness in a computational model.
- Radiomics quality score
-
A score system used to assess the quality of radiomics studies; consists of 16 items covering methodology, reporting, clinical utility and contribution to open science.
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Smits, M. MRI biomarkers in neuro-oncology. Nat Rev Neurol 17, 486–500 (2021). https://doi.org/10.1038/s41582-021-00510-y
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DOI: https://doi.org/10.1038/s41582-021-00510-y
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