Imaging in Neuro-oncology

Abstract

Brain tumors are a diverse group of neoplasms that vary widely in treatment and prognosis. Imaging serves as the cornerstone of diagnosis, monitoring response to treatment and identifying progression of disease in neuro-oncologic care. This review outlines current and emerging imaging modalities with a focus on clinical application in glioma, meningioma, and brain metastasis. We cover standard imaging modalities, advanced magnetic resonance techniques such as perfusion and spectroscopic imaging, and nuclear imaging with positron emission tomography (PET), including amino acid PET. We summarize the standardized Response Assessment in Neuro-Oncology (RANO) criteria, and explore innovations in radiomics, artificial intelligence, and targeted imaging biomarkers. Finally, we address challenges related to equitable access to advanced imaging. This review provides a practical, clinically focused guide to support neurologists in the imaging-based care of patients with primary or metastatic brain tumors.

Keywords

neuro-oncology - brain tumor - advanced MRI - amino acid PET - radiomics

Introduction

Central nervous system (CNS) tumors represent a diverse group of primary and secondary neoplasms that vary widely in clinical behavior, prognosis, and treatment strategies. Primary brain and spine tumors arise from cells within the CNS and include gliomas (astrocytomas, oligodendrogliomas, glioblastomas), meningiomas, pituitary adenomas, primary central nervous system lymphomas (PCNSL), and rarer histologies such as medulloblastomas and ependymomas. Glioblastoma, which originates from glial cells, is the most common primary malignant brain tumor in adults, with over 13,000 new cases diagnosed per year.[1] Meningiomas are the most common primary brain tumors, with approximately 40,000 diagnosed per year.[1]

Secondary brain tumors, or brain metastases, are far more common than primary CNS tumors and occur in up to 20 to 40% of patients with systemic cancer, particularly lung, breast, melanoma, renal cell carcinoma, and colorectal cancer.[2] Improved CNS surveillance and imaging, along with more efficacious systemic therapies, are leading to a growing number of patients with brain metastases.[3]

Neuroimaging is one of the most frequently used tools in neuro-oncology and often serves as the first step in diagnosing or identifying CNS tumors. Clinicians use imaging not only to confirm the presence of a tumor but also to guide surgical and radiation planning. Throughout treatment and follow-up, imaging helps monitor tumor response, detect recurrence, and identify treatment-related effects. After therapy, neurologists rely on imaging to assess disease progression and make informed adjustments to the care plan.

However, interpreting imaging findings in neuro-oncology is often complex. Treatment-related phenomena such as pseudoprogression, transient increase in lesion enhancement due to inflammation after chemoradiation, and pseudoresponse, rapid reduction in enhancement and edema following anti-angiogenic therapy, can mimic true disease progression or response. These effects complicate radiographic assessments and highlight the importance of clinical context and the integration of advanced imaging techniques. In response to these challenges, neuroimaging has advanced significantly, with new modalities offering deeper insight into tumor biology, treatment response, and prognosis.[4] The growing complexity of imaging technologies and interpretation frameworks underscores the need for a practical understanding of how to apply these tools in neurologic care.

This review outlines both current and emerging imaging modalities in neuro-oncology and demonstrates how neurologists can apply these tools in clinical practice. It covers core techniques, advanced magnetic resonance imaging (MRI) sequences, and nuclear imaging, including positron emission tomography (PET). We summarize standardized response criteria such as the Response Assessment in Neuro-Oncology (RANO) and explore innovations in radiomics, artificial intelligence, and targeted imaging biomarkers. Finally, we address challenges related to equitable access to advanced imaging, an increasingly important issue as precision medicine continues to evolve. This review provides a practical, clinically focused guide to support neurologists in the imaging-based care of patients with primary or metastatic brain tumors.

Core Imaging Modalities

Computed Tomography (CT)

Computed tomography (CT) serves as a rapid, accessible imaging modality, often used in the acute setting when MRI is not always feasible or practical. Clinicians commonly utilize CT at initial presentation, in response to acute changes in the clinical picture and postoperative surveillance. At initial presentation, CT is used to rule out ischemic stroke, hemorrhage, and CNS infection. Features of intra-axial tumors are widely variable. Many demonstrate hypoattenuation or isoattenuation, with CNS lymphoma and meningiomas being the most common hyperattenuated masses. Tumors tend to spare the cortex and demonstrate mass effect and edema, which may look more severe than presenting symptoms would suggest. Clinicians rely on CT to triage patients with midline shift, ventricular entrapment, and concern for increased intracranial pressure. Bony involvement is rare but can be seen in some invasive tumor types. Unexplained calcifications may also raise suspicion for primary brain tumors. CT is also used for operative planning, often with fiducial markers to support image-guided neuronavigation, and intraoperative CT can provide real-time updates to guide resection and confirm surgical accuracy.[5] [6] In any case where tumor is suspected, clinicians should pursue MRI of the brain with and without contrast.

