Neuroimaging plays a central role in the management of brain tumors, where accurate visualization of tumor morphology, functional networks, and surrounding microstructure is crucial for surgical planning and postoperative follow-up. Recent advances in magnetic resonance imaging (MRI), including diffusion, tractography, perfusion, chemical exchange saturation transfer (CEST), multi-nuclear, ultra-high field, and functional imaging, along with positron emission tomography (PET) imaging have significantly improved clinicians’ ability to characterize brain tumors, delineate tumor boundaries, and preserve eloquent brain structures. Despite these advances, major challenges persist in integrating these multimodal datasets into real-time surgical decision-making and in predicting long-term functional and cognitive outcomes. Consequently, the field is witnessing the development of innovative imaging biomarkers, artificial intelligence (AI)–driven models, and intraoperative imaging platforms aiming to improve diagnostic precision and accelerate clinical translation. However, systematic frameworks to validate these technologies and standardize their clinical utility remain limited.

This Research Topic aims to bring together interdisciplinary research exploring the application of advanced neuroimaging techniques for brain tumor surgical planning and outcome assessment. It seeks to understand how multimodal imaging data can improve tumor visualization, resection accuracy, and preservation of brain function. The goal is to facilitate comparative studies, methodological innovations, and translational research evaluating the predictive power of imaging biomarkers for surgical outcomes, neurocognitive preservation, and long-term quality of life. Questions of interest include: How can imaging data be optimized for intraoperative use? What imaging information most greatly influences preoperative planning and intraoperative decisions? What metrics best characterize surgical success beyond resection margins? Can neuroimaging signatures of tumor heterogeneity, intratumoral connectivity, and tumor-brain interactions inform clinical management? Which imaging modalities or combined approaches provide the most robust prognostic insights?

To gather further insights into the clinical and technical boundaries of this field, we welcome articles addressing, but not limited to, the following themes:

• Development and validation of novel neuroimaging biomarkers for tumor delineation and non-invasive diagnosis
• Application of advanced MRI, PET, and hybrid modalities in pre- and intraoperative planning
• AI and machine learning for image segmentation, integration, diagnostic inference, and outcome prediction
• Fusion of structural, functional, and metabolic imaging to guide surgical navigation
• Identifying neuroimaging correlates of tumor heterogeneity, intratumoral connectivity, and tumor-brain interactions that inform clinical management
• Correlation of pre and post-operative imaging findings with neurocognitive, behavioral, and survival outcomes
• Standardization and reproducibility challenges in multimodal neuroimaging studies
• AI and machine learning for image segmentation, integration, diagnostic inference, and outcome prediction
• Clinical trials and translational studies evaluating imaging biomarkers, imaging-guided surgical interventions, and imaging-based endpoints

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Advanced Brain Tumor Imaging for Diagnosis, Surgical Decision Making, and Outcome Assessment