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@article{203279,
        author = {Dr. Gurrala Harsha Chandra and Dr. Nabin Kumar Yadav and Dr. Amit Kumar Sah and Dr. Surendra Prasad Saha and Dr. Mohammad Ahamadullah and Dr. Aqib Naeem},
        title = {Advanced MRI Techniques in Brain Tumor Diagnosis and Neurological Oncology},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10384-10394},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203279},
        abstract = {Advanced MRI modalities—such as diffusion imaging (DWI, DTI, DKI), perfusion imaging (DSC, DCE, ASL), MR spectroscopy (MRS), functional MRI (fMRI), susceptibility-weighted imaging (SWI), quantitative relaxometry (T1/T2 mapping), and quantitative radiomics/AI—are increasingly indispensable in neuro-oncology. They provide metabolic, vascular, and cellular information that complements conventional MRI, enabling more accurate tumor grading, differentiation of tumor recurrence vs. treatment effects, prediction of molecular markers (IDH, 1p/19q, MGMT), and monitoring of therapy. Recent studies and consensus guidelines highlight the added value of these methods: for example, perfusion MRI (DSC/DCE) shows high sensitivity/specificity (~90%/85%) in distinguishing tumor recurrence from radiation necrosis, and combined MRS and perfusion yields ~92% specificity for differentiating neoplasms from non-neoplastic lesions. Radiomics and machine learning leveraging multimodal MRI (and PET/MRI) can predict key genotypes with accuracies around 80–90%. We synthesize the recent literature (past decade) and consensus guidelines to present an in-depth review of each modality, including technical principles, typical acquisition parameters, diagnostic performance, applications to grading and molecular subtyping, roles in posttreatment monitoring, limitations, and future directions. We also provide comparative tables of protocols and performance metrics, and a flowchart for multimodal imaging decision-making (Figure 1). Our findings support an integrated imaging approach: for instance, international recommendations advise routine use of perfusion MRI for gliomas lacking clear high-grade features. In conclusion, advanced MRI techniques significantly enhance neuro-oncologic imaging and support personalized management, but standardized protocols and further validation are needed to fully realize their potential in clinical practice.},
        keywords = {Brain tumors, advanced MRI, diffusion imaging, perfusion MRI, MR spectroscopy, functional MRI, SWI, relaxometry, radiomics, PET/MRI, tumor grading, molecular markers, neuro-oncology.},
        month = {May},
        }