Magnetic Resonance

Core Sequences

Magnetic resonance represents the mainstay of radiologic assessment for both primary and secondary brain tumors. Although clinical symptoms play an important role, radiologists and clinicians rely on MRI to assess both progression of disease and treatment response. A consensus for Brain Tumor Imaging Protocol (BTIP) was developed in 2015[7] and while geared toward clinical trial standardization, it has been widely adopted at most brain tumor centers. This includes axial T2-weighted sequences, axial fluid-attenuated inversion recovery (FLAIR), axial diffusion-weighted imaging (DWI), and 3D T1-weighted pre- and post-contrast images using either a 1.5T or 3T strength magnet.

Contrast-enhanced MRI remains the most sensitive and reproducible imaging modality to assess brain tumors, establishing it as the standard imaging approach[8] ([Fig. 1]). Due to the characteristic angiogenesis associated with more aggressive tumors, pre- and post-contrasted T1-weighted images are critical in any brain tumor evaluation. 3D isotropic imaging allows the detection of smaller lesions as well as more accurate comparison between scans to detect changes in lesion size by limiting differences in slice prescriptions. An added advantage is the ability to reconstruct images in different planes which are often needed for neurosurgical or radiation planning, without the need for reacquisition. Although not yet standardized, multiple studies[9] [10] [11] have demonstrated the benefit of using volumetric assessments over conventional cross-sectional areas for tumor assessment, particularly in nonenhancing tumors.[12] Volumetrics can decrease inter-use variability in size assessments and help detect early signals of progression when conventional radiographic response criteria are not yet met.[13]

Fig. 1 Axial T1-weighted pre-contrast image (A) and post-contrast image (B) of a glioblastoma. The pre-contrast image shows a mix of hypointense T1 signal in the right hemisphere. In post-contrast image, there is a heterogeneously enhancing tumor with irregular signal characteristics, consistent with necrosis, angiogenesis, and intratumoral blood products.

T2-weighted images are crucial for monitoring nonenhancing tumors such as low-grade gliomas, as well as early indicators of progression in enhancing tumors and those treated with anti-angiogenic agents such as bevacizumab.[14] T2-weighted FLAIR MRI combines T1- and T2-weighting to suppress CSF signal, allowing improved visualization of periventricular and juxtacortical tumor, vasogenic edema, and gliosis ([Fig. 2]). FLAIR sequences are used to determine progression in the Response Assessment in Neuro-Oncology (RANO) criteria, which is detailed in a later section.

Fig. 2 Axial T2-weighted image (A) and T2-weighted fluid-attenuated inversion recovery (FLAIR) image (B) of the same glioblastoma shown in Fig. 1. Note the peritumoral hyperintense T2 signal, indicative of edema. On the T2-weighted image, cerebrospinal fluid (CSF) appears hyperintense, with signal intensity similar to the necrotic, cystic regions within the tumor. In contrast, the FLAIR sequence suppresses the CSF signal, enhancing visualization of the tumor relative to normal brain parenchyma.

DWI is the last component of standard tumor imaging protocols ([Fig. 3]). Its sensitivity to microscopic water motion allows visualization of infection as well as ischemic injury. Because DWI is acquired with T2-weighting, high T2 signal can appear bright on DWI (“T2 shine through”); thus, an apparent diffusion coefficient (ADC) map is often generated by comparing DWI of two distinct b-values. ADC values have been inversely correlated with cellular density, which aids in distinguishing between grades of glioma and highly cellular tumors such as lymphoma from primary gliomas.[15] DWI and ADC have been investigated in predicting overall survival[16] and distinguishing tumor progression from treatment effect[17] [18] [19]; however, the presence of vasogenic edema and necrosis causes substantial variation in these measurements, limiting their widespread use.

Fig. 3 Axial T2-weighted diffusion-weighted imaging (DWI) of the glioblastoma shown in Figs. 1 and 2. The hyperintense signal within the tumor indicates areas of hypercellularity and restricted water diffusion.

Advanced MRI

Perfusion

Three primary methods for measuring hemodynamic perfusion have been studied in neuro-oncology: dynamic susceptibility contrast MRI (DSC-MRI), dynamic contrast-enhanced MRI (DCE-MRI), and arterial spin labeling (ASL). DSC measures the drop in T2 signal intensity over time as a function of local contrast concentration over time, generating dynamic measurements such as relative cerebral blood volume (rCBV). DSC is the most commonly used perfusion imaging and is widely incorporated into research and clinical studies. It is particularly useful in distinguishing CNS lymphoma from primary glioma, where the former demonstrates lower rCBV,[20] and solitary brain metastases from infiltrative glioma where the peritumoral region often shows increased rCBV.[21]

DCE augments the T1–post-contrast imaging by serial T1 acquisitions, providing estimation of contrast kinetics. The most widely used parameters are k^trans or the efflux rate of contrast from the plasma into the extravascular extracellular space, serving as an estimate of blood–brain barrier permeability, and fractional plasma volume (Vp), which serves as a physiologic equivalent to rCBV ([Fig. 4]). ASL is achieved by applying radiofrequency pulses to arterial blood, which invert the longitudinal magnetization of protons, effectively turning upstream arterial blood into a tracer without requiring contrast administration. Kinetic modeling enables estimation of cerebral blood flow (CBF). Similar to DSC-MRI, both DCE and ASL can help distinguish high-grade glioma from PCNSL[22] and low-grade glioma,[23] [24] and both have been investigated as tools to distinguish tumor recurrence from treatment effect,[25] [26] with DSC performing better than ASL and DCE in at least one study.[27] Both DSC and ASL require complex kinetic modeling, but ASL overcomes the limitations of susceptibility artifacts and eliminates the need for contrast administration required by the other techniques. Although nearly reaching routine use in brain tumor imaging, variability in perfusion acquisition sequences, contrast timing, and pre- and post-processing limits the generalizability of perfusion imaging data across institutions.[28]

Fig. 4 Dynamic contrast-enhanced MRI (DCE-MR), with K^trans map (A) and Vp map overlaid on the T1–post-contrast image (B). Bright yellow regions represent areas of hyperperfusion, including normal vasculature and regions within the tumor, reflecting neovascularization and angiogenesis.

Susceptibility-weighted Imaging (SWI)

Susceptibility-weighted imaging (SWI) is a technique which takes advantage of changes in the proton precession frequency revealed by T2* weighted phase signals, leading to phase differences between paramagnetic deoxygenated blood products and nearby brain parenchyma. It is particularly useful in detecting microhemorrhages, calcifications, and venous blood.[29] Increased intratumoral susceptibility signals (ITSS) have been associated with higher tumor grade,[30] as well as the ability to differentiate metastatic melanoma from breast or lung metastases[31] ([Fig. 5]).

Fig. 5 Susceptibility-weighted imaging (SWI) of a glioblastoma. The blooming artifacts within the tumor reflect the presence of intratumoral blood products, consistent with hemosiderin deposition.

Delayed Contrast

Delayed contrast imaging refers to MRI sequences acquired several minutes after contrast administration, rather than immediately following injection. Timing can vary depending on the clinical context, but typically involves rescanning 5 to 15 minutes,[32] or up to 75 minutes,[33] after the initial post-contrast images. This approach can improve the detection of tumor infiltration and subtle enhancement by allowing additional time for contrast accumulation in regions with impaired blood–brain barrier integrity. It is used to help distinguish tumor progression from treatment-related effects such as radiation necrosis, as active tumor will usually have persistent enhancement due to active neovascularization and ongoing disruption of the blood–brain barrier[32] ([Fig. 6]).

Fig. 6 Axial T1-weighted post-contrast image of a cerebellar enhancing lesion (A) and delayed contrast-enhanced image (B). The central region of the lesion shows “blue clearance,” indicative of delayed contrast washout and suggestive of viable tumor, which was confirmed on subsequent biopsy.

Diffusion Tensor

Beyond standard DWI, diffusion tensor imaging (DTI) is a technique that acquires diffusion-weighted data in at least six directions, allowing assessment of both the magnitude and direction of water diffusivity through derivation of fractional anisotropy (FA). FA ranges from 0 to 1, with 0 indicating equal diffusion in all directions and 1 indicating diffusion in a single direction. The primary application of DTI in brain tumors has been to improve the detection of tumor extent for infiltrative gliomas by combining mean diffusivity with FA in peritumoral regions.[34] [35] Incorporating DTI into presurgical planning can also enhance identification of eloquent white matter tracts, aiding in functional sparing and reducing the risk of detrimental neurological deficits.[36]

Functional MRI (fMRI)

Functional MRI (fMRI) uses changes in blood flow as a surrogate for localized neuronal activity. The magnetic susceptibility of hemoglobin depends on its oxygenation state. While oxygenated hemoglobin is diamagnetic, deoxygenated hemoglobin is paramagnetic and exhibits a shortened T2 relaxation time. This difference generates imaging contrast termed the blood oxygen level dependent (BOLD) effect.[37] When neuronal circuits are activated, the increased demand for oxygenated blood flow leads to a relative decrease in deoxygenated blood concentration, which in turn increases the BOLD signal. Presurgical task-based fMRI is commonly used in tumor resection planning to map eloquent areas of language, visual, and motor cortex ([Fig. 7]). When combined with traditional intraoperative direct stimulation and somatosensory evoked potentials, fMRI provides a reliable method to minimize neurologic deficit after surgery.[37] [38]

Fig. 7 Axial (A) and sagittal (B) T2/FLAIR with fMRI overlay. Language task activation identified the T2/FLAIR hyperintense tumor at the inferior lateral-most aspect of the precentral (motor) and postcentral (sensory) gyri. The patient was determined to be left language dominant with tumor abutting Broca's area.

Spectroscopy

Metabolic imaging is a growing field that encompasses both MR spectroscopic techniques as well as many PET tracers. Quantifying tumor metabolism, either in steady state or in response to a supply of substrate, provides biological insight far beyond anatomic imaging. Proton MR spectroscopy (¹H MRS) leverages the differential chemical shift of protons, determined by their chemical environment when subjected to a magnetic field and radiofrequency pulses. This generates a nuclear magnetic resonance (NMR) spectrum that can be spatially resolved, providing metabolic information localized to regions of interest.[39]

Single-voxel MRS acquires concentrations of metabolites in one specified area, whereas MRS Imaging (MRSI) acquires maps from multiple voxels simultaneously, generating a spatially resolved metabolite map at the expense of sensitivity and acquisition time. The most commonly analyzed metabolites in neuro-oncology are choline (Cho), lactate (lac), N-acetyl aspartate (NAA), glutamine-glutamate (Glx), creatine (Cr), lipids, and more recently, the oncometabolite 2-hydroxyglutarate (2-HG). Clinically, ¹H MRS is most often employed to differentiate between neoplastic and non-neoplastic lesions seen on MRI, particularly when surgery is not feasible or poses higher risk than standard resection or biopsy. Increased Cho and decreased NAA levels are associated with brain tumors,[40] along with a decrease in Cr[41] and increase in Glx.[42] Cho/Cr and Cho/NAA ratios have been used to predict tumor grading[43] and monitor for tumor recurrence.[44] The Lac/Glx ratio captures the characteristic metabolic reprogramming termed the Warburg Effect—the shift to anaerobic glycolysis that increases lactate production and decreases Glx generation—and has been shown to differentiate pseudoprogression from tumor recurrence.[45] Importantly, the detection of the oncometabolite 2-HG has emerged as a highly specific marker for isocitrate dehydrogenase (IDH)-mutant gliomas ([Fig. 8]). The IDH mutation is a genetic mutation that holds significant prognostic and treatment implications in the world of primary brain tumors.[46]

Fig. 8 Representative single voxel ¹H MRS (x-axis: ppm) in nonenhancing brainstem tumor (A) and normal brain (B) demonstrating increased choline, decreased NAA, elevation of Cho/Cr (2.6; typical normal range, 0.7–1.1), elevated Cho/NAA (2.9; typical normal range 0.3–0.6), and positive 2-HG peak detected at 2.25 ppm in the tumor, supporting diagnosis of IDH-mutant low-grade glioma. 2-HG, 2-hydroxyglutarate; Cho, choline; Cr: creatine; Glu, glutamate; IDH, isocitrate dehydrogenase; MRS, magnetic resonance spectroscopy; NAA, N-acetyl aspartate; ppm, parts per million.

The interpretation of ¹H MRS can be limited by intratumor heterogeneity, overlap of metabolite shifts, and lack of specificity between histologies. For example, radiation necrosis and tumor progression may exhibit many overlapping metabolite concentrations, and without pre-treatment spectroscopy for comparison, the interpretation is even further limited. For these reasons, ¹H MRS is currently used as a complementary tool rather than a standalone diagnostic method.

Hyperpolarized MRI is an emerging technique that uses dynamic nuclear polarization to augment the signal-to-noise ratio by >10,000 fold, which, when combined with isotope labeling, enables the real-time and dynamic quantification of metabolism via MRS.[47] Using deuterium as a solvent extends the lifespan of hyperpolarized probes, enabling more clinical applications.[48] The injection of hyperpolarized [1-¹³C] pyruvate has demonstrated increased lactate production in brain tumor patients.[49] Ongoing studies are exploring additional labeled substrates for future clinical and research applications.

Positron Emission Tomography (PET)

FDG-PET

Positron emission tomography (PET) is another form of functional metabolic imaging that provides biological insight beyond what is discernable by anatomic MRI. PET takes advantage of the energy emitted from the collision of positrons emitted by decaying radiolabeled isotopes and nearby electrons. The resulting photons are absorbed by the scintillation crystals in PET cameras, producing light that is converted into an electrical signal.[50]

¹⁸F-2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) is a glucose analog and the most widely used PET tracer in clinical nuclear medicine, with numerous applications in oncology. Many cancers are characterized by an increase in anaerobic glycolysis (Warburg effect), driven by overexpression of glucose transporters.[51] As a result, FDG is preferentially taken up by malignant cells. Upon transport into the cell, ¹⁸F-FDG is phosphorylated and, in the absence of high glucose-6-phosphatase activity, becomes effectively trapped, unable to undergo further metabolism. This leads to accumulation in malignant and other metabolically active cells that upregulate glycolysis.

Historically, ¹⁸F-FDG-PET has been used in neuro-oncology for distinguishing high-grade gliomas from other malignant brain tumors, delineating tumor “hot spots” to guide biopsy,[52] differentiating between radiation necrosis and active tumor,[53] and serial imaging to assess treatment response[54] ([Fig. 9]). In glioblastoma, increased ¹⁸F-FDG-PET uptake has been shown to correlate with decreased survival in both the newly diagnosed[55] [56] and post-treatment setting.[57] [58] In CNS lymphoma, a systematic review estimated the pooled diagnostic sensitivity of ¹⁸F-FDG-PET at 0.88 (95% CI: 0.80–0.94) and specificity at 0.86 (95% CI: 0.73–0.94).[59] Furthermore, multiple studies have established prognostic value from pre-treatment ¹⁸F-FDG-PET scans in this population.[60] [61] Additionally, ¹⁸F-FDG-PET can help distinguish glioblastoma from CNS lymphoma,[52] [62] increasing the diagnostic value in this population.

Fig. 9 Axial T1-weighted post-contrast image of a left occipital lesion (A) that had previously received radiation. FDG-PET MR showed a focal area of hypermetabolic activity (B, C). Subsequent resection confirmed active tumor amid radiation necrosis.

Despite these informative uses, there are many limitations of ¹⁸F-FDG-PET in the management of brain tumors. Although ¹⁸F-FDG-PETcan be used to assess brain metastasis, it does not perform as well as contrast-enhanced MRI. Studies evaluating its role in differentiating radiation treatment effects from viable tumor in brain metastasis are limited by their small sample sizes, heterogeneous histologies, and variable SUV cutoffs, leading to pooled sensitivity and specificity estimates of 40 to 95% and 50 to 100%, respectively.[63] High background uptake in the cortex and basal ganglia severely limits the detection of hypermetabolic lesions in these areas. Additionally, although ¹⁸F-FDG-PET is generally considered to be a sensitive measure of hypermetabolism, it does not achieve the specificity needed to distinguish high-grade gliomas from secondary metastatic tumors, nor can it differentiate brain abscesses, fungal lesions, granulomatous disease, or tumefactive demyelinating lesions.[64] For these reasons, PET applications in the management of primary and secondary brain tumors are shifting to alternative probes, such as amino acid tracers. A 2019 consensus summarized by the Response Assessment in Neuro-Oncology(RANO)/PET PET RANO working group concluded that in 2019, the utility of ¹⁸F-FDG-PET in brain metastasis is outperformed by amino acid PET tracers for every indication.[63]

Amino Acid PET

Multiple primary research studies and consensus guidelines have outlined the superiority of amino acid PET tracers in the management of patients with primary and secondary brain tumors.[4] [63] [65] [66] The most used amino acid PET tracers are O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET), ¹¹C-methyl-l-methionine (¹¹C-MET), and 3,4-dihydroxy-6-¹⁸F-fluoro-l-phenylalanine (¹⁸F-FDOPA). Two subtypes of L-type large neutral amino acid transporters (LAT1 and LAT2) are overexpressed in gliomas and brain metastasis, facilitating uptake of these tracers.[67] [68] Anti-1-amino-3-¹⁸F-fluorocyclobutane-1-carboxylic acid (¹⁸F-fluciclovine) is a synthetic amino acid analog that has gained interest in glioma and is predominantly transported by the neutral alanine, serine, and cysteine transporter 2 in addition to LAT1.[69] The relatively low physiologic uptake of amino acid tracers in normal brain tissue enables high contrast between tumor and surrounding unaffected tissue and offers a distinct advantage over ¹⁸F-FDG. Although extensively used in Europe, limited availability remains the biggest barrier to widespread use of amino acid PET tracers in the United States.

Amino acid PET, most frequently using ¹⁸F-FET, has been evaluated in many of the same applications as ¹⁸F-FDG. A meta-analysis of 462 patients demonstrated superior diagnostic accuracy of ¹⁸F-FET over ¹⁸F-FDG for identifying primary brain tumors,[70] although some reports have noted positive uptake in brain abscesses and demyelinating lesions.[71] [72] While differentiating between higher and lower grade gliomas remains a challenge in both amino acid and glucose analogs, ¹¹C-MET, ¹⁸F-FET, and ¹⁸F-FDOPA PET all outperform standard MRI in delineating tumor extent in both contrast-enhancing and nonenhancing tumors.[73] [74] [75] [76] [77] [78] [79] [80] This information can aid surgical planning, particularly for selecting biopsy sites within heterogeneously enhancing lesions or when tumor is abutting eloquent functional brain areas.[81] [82] [83]

Although studies have incorporated amino acid PETs into radiation planning workflows, they have not yet demonstrated a survival benefit.[84] However, the prognostic value of amino acid PET has been demonstrated in many studies of all grades of glioma using both static and dynamic-derived parameters.[85] [86] [87] [88] In grade 3 and 4 tumors, multiple studies have shown that a reduction in amino acid uptake or decrease in tumor volume measured by amino acid PET indicates treatment response and correlates with improved outcomes.[89] [90] [91] [92] [93] Additionally, amino acid PET has outperformed MRI in assessing treatment response to antiangiogenic therapy with bevacizumab.[94] [95] [96] [97]

Importantly, amino acid PET demonstrates utility in differentiating treatment effects from progressive disease in both glioma and brain metastasis.[79] [98] [99] [100] In the latter, the limited available data show high diagnostic accuracy of amino acid over ¹⁸F-FDG-PET in diagnosis, treatment response, and differentiating immunotherapy-induced changes.[63]

Somatostatin Receptor Analog PET

There are several ⁶⁸Ga-labeled DOTA chelator-conjugated somatostatin analogs that bind with high affinity to somatostatin receptors, including [⁶⁸Ga]Ga-DOTA-D-Phe1-Tyr3-octreotide ([⁶⁸Ga]Ga-DOTATOC), [⁶⁸Ga]Ga-DOTA-D-Phe1-Tyr3-octreotate ([⁶⁸Ga]Ga-DOTATATE), and [⁶⁸Ga]Ga-DOTA-D-Phe1-Nal3-octreotide ([⁶⁸Ga]Ga-DOTANOC).[101] Although [⁶⁸Ga]Ga-DOTA-SSTR PET was initially developed for imaging pituitary adenomas, the overexpression of somatostatin subtype receptor 2 (SSTR2) on meningiomas has enabled its application in these tumor types as well[102] ([Fig. 10]).

Fig. 10 Axial T1-weighted post-contrast image of a left posterior fossa extra-axial dural-based lesion (A). [⁶⁸Ga]Ga DOTATATE PET shows intense tracer avidity of the lesion (B, C), supporting the diagnosis of meningioma.

Given the high specificity of the SSTR2 receptor to these tumors when compared with normal brain, [⁶⁸Ga]Ga-DOTA-SSTR PET has demonstrated high diagnostic accuracy in identifying meningioma,[103] [104] evaluation of infracranial/transosseous extent of tumor,[103] and differentiating from dural-based brain metastasis.[105] [106] The use of [⁶⁸Ga]Ga-DOTA-SSTR PET for contouring tumor volume to guide radiation therapy planning has also shown promise in both meningioma[107] [108] and in pituitary carcinoma invading the cavernous sinus.[109] The combination of ¹⁸F-FDG with [⁶⁸Ga]Ga-DOTA has been shown to identify residual pituitary adenoma after resection,[110] and the detection of both primary and secondary pituitary carcinomas is highly accurate.[111] [112] However, due to the limited number of available research studies, SSTR analogs remain an adjunct tool in the management of meningioma and pituitary adenomas or carcinomas.

Clinical Decision Frameworks

Standardized imaging criteria are essential for evaluating tumor response, guiding clinical decisions, and supporting enrollment and assessment in clinical trials. One of the most widely used frameworks in neuro-oncology is the Response Assessment in Neuro-Oncology (RANO) criteria, designed to improve consistency in evaluating patients with brain tumors, especially in clinical trial settings.[113]

The original RANO criteria, introduced in 2010, were developed to address limitations of the earlier Macdonald criteria, which relied solely on changes in contrast-enhancing tumor on T1-weighted MRI.[114] [115] RANO criteria incorporate nonenhancing disease on T2/FLAIR, neurologic status, and corticosteroid use, offering a more comprehensive and clinically relevant assessment ([Table 1]). These criteria apply most commonly to high-grade gliomas but have also been adapted for other tumor types. RANO-BM (Response Assessment in Neuro-Oncology for Brain Metastases) offers guidance on assessing intracranial metastatic disease, accounting for the number, size, and location of lesions, as well as the presence of extracranial disease.[116] RANO-BM allows for the selection and longitudinal measurement of up to five individual CNS target lesions, enabling lesion-specific tracking. This approach is necessary in patients with multiple brain metastases, where responses may vary across lesions. RANO-LM, designed for leptomeningeal disease, emphasizes radiographic features such as linear versus nodular enhancement on MRI, in combination with neurologic symptoms and cerebrospinal fluid cytology, to assess progression and response.[117]

Table 1 Response Assessment in Neuro-Oncology (RANO) 2.0 criteria for enhancing tumors

	Imaging features	Clinical features
Complete response	● Disappearance of all enhancing disease (measurable and non-measurable) ● Sustained for at least 4 weeks ● No new lesions	● No corticosteroids (physiological replacement doses allowed) ● Clinically stable or improved
Partial response	● ≥65% decrease in volumetric measurement of enhancing tumor compared with baseline or ≥50% decrease in the product of perpendicular diameters ● Sustained for at least 4 weeks ● No progression of non-measurable disease ● No new lesions	● Stable or reduced corticosteroids (compared with baseline) ● Clinically stable or improved
Stable disease	● Does not qualify for complete response, partial response, or progression ● Stable area(s) of enhancing target lesions on imaging ● No new lesions ● No progression of non-measurable disease or nontarget lesions	● Stable or reduced corticosteroids (compared with baseline) ● Clinically stable
Progression	● ≥40% increase in volumetric measurement of enhancing tumor or a ≥25% increase in the product of perpendicular diameters, on stable or increasing steroid dose ● Clear progression of nonmeasurable lesions ● Any new lesion measuring ≥10 mm × 10 mm in perpendicular diameters ● Appearance of definite leptomeningeal disease ● Unequivocal progression of existing nontarget lesions	● Clinical deterioration (not attributable to other non-tumor causes and not due to steroid decrease) ● Failure to return for evaluation because of death or deteriorating condition should also be considered as progression unless caused by documented nonrelated disorders

Note: This framework updates prior RANO guidelines by incorporating volumetric measurements, emphasizing confirmation of progression, and refining criteria for T2/FLAIR progression to improve diagnostic consistency in clinical trials and routine practice.[140]

For patients receiving immunotherapy, iRANO (Immunotherapy Response Assessment in Neuro-Oncology) was developed to address the phenomenon of pseudoprogression, where immune infiltration can transiently enlarge lesions or cause new enhancement.[118] iRANO recommends deferring a formal declaration of progression within the first 6 months of immunotherapy unless there is unequivocal clinical decline, helping prevent the premature discontinuation of potentially effective treatment.

In spinal cord tumors, no formal RANO criteria exist, but imaging assessments follow similar principles, using contrast-enhanced and T2-weighted spinal MRI to evaluate cord expansion, enhancement patterns, and signal changes over time. For meningiomas, volumetric assessment on MRI and serial evaluation of contrast enhancement remain standard, and ⁶⁸Ga-DOTATATE PET is occasionally used.[119] Although typically slow-growing, meningiomas can recur or progress unpredictably, particularly in higher grade subtypes or after subtotal resection.

The PET Response Assessment in Neuro-Oncology (PET-RANO) group has recently proposed consensus guidelines to integrate PET imaging into standardized response criteria for gliomas.[66] Recognizing the limitations of MRI alone in differentiating true progression from treatment-related effects, PET-RANO incorporates metabolic imaging findings, primarily from amino acid PET, into response assessments alongside conventional MRI, neurologic status, and steroid use. This combined approach aims to improve diagnostic accuracy in challenging scenarios such as pseudoprogression, post-radiation changes, and evaluation of nonenhancing tumor burden. Although PET-RANO criteria are still in early implementation phases, they represent an important step toward harmonizing multimodal imaging interpretation in clinical trials and practice.

In 2023, the RANO 2.0 working group proposed updated response criteria for gliomas, reflecting changes in clinical practice and the increasing use of molecular classification[120] ([Table 1]). RANO 2.0 retains the core principles of the original framework but integrates more consistent guidelines for imaging timing, standardized volumetric thresholds, and decision rules that incorporate IDH status and other biomarkers. It also emphasizes harmonization with clinical trial design, ensuring that response assessments align with therapeutic mechanisms and endpoints.

Together, these frameworks reflect the growing complexity of neuro-oncologic care and the need for disease- and treatment-specific imaging strategies. For neurologists, familiarity with these criteria enhances the ability to interpret imaging meaningfully and to guide patients through increasingly nuanced treatment pathways.

Emerging Frontiers

Radiomics uses artificial intelligence (AI) to extract features from routine imaging surveillance that cannot be detected through conventional imaging analysis. Images typically undergo pre-processing, tumor segmentation, and feature extraction, after which a machine-learning model is generated and validated.[121] These techniques have been applied to MRI,[122] amino acid PET,[123] and MRS.[124] Several studies have demonstrated potential utility in glioma and brain metastasis, including identifying biomarkers,[125] predicting survival,[122] and distinguishing treatment effects from viable tumor.[126] [127] These techniques are being increasingly incorporated into clinical trials.[128] Radiogenomics combines available genomic data to a radiomics analysis, thereby increasing the accuracy of machine learning algorithms.[122] Limitations include a lack of standardization in evaluating and reporting high-quality radiomics in studies, a missing emphasis on the biologic meaning of radiomics features, and the absence of multicenter validation.[129]

The development of imaging tracers, both targeting specific receptors and interrogating metabolic substrates beyond pyruvate, has increased rapidly over recent decades. These include PET tracers targeting epidermal growth factor receptor (EGFR) expression in gliomas, chemokine receptor type 4 (CXCR4) in lymphoma, translocator protein (TSPO), and prostate-specific membrane antigen (PSMA).[130] Imaging of hypoxia and other amino acids are growing applications, and PET probes targeting the immune system, specifically the metabolic reprogramming of T-cells,[130] represent an exciting frontier in the age of immunotherapy. Hyperpolarized MRS probes under study include [2-¹³C] pyruvate,[131] [1-¹³C]α-ketoglutarate, and [1-¹³C]glutamate in IDH-mutant gliomas,[132] [1-¹³C] urea for brain perfusion,[133] and [5-¹³C,4,4-²H2,5-¹⁵N]-L-glutamine to monitor glutamine metabolism.[134] The combination of [1-¹³C] dehydroascorbic acid with [1-¹³C] pyruvate has demonstrated the ability to probe redox status and glycolytic flux simultaneously,[135] with promising neurologic and oncologic applications.

Equity in Access

Despite advances in neuro-oncologic imaging, access to high-resolution MRI, MR perfusion, spectroscopy, and PET imaging remains uneven across healthcare settings.[136] Patients treated in safety-net hospitals, rural centers, or under public insurance plans may face limited availability of these tools, delayed scheduling, or outright denial of coverage, particularly for sequences considered non-standard despite their clinical utility.[137] These disparities can lead to delayed diagnoses, ambiguous treatment-response assessments, and reduced eligibility for clinical trials that require advanced imaging.[138] Furthermore, lack of access to centralized imaging review or volumetric tools can undermine consistency in care. Addressing these gaps requires broader policy efforts, including reimbursement reform, infrastructure investment in underserved areas, and inclusion of equity metrics in trial design.[139] As imaging becomes increasingly central to precision neuro-oncology, ensuring equitable access must remain a core priority.

Conclusion

Neuroimaging plays a central role in the diagnosis, management, and surveillance of patients with primary and metastatic brain tumors. Advances in MRI techniques—including perfusion imaging, diffusion tensor imaging, functional MRI, susceptibility-weighted imaging, and MR spectroscopy—as well as complementary modalities such as amino acid PET, have significantly enhanced our ability to characterize tumor biology, assess treatment response, and guide surgical and radiation planning. Standardized frameworks such as the RANO criteria have brought greater consistency to clinical trial assessment and routine care, though interpretation remains nuanced due to phenomena like pseudoprogression, pseudoresponse, and treatment-related effects. Emerging applications of radiomics and artificial intelligence hold promise for augmenting imaging interpretation by extracting quantitative features and predictive biomarkers not readily discernible through conventional analysis. As imaging continues to evolve alongside molecular classification and targeted therapies, it is critical to ensure equitable access to advanced modalities across healthcare settings. For neurologists and other clinicians, a working knowledge of these imaging tools and clinical decision frameworks is essential to delivering precise, patient-centered neuro-oncologic care.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 07 August 2025

Accepted: 24 September 2025

Accepted Manuscript online:
13 October 2025

Article published online:
30 October 2025