 
Vol. 11/ Núm. 2 2024 pág. 272 
https://doi.org/10.69639/arandu.v11i2.266 
Advanced Imaging Techniques in Preoperative Planning for 
Brain Tumor Resection: Evaluating the Impact on Surgical 
Precision and Neurological Outcomes 
 
Técnicas avanzadas de imágenes en la planificación preoperatoria para la resección de 
tumores cerebrales: evaluación del impacto en la precisión quirúrgica y los resultados 
neurológicos 
 
Fredy Agustín Gutiérrez Hernández 
gutez32@gmail.com 
https://orcid.org/0009-0004-0182-9088 
Hospital Departamental de Villavicencio Meta Colombia 
Colombia 
 
Giovanni Andres Arias Audor 
andresariasa672@gmail.com  
https://orcid.org/0000-0003-1295-9529 
Universidad de Santander 
Colombia 
 
Natalia Quintero Serrano 
nqs90@hotmail.com 
https://orcid.org/0009-0007-6214-5976 
Universidad Santiago de Cali 
Colombia 
 
Andrés Felipe Gutiérrez Robayo  
af.gutierrezrobayo@gmail.com  
https://orcid.org/0009-0001-9279-923X  
Investigador Independiente 
Colombia 
 
Juan Sebastián Gracia Guillén 
af.gutierrezrobayo@gmail.com  
https://orcid.org/0009-0007-1423-6881  
Universidad Cooperativa de Colombia 
Colombia 
 
Natalia Fernanda Torres García 
n.f.torresgarcia@gmail.com 
https://orcid.org/0009-0009-0316-0388  
Investigador Independiente 
Colombia 
 
Artículo recibido: 20 julio 2024   -  Aceptado para publicación: 26 agosto 2024 
Conflictos de intereses: Ninguno que declarar 
 
ABSTRACT 
This study investigates advanced imaging techniques and their impact on surgical precision and 
neurological  outcomes  in  preoperative  planning  for  brain  tumor  resections.  Selected  studies 

Vol. 11/ Núm. 2 2024 pág. 273

include large human sample sizes, peer-reviewed research, systematic reviews, and meta-analyses

focusing on advanced imaging techniques and their impact on surgical precision and neurological

outcomes. Latest imaging modalities experiments on animal studies are considered. Systematic

review of recent literature on advanced imaging modalities—such as MRI, fMRI, PET, and

DTI—and their application in preoperative planning. Our findings suggest advanced imaging

techniques, including Functional MRI, Ultra-High Field MRI, Diffusion Tensor Imaging (DTI),

PET/CT and Deuterium Magnetic Resonance Spectroscopy (2H MRS) improve surgical precision

and neurological outcomes in brain tumor resections by enhancing tumor targeting, reducing

morbidity, and improving resection accuracy. Future advancements should focus on integrating

and optimizing these technologies to further improve preoperative planning and patient-specific

treatment strategies.

Keywords: advanced imaging, brain tumor resection, surgical precision, neurological

outcomes, preoperative planning

RESUMEN

Este estudio investiga técnicas de imagen avanzadas y su impacto en la precisión quirúrgica y los

resultados neurológicos en la planificación preoperatoria para resecciones de tumores cerebrales.

Los estudios seleccionados incluyen muestras humanas de gran tamaño, investigaciones revisadas

por pares, revisiones sistemáticas y metanálisis centrados en técnicas de imagen avanzadas y su

impacto en la precisión quirúrgica y los resultados neurológicos. Se consideran los últimos

experimentos en modalidades de imágenes en estudios con animales. Revisión sistemática de la

literatura reciente sobre modalidades avanzadas de imágenes, como MRI, fMRI, PET y DTI, y su

aplicación en la planificación preoperatoria. Nuestros hallazgos sugieren que las técnicas de

imagen avanzadas, que incluyen resonancia magnética funcional, resonancia magnética de campo

ultraalto, imágenes con tensor de difusión (DTI), PET/CT y espectroscopia de resonancia

magnética de deuterio (2H MRS), mejoran la precisión quirúrgica y los resultados neurológicos

en las resecciones de tumores cerebrales al mejorar la localización del tumor. , reduciendo la

morbilidad y mejorando la precisión de la resección. Los avances futuros deberían centrarse en la

integración y optimización de estas tecnologías para mejorar aún más la planificación

preoperatoria y las estrategias de tratamiento específicas del paciente.

Palabras clave: imágenes avanzadas, resección de tumores cerebrales, precisión

quirúrgica, resultados neurológicos, planificación preoperatoria

Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo

licencia Creative Commons Atribution 4.0 International.

Vol. 11/ Núm. 2 2024 pág. 274

INTRODUCTION

Brain tumors encompass over 100 types, each affecting the brain uniquely, with symptoms

ranging from cognitive impairment to psychological changes. Despite ongoing research, survival

rates for brain cancer have stagnated, impacting about 94,390 Americans annually, with

approximately 1 million currently living with the condition. (Brain Tumor Facts - National Brain

Tumor Society, 2024)

Glioblastoma multiforme (GBM), recognized as the most lethal form of brain cancer,

typically limits survival to just 12–16 months following diagnosis. Although its annual occurrence

is relatively modest—around 3.19 cases per 100,000 in developed regions—its prevalence is

gradually rising in certain areas due to demographic shifts toward aging populations and advances

in detection methods. (Obrador., 2024) Generally diagnosed at an average age of 64, with the

highest occurrence seen between 75 and 84 years, GBM is traditionally identified through

histopathological analysis. However, contemporary diagnostic strategies now incorporate a range

of imaging modalities including Functional MRI, Ultra-High Field MRI, Diffusion Tensor

Imaging (DTI), PET/CT in conjunction with Brain MRI, Deuterium Magnetic Resonance

Spectroscopy (2H MRS), Intraoperative MRI, and 3D Imaging and Mapping, all of which offer

in-depth tumor profiling and surgical guidance (Thakkar et al., 2014).

Novel modalities of neurological imaging technologies transformed preoperative planning

for brain tumor resections by enhancing the precision and efficacy of surgical interventions. High-

resolution MRI has evolved with advanced sequences and increased spatial resolution and is

offering unparalleled detail of brain anatomy and enabling precise tumor localization and

characterization. Functional MRI (fMRI) is advanced in temporal and spatial resolution which

allow detailed mapping of brain activity and vital functional areas, critical for avoiding damage

to regions responsible for essential functions such as speech and motor control. Diffusion Tensor

Imaging (DTI) improves higher resolution and more accurate tractography algorithms while

providing detailed visualization of white matter pathways and helping to navigate around critical

neural networks to minimize postoperative deficits. Positron Emission Tomography (PET)

imaging is when combined with MRI (PET-MRI) which is being used to enhances differentiation

of tumor tissues from normal brain tissue based on metabolic activity aiding in more accurate

tumor delineation and characterization. Magnetic Resonance Spectroscopy (MRS) has advanced

with better spectral resolution and more comprehensive metabolite profiling and is assisting in

distinguishing between different tumor types and grades based on their unique metabolic

signatures. Intraoperative MRI systems are now more sophisticated because they are offering real-

time imaging with improved spatial and temporal resolution for immediate adjustments to the

surgical approach based on intraoperative findings. 3D Imaging and Mapping is also integrated

with high-definition modeling and augmented reality and is providing surgeons with detailed,

Vol. 11/ Núm. 2 2024 pág. 275

interactive views of the brain’s structure and tumor, thus facilitating enhanced surgical navigation

and planning. Novel advancements up to 2024 include the incorporation of AI and ML algorithms

to automate and refine image analysis, improve diagnostic accuracy, and predict surgical

outcomes. All these technological innovations are playing their role in enhancing preoperative

planning by improving tumor localization, preserving neurological functions, and ultimately

optimizing surgical outcomes. (Martucci., 2023) (CE et al., 2023) (Jian., 2022)

In this review, we will be focusing on 1). Functional MRI (fMRI), 2) Intraoperative MRI,

3) High-Resolution MRI, 4) Diffusion Tensor Imaging (DTI), 5) 3D Imaging and Mapping, 6)

Deuterium Magnetic Resonance Spectroscopy (2H MRS), and 7) Positron Emission Tomography

(PET). Each of these approaches is integral in refining surgical precision and optimizing treatment

strategies.

Hypotheses or Research Questions

Hypothesis: Advanced imaging techniques improve surgical precision and reduce

postoperative neurological deficits.

Research Questions: 1), How do various imaging modalities contribute to tumor

localization and surgical planning? 2), What is the impact of these techniques on postoperative

neurological outcomes?

METHOD

Inclusion and Exclusion Criteria

We prioritize studies with large human sample sizes. We added studies that have followed

rigorous methodologies such as systematic reviews and meta-analyses that adhere to PRISMA

guidelines. Studies that lack clear methodological transparency or employ less stringent methods

were excluded as they may not provide reliable evidence. Studies should report detailed and

clinically significant outcomes such as those that directly impact preoperative planning and

postoperative neurological results. Studies that provide vague or insufficient outcome data, or

focus solely on technical aspects without clear clinical correlations should be avoided, and we did

not added any information from unauthentic sources. Both human and animal models added

studies are added and we intend keeping our review current we prioritize papers published in 2021

to 2024 to discuss and capture novel advancements.

Exclusion Criteria

We excluded reviews with limited applicability to broader clinical settings or those which

studies may have high internal validity but low external validity. All the selected papers must

discuss brain tumours with advanced imaging modalities were included and those focusing more

on treatment were excluded. Those papers with limited statistical analysis or low power, such as

those not reporting key metrics like odds ratios, effect sizes, or confidence intervals were

excluded. Studies that focus heavily on the technological aspects of imaging without correlating

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these to improved patient outcomes e.g., studies that report on imaging resolution without linking

it to clinical benefits were also not considered.

Keywords

Our primery keywords were: “Advanced imaging” "brain tumor resection," "surgical

precision," "neurological outcomes," and "preoperative planning." We also designed some

secondary keywords to focus more on different advancements of imaging modalities such

as"functional MRI (fMRI)," "high-resolution MRI," "positron emission tomography (PET),"

"diffusion tensor imaging (DTI)" ( Deuterium Magnetic Resonance Spectroscopy (2H MRS)"

"intraoperative MRI" "magnetic resonance spectroscopy (MRS), and "3D imaging and mapping."

To refine the search and avoid irrelevant studies, the exclusion criteria include terms such as

"traditional imaging," "conventional MRI," and "non-advanced imaging methods," as well as

"animal studies," "in vitro studies," and "non-clinical research." These exclusions ensure that the

focus remains strictly on advanced imaging techniques relevant to human clinical contexts.

The corresponding MeSH string construction for an effective literature search is:

("Advanced Imaging" OR "High-Resolution MRI" OR "Diffusion Tensor Imaging" OR

"Functional MRI" OR "Positron Emission Tomography" OR "Intraoperative MRI" OR "3D

Imaging and Mapping") AND ("Brain Tumor Resection" OR "Magnetic Resonance

Spectroscopy" OR "Brain Neoplasms" OR "Neurosurgery") AND ("Surgical Precision" OR

"Surgical Outcomes" OR "Surgical Planning") AND ("Neurological Outcomes" OR

"Neurological Function" OR "Postoperative Neurological Deficits") AND ("Preoperative

Planning" OR "Preoperative Care" OR "Surgical Preparation")

Selection

This review included diverse range of patient demographics, including various ages, sexes,

and tumor types undergoing diverse range of imaging techniques. We specifically focus on studies

involving adults and children with gliomas, meningiomas, and metastatic tumors but we also

selected some most recent experiments on animals like on mice for getting precision of advanced

technology and its applicability to humans. All the studies are only published on peer-reviewed

journals like PubMed, Cochrane library and Google Scholar. Only high level of papers selected

for example, Systematic reviews and Meta analysis, Randomized Controlled Trials, and other

Cohorts to come with reliable conclusion. Selected studies underwent a process known as Data

Extraction. To extract potential predictors, we applied the standardized extraction forms

highlighting such important information as the type of the imaging technology, patient’s

characteristics, tumour type, and surgical results. The search instruments and bibliographic aids

were used to collect data by following advanced searching techniques to avoid omission of any

material. Information about the application of each imaging modality in preoperative planning

and the effects of these imaging studies on surgical outcomes was the primary area of interest; we

endeavoured to obtain sufficient and pertinent data to perform an analytical review on. Research

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Analysis involved exposing the various forms of studies, for instance, experimental,

observational, and clinical trials. Looking at the design and methodology used in each of the

studies we sought to establish the effect of advanced imaging features on surgical accuracy and

patient’s neurological status. Some of the techniques used in data analysis were categorization

and aggregation of findings, comparison between findings from various imaging techniques.

Shown below is a prisma flow diagram with included studies which depict how further

development of imaging modalities affect preoperative planning and enhancement of surgical

results.

Grafico 1

Identification of new studies via databases and registers

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RESULTS

Table 1

Effectiveness of Imaging Techniques For Brain Tumor

Study

Author 1st

Name/Year

Imaging

Technique

Sample

Size

Methodological

Details

Key Findings

Statistical

Summary (Odd

Ratio+ ER)

Licia, 2021

Functional

MRI

3280

participants

Systematic

review and meta-

analysis,

PRISMA-

compliant.

Evidence level

assessed via

Newcastle-

Ottawa scale and

Moga tool.

Measures:

Karnofsky,

modified Rankin,

British Medical

Research

Council.

Reduced

Morbidity

Odds Ratio 0.25, ER

11%

Annabelle,

2022

Ultra-High

Field MRI

Technique

46 (43

human, 3

animal

models)

Systematic

review of 7T

MRI studies from

PubMed;

included human

and animal

studies; followed

PRISMA

guidelines;

excluded

irrelevant

studies.

Enhanced

resolution,

precise

targeting,

advanced

grading.

UHF MRI improved

SNR, reduced GBM

volume by 7.4%,

detected more

microbleeds, and

identified significant

fractal dimension

differences.

Khursheed,

2019

Diffusion

Tensor

Imaging

(DTI)

128

Prospective

cohort study,

preoperative

neurologic status

and tumor

volume were

assessed. MRI-

based surgical

plans were

reviewed with

DTI, classifying

tracts as

displaced,

infiltrated, or

disrupted.

Postoperative

outcomes were

evaluated.

Advanced

imaging

techniques,

including

DTI,

improved

surgical

precision in

47% of cases.

Displaced

fibers were

linked to

lower

neurologic

deterioration

(7.1%)

compared to

disrupted

fibers

(13.9%),

enhancing

resectability

outcomes.

The statistical

summary revealed

that displaced fibers

had an odds ratio for

reduced neurologic

deterioration of 0.49

(7.1% vs. 13.9% in

disrupted/infiltrated

fibers).

Yi Feng,

2024

PET/CT and

Brain MRI

2,298

Patient

The

methodology

aimed to evaluate

metastasis

detection

efficacy using

PET/CT and

MRI, analyzing

cut-off values

Preoperative

PET/CT and

MRI enhance

surgical

precision and

neurological

outcomes by

improving

metastasis

Higher metabolic

parameters

(SUVmax HR =

12.94, SUVmean

HR = 11.33,

SULpeak HR = 9.65,

MTV HR = 9.16,

TLG HR = 12.06)

and larger nodules

Vol. 11/ Núm. 2 2024 pág. 279

and survival

rates.

detection and

planning for

brain tumor

resection.

(solid HR = 0.12,

sub-solid HR = 0.61)

significantly affect

surgical precision

and outcomes.

Kyo, 2023

Deuterium

Magnetic

Resonance

Spectroscopy

(2H MRS)

Mouse

model, 9

lesions.

The study used

deuterium

magnetic

resonance

spectroscopy (2H

MRS) with a

specialized SPin-

ECho sequence

to analyze

metabolic

changes in brain

lesions,

employing

deuterated

glucose to

measure tumor

and necrosis

ratios.

(2H MRS)

effectively

differentiated

tumor from

necrosis,

aiding in

decision

planning for

brain tumor

treatment.

The statistics show a

strong correlation

(Pearson’s r = 0.87)

between 2H MRS

ratios and tumor

fraction, explaining

77% variance.

Ghaith,

2024

Intraoperative

MRI

25 patients

Retrosprective

analysis of 25

BM resection

with 3-Tesla

iMRI. The

objective was to

evaluate iMRI's

impact on

resection extent,

surgical

outcomes, and

postoperative

complications.

Enhanced

tumor

resection

precision.

iMRI improved

extent of resection

(EOR) from 91.06%

to 95.4%, with 24%

of cases achieving

gross total resection.

Table 2

Impact on Neurological Outcomes

Study

Imaging

Technique

Pre-operative planning

Postoperative Neurological

Outcomes

Study

Functional MRI

Preoperatively, Advanced MRI enhanced

surgical precision, cut complications:

Odds Ratio 0.25, ER 11%.

Improved post-surgical outcome,

higher Karnofsky scores (Hedges g,

0.66; P = .004), and fewer

complications (ER 11%).

Study

Ultra-High Field

MRI Technique

Sensitivity: 85-95%, Specificity: 80-90%,

PPV: 75-85%, NPV: 90-95%, Accuracy:

85-90%, CNR: 10-20 dB; high values

enhance precision in preoperative

planning and decision-making.

Post-operative outcomes were

improved with precision, rare

complications.

Study

Diffusion Tensor

Imaging (DTI)

MRI-based plans were revised with DTI in

47% of cases, enhancing surgical

accuracy.

DTI reduced neurologic deterioration

to 7.1% for displaced fibers, versus

13.9% for disrupted fibers.

Study

Positron Emission

Tomography

(PET)/CT

PET/CT's precise detection of solid

nodules ≥ 8.0 mm (HR = 0.12) and sub-

solid nodules ≥ 10.0 mm (HR = 0.61)

enhances surgical accuracy, identify tumor

characteristics, allow individualized

therapy

Elevated PET/CT metabolic

parameters (SUVmax HR = 12.94,

SUVmean HR = 11.33, SULpeak HR

= 9.65) indicates higher risk, needs

careful interpretation.

Study

Deuterium

Magnetic

Resonance

Spectroscopy (2H

MRS)

Offers precise tumor-to-necrosis ratios,

enhancing planning by accurately defining

tumor boundarie

Predict outcomes by improving

tumor detection and differentiation,

potentially affecting recovery and

treatment effectiveness.

Vol. 11/ Núm. 2 2024 pág. 280

Study

Intraoperative MRI

iMRI guided extensive tumor resection,

improving precision with real-time

imaging feedback

Improved outcomes, 60% of patients

maintained preoperative neurological

status; 24% improved, while 16%

experienced worsening deficits; no

wound-healing issues.

RESULTS

Luna et al. (2021) meta-analysis evaluated impact of presurgical fMRI on brain tumor

resection outcomes. Methodologically, 68 studies (n = 2756) were analyzed, revealing an overall

event rate (ER) of 11% for unfavorable outcomes post-surgery. The primary outcome, comparing

presurgical fMRI with standard imaging showed that fMRI significantly reduced postsurgical

functional deterioration (odds ratio = 0.25; 95% CI: 0.12–0.53; P < .001) and improved Karnofsky

performance scores (Hedges g = 0.66; 95% CI: 0.21–1.11; P = .004). fMRI did not affect the gross

total resection rate (odds ratio = 1.45; 95% CI: 0.49–4.31; P = .50). Modest technologies like

presurgical fMRI improved surgical outcomes and reduced neurological deficits by enhancing

tumor delineation and functional mapping which leads more precise tumor resection and better

preservation of critical brain functions. Innovations like intraoperative MRI and cortical

stimulation further refine surgical accuracy and minimize postoperative deficits.

Shaffer et al. (2022) focused on advancements in Ultra-High Field MRI (UHF-MRI), with

spatial resolution and enhanced contrast which enabled detailed tumor visualization and

microstructural details. UHF-MRI provides precise mapping of tumor boundaries and infiltration

aiding in more accurate diagnosis helping in surgical planning. Convolutional neural networks

(CNNs) models have attained high accuracy in tumor classification, segmentation and prediction

of patient survival which outperform traditional methods. Radiomics, by extracting high-

dimensional data from medical images stands out for its precision of tumor characterization and

individualized treatment planning demonstrating improved decision-making and patient

outcomes. PET/CT imaging has contributed by providing functional and metabolic insights that

complement anatomical details enabling more accurate tumor localization and assessment of

treatment response. Statistically deep learning algorithms have shown increased accuracy in

predicting outcomes with some studies reporting accuracies above 90%. Radiomics gives

statistically significant correlations between image-derived features and clinical outcome while

PET/CT imaging has improved specificity and sensitivity in tumor detection.

Khan et al. (2019) demonstrated performance of diffusion tensor imaging (DTI) which led

to a change in the surgical corridor for 47% of patients. Displaced fibers correlated with lower

neurologic deterioration (7.1%) compared to disrupted/infiltrated fibers (13.9%). Odds ratio for

reduced neurologic issues with displaced fibers was 0.49 which means it provide notable

improvement in outcomes associated with this classification.

Feng et al. (2024) discussed about PET/CT and MRI for preoperative brain tumor planning.

They summarize efficacy in improving surgical precision and outcomes, PET/CT measures tumor

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metabolism using parameters like SUVmax, SUVmean, SULpeak, MTV, and TLG, which are

crucial for evaluating tumor aggressiveness. This research set cut-off values for these metrics,

SUVmax at 1.09, SUVmean at 0.26, SULpeak at 0.31, MTV at 0.55, and TLG at 0.81. These

values were derived through ROC analysis and indicate thresholds where higher metabolic

activity suggests a more aggressive tumor. For survival outcomes, patients with solid nodules ≥

8.0 mm and sub-solid nodules ≥ 10.0 mm who underwent PET/CT plus MRI had longer OS (HR

= 0.44, p < 0.001) compared to those who did not. Conversely those patients with metabolic

parameters above the cut-off values exhibited reduced OS: SUVmax (HR = 12.94, p < 0.001),

SUVmean (HR = 11.33, p < 0.001), SULpeak (HR = 9.65, p < 0.001), MTV (HR = 9.16, p =

0.031), and TLG (HR = 12.06, p < 0.001). PET/CT excels in preoperative brain tumor planning

by offering detailed metabolic insights through parameters like SUVmax (cut-off 1.09) and TLG

(cut-off 0.81) showing tumor aggressiveness and guiding surgical precision. It is dicussed that

contrast-enhanced CT is while effective for anatomical detail but it lacks metabolic information

limiting its ability to differentiate tumor types and predict outcomes. MRI provides superior soft

tissue contrast and functional data but does not measure metabolic activity. Advanced MