Magnetic Resonance Imaging Techniques for Differentiation of Brain Tumors

Magnetic resonance imaging (MRI) has evolved from conventional T1- and T2-weighted sequences to a suite of advanced techniques that non-invasively probe cellular and vascular properties of intracranial lesions. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) characterise water mobility and microstructural integrity, aiding distinction between highly cellular glioblastomas and peritumoral oedema surrounding metastases. Perfusion methods, notably dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) imaging, quantify blood volume and vascular permeability to highlight neovascular proliferation typical of high-grade gliomas. Arterial spin labelling (ASL) provides non-contrast measures of cerebral blood flow, while spectroscopy and relaxometry furnish metabolic and quantitative tissue parameters. Recent developments in neurite orientation dispersion and density imaging (NODDI) grant insight into microstructural heterogeneity, and radiomics frameworks extract high-dimensional features for machine-learning classification. Integrating these modalities with deep neural networks and quantitative analytics has substantially improved accuracy in separating glioblastoma, brain metastasis and primary central nervous system lymphoma, with promising impact on surgical planning and personalised therapy.

Research from Nature Portfolio

Recent studies have demonstrated the power of deep learning combined with radiomic profiling to distinguish glioblastoma from single brain metastasis. A deep neural network was trained on hundreds of radiomic features derived from contrast-enhanced and peritumoral regions, achieving an area under the curve exceeding 0.95 in external validation cohorts. This approach outperformed traditional machine-learning classifiers and expert readers, underscoring the generalisability of convolutional architectures for automated differential diagnosis. The work highlights the added value of peritumoral feature extraction and supports integration of radiomics-driven networks into routine MRI workflows.

Magnetic Resonance Imaging Techniques for Differentiation of Brain Tumors publication trend

The graph below shows the total number of articles in magnetic resonance imaging techniques for differentiation of brain tumors across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic susceptibility contrast (DSC) perfusion imaging: A technique that measures cerebral blood volume and flow by tracking signal changes following the injection of a gadolinium-based contrast agent.

Diffusion-weighted imaging (DWI): An MRI sequence sensitive to the random motion of water molecules, providing metrics related to tissue cellularity.

Neurite orientation dispersion and density imaging (NODDI): An advanced diffusion MRI method that quantifies neurite microstructure by modelling orientation dispersion and density of neuronal processes.

Radiomics: The extraction and analysis of quantitative features from medical images to characterise tumour phenotype.

Convolutional neural network (CNN): A class of deep learning algorithm designed to process grid-like data, such as images, through successive layers of convolutional filters.

Peritumoral oedema: The region of increased fluid accumulation surrounding a tumour, visible as hyperintensity on T2-weighted or FLAIR images.

References

1. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Reports Medicine (2024).
2. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Scientific Reports (2020).
3. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. European Radiology (2024).
4. Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor. Cancers (2023).