Magnetic Resonance Imaging Applications in Central Nervous System Tumors

Magnetic resonance imaging (MRI) has become integral to the diagnosis, characterisation and management of central nervous system tumours. Conventional T1- and T2-weighted sequences remain the foundation for detecting lesion morphology, enhancement patterns and oedema. Diffusion-weighted imaging and the derived apparent diffusion coefficient (ADC) provide quantitative measures of cellularity that assist in distinguishing high-grade gliomas from lymphomas and metastases. Perfusion methods—dynamic susceptibility contrast, dynamic contrast-enhanced imaging and arterial spin labelling—offer insight into tumour vascularity and permeability, facilitating non-invasive grading and guiding biopsy targets. Advanced techniques such as diffusion tensor imaging yield microstructural detail of white-matter tracts, informing surgical planning. The emergence of radiomic analysis and machine-learning algorithms has further enhanced the capacity to extract high-dimensional features and predict tumour genotype, treatment response and prognosis. Together, these MRI applications form a multi-parametric toolkit that supports personalised management of central nervous system tumours and drives ongoing innovation in neuro-oncology.

Research from Nature Portfolio

One foundational study introduced a radiomics-based strategy combining handcrafted image features with a multilayer perceptron network to differentiate glioblastoma from primary central nervous system lymphoma. By extracting texture, shape and intensity metrics from diffusion and contrast-enhanced images, and training a small-sample classifier, this approach demonstrated robust performance across varying MRI protocols. The neural network exhibited high generalisability in external validation, outperforming end-to-end convolutional models and matching expert radiologists, thereby laying the groundwork for clinical deployment of radiomic classifiers in neuro-oncology.

Magnetic Resonance Imaging Applications in Central Nervous System Tumors publication trend

The graph below shows the total number of articles in magnetic resonance imaging applications in central nervous system tumors across all publications each year (not limited to Nature Index journals).

Technical terms

Apparent Diffusion Coefficient (ADC): Quantitative measure of water diffusion within tissue, inversely related to cellular density.

Arterial Spin Labelling (ASL): Non-contrast perfusion method that labels inflowing blood magnetically to estimate cerebral blood flow.

Diffusion-Weighted Imaging (DWI): MRI technique sensitive to the random motion of water molecules, used to infer tissue microstructure.

Radiomics: High-throughput extraction of quantitative image features for pattern analysis and predictive modelling.

Multilayer Perceptron (MLP): Feedforward artificial neural network composed of interconnected processing layers, used for classification or regression tasks.

References

1. Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling. European Radiology (2023).
2. Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient. Cancer Imaging (2023).
3. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Scientific Reports (2019).