Advances in Magnetic Resonance Imaging of brain tumors allow not only high resolution structural imaging for the pre- operative planning of brain neoplasms, but also provide details of the cellular, architectural, microvascular and metabolic milieu of the tumor that helps in grading and prognostication of these lesions¹. Recent advances in Perfusion Weighted Imaging (PWI), Diffusion and Diffusion Tensor Imaging (DWI and DTI), Susceptibility Weighted Imaging (SWI), Multivoxel MR Spectroscopic Imaging (MRSI) and functional MRI (fMRI) have paved the way for more accurate and efficient pre- operative diagnosis, treatment planning and follow up. Despite the well known limitations of subjectivity, inter-observer variability and errors due to under sampling, the 2007 modification of WHO classification is considered as standard for brain tumor stratification and treatment decisions even now. This histopathological classification is based mainly on tumor cellularity, nuclear atypia, pleomorphism, necrosis and mitosis. However it is highly controversial that the histological grading alone can determine the outcome of therapeutic interventions in various brain tumors. Surrogate imaging signatures derived from advanced physiological neuroimaging might provide pre- surgical information on the genetically determined possible biologic behavioral differences between tumors of the same WHO type and grade. Thus a genomic or transcriptomic sub-classification of these tumors and their imaging correlates (Imaging genomics) becomes all the more important, especially in high grade tumors with generally poor therapeutic outcome. Development of novel targeted anti- cancer medications and personalized therapeutic strategies have already resulted in improvement in prognosis in selected tumor subtypes.

Perfusion Imaging: PWI using Dynamic Susceptibility Contrast (DSC) and Dynamic Contrast Enhanced (DCE) sequences has been extensively studied for pre and post therapy clinical evaluation of glial and non- glial neoplasms of the brain. The relative Cerebral Blood Volume (rCBV) maps derived from DSC imaging will provide a surrogate marker of tumor microvascularity and VEGF expression^{2, 3}. However inherent limitations of the technique and the diverse acquisition and processing methods used, make it difficult to standardize the sequence across different field strengths and vendors. There can be gross under or overestimation of CBV depending on the tumor type, extend of blood brain barrier (BBB) breakdown and the sequence parameters used. However still it is useful in differentiating low grade glial neoplasms (WHO grade 2) from high grade (grade 3 and 4)². One caveat is that highly perfused solid areas can be detected in pilocytic astrocytomas (WHO grade 1), benign oligodendrogliomas and choroid plexus tumors. Thus MR perfusion alone may not be useful for pre- operative tumor grading. Recently Time – Intensity Curve analysis and Percentage of Signal Recovery calculations have been shown to give further insight into the type of tumor vascularity and degree of breakdown of BBB, thus helping in differentiating tumors⁴. Although rCBV might be useful in diagnosing high grade gliomas, a recent report has shown that CBV is not significantly different among molecular subclasses of GBM. Several reports have also shown the usefulness of DSC perfusion in differentiating non- glial tumors like metastasis, primary CNS lymphoma or extra axial lesions like meningiomas. DCE perfusion helps in estimating the endothelial permeability due to BBB disruption by measuring parameters like K^trans. This is also useful for tumor grading. DCE imaging has advantages like better spatial resolution and less susceptibility to artifacts, but has a more complex data set for analysis1. K^trans from DCE and rCBV from DSC imaging remain the main in- vivo parameters used by researchers to measure the capillary permeability and angiogenesis respectively in brain tumors. Further clinical and research uses of PWI include selection of biopsy site representing the most malignant area of the tumor, monitoring efficacy of anti-angiogenic medications, altering the BBB permeability for better local drug delivery and differentiating radio-chemotherapy induced necrosis from tumor recurrence. Arterial Spin Labeling (ASL) perfusion has also been shown to be useful, especially in pediatric population.

Diffusion Imaging and DTI: One of the earliest applications of DWI in brain tumors was to determine the tumor cellularity. Apparent Diffusion Coefficient (ADC) in highly cellular portions of the tumors is found to be low because of the reduced extracellular space available for free water diffusion in such areas. However inferring cellularity based only on ADC measurements can be fallacious. ADC measurements have also been proved to be useful in differentiating gliomas from PCNS lymphomas and their post therapy follow up for recurrence free survival. DTI on the other hand helps in determining the microstructural alterations caused by brain tumors by measuring parameters like Fractional Anisotrpy (FA) and mean diffusivity (MD) in and around the tumor. It is found to be useful in separating vasogenic from infiltrative edema around the tumor which can differentiate solitary metastasis from high grade gliomas^5,6. More advanced metrics like Planar Anisotropy (CP), Linear Anisotropy (CL), Spherical Anisotropy (CS), pure isotropic component of diffusion (p), anisotropic component of diffusion (q) and total magnitude of diffusion tensor (L) help to further characterize the microstructural alterations in and around the tumor helping in making specific tumor diagnosis^7,8. However its role in prediction of prognosis with specific therapy is yet to be proved⁹. DTI also helps in representing eloquent white matter tracts distorted or displaced by the tumor tissue which can be represented in a three dimensional manner helping surgeon to plan the best “angle of attack” for resection of the tumor. Exporting the DTI data to neuronavigational suite or even performing per- operative fiber tractography has been shown to be useful in reducing the post operative morbidity.

Susceptibility Weighted Imaging: SWI can detect vascular structures, blood products and calcification within the tumor matrix. Intratumoral susceptibility signals (ITSS) has been used to differentiate GBM, metastasis and lymphomas^10,11. The ITSS is least prominent in lymphoma and most prominent in GBM.

Multivoxel MR Spectroscopy: MRSI also known as chemical shift imaging (CSI), helps to map the metabolic profile of brain tumors¹². Specific metabolites like as n-acetylaspartate (NAA), creatine (Cr), choline (Cho), lipids and lactate within intra and peri-tumoral regions can be semi- quantitatively mapped and compared with normal appearing contralateral brain regions. However most brain tumors have a non-specific spectrum with increased Cho/Cr and decreased NAA/Cr ratios within the tumor due to high myelin turnover and disruption of neuronal integrity. Normalized choline map is also shown to be useful. However there is still controversy on the real use of spectroscopy in grading tumors. Lipid as a marker of necrosis and lactate as a marker of hypoxia and anaerobic metabolism have been studied especially with the use of lactate edited spectroscopy. Other metabolites studied include GLX and myoinositol. Recently 2-Hydroxyglutarate (2HG) detected on in-vivo MRS has been implicated with IDH1 mutation and MGMT methylation status of glial tumors predicting better outcome after therapy^13,14.

Multimodal MR Imaging: Combined use of advanced MRI protocols augments the conventional MRI in diagnosis of brain tumors^15,16. For example use of rCBV together with ADC measurements increases the efficiency of glioma grading pre- operatively. Similarly combination of DTI metrics and perfusion imaging has also been found to be useful. This synergistic effect of multimodal imaging is only natural as histopathological diagnosis depends on many factors including cellularity and vascularity of the tumor.

Functional MRI: Mapping the eloquent cortex using task based BOLD fMRI to prevent or minimize the post operative functional deficits has been in use for several years. Recently resting state functional MR (rsfMRI) studies have also been used to map the language and motor networks pre- operatively.

Other advances: Amide Proton Transfer Imaging (APT), Intravoxel Incoherent Motion imaging, Sodium imaging, novel methods of BBB manipulation using image guided focused ultrasound for selective drug delivery applications, development of new targeted contrast agents are active areas of research.

With the new interest in ‘omics’, the classification of brain tumors is changing rapidly and the next WHO classification is bound to include them, as tumors of the same WHO class and grade differ drastically in response to therapy and which can be predicted by their genomic sub-classification like IDH1 and 2 mutations and MGMT promoter methylation status in GBMs^17-19. Some of the conventional imaging features like location, type of contrast enhancement, non- enhancing tumor areas and necrosis has been shown to have ‘omic’ correlation and hence predictable treatment response. Similar research is going on to classify many tumor types like Medulloblastoma into transcriptomic sub classes like WNT, SHH, Group 3 and 4 with their imaging correlates to design specific targeted therapy and to predict overall prognosis^20,21.Advanced multimodal MRI has been shown to improve the diagnosis, classification and post therapy changes of brain tumors, despite the limitations. Further research into the ‘omic’ classification and their imaging correlates in these tumors will help in prediction of their treatment response and tumor free survival.