Short Communication

Magnetic Resonance Imaging (MRI) has become the cornerstone of brain tumor diagnosis, characterization and management, providing unparalleled soft-tissue contrast and detailed visualization of intracranial structures. Brain tumors encompass a wide spectrum of neoplasms, ranging from benign lesions to highly aggressive malignancies, each with distinct imaging characteristics and clinical implications. An atlas that combines MRI features with clinical correlates offers an invaluable resource for clinicians, radiologists and trainees, allowing for the integration of anatomical, pathological and functional insights to guide diagnosis and therapeutic decision-making.

MRI provides a multi-dimensional assessment of brain tumors, capturing their size, location, morphology and tissue composition. Conventional sequences such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images reveal tumor boundaries, edema, cystic components and mass effect, which are crucial for preoperative planning and prognostication. Contrast-enhanced T1-weighted imaging further delineates regions of blood-brain barrier disruption, highlighting areas of active tumor and potential malignancy. Each tumor type exhibits characteristic MRI patterns; for instance, meningiomas typically appear as well-circumscribed, extra-axial masses with homogeneous enhancement, whereas glioblastomas often present as irregular, infiltrative lesions with central necrosis and peripheral contrast enhancement. Recognizing these patterns is essential for accurate tumor classification and guiding biopsy or surgical intervention.

Incorporating MRI features and clinical correlates into a structured atlas serves as a powerful educational and diagnostic tool. It enhances pattern recognition, supports differential diagnosis and fosters decision-making skills in complex cases. By presenting a wide range of tumor types with representative imaging and associated clinical scenarios, such an atlas empowers clinicians to bridge the gap between visual information and patient-centered care. Moreover, digital and interactive atlases allow for three-dimensional visualization, annotation and integration with emerging technologies such as artificial intelligence, further refining diagnostic precision and predictive modeling.

In conclusion, a brain tumor imaging atlas that combines MRI features with clinical correlates is an indispensable resource in modern neuro-oncology. By integrating anatomical, functional and pathological insights, it provides a comprehensive framework for accurate diagnosis, treatment planning and outcome prediction. MRI remains the cornerstone of this approach, offering detailed visualization and functional assessment that guide both clinical and surgical decision-making. As imaging technologies continue to evolve, such atlases will play an increasingly central role in enhancing diagnostic accuracy, improving patient outcomes and training the next generation of clinicians.

Keywords

Brain Tumor MRI, Neuroimaging, Clinical Correlates

Acknowledgement

None.

Conflict of Interest

None.

References

1. Ozyurt F, Sert E and Avci D (2020). An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med Hypotheses 134: 109433.

Google Scholar Cross Ref Indexed at

2. Cinar A and Yildirim M (2020). Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses139: 109684.

Google Scholar Cross Ref Indexed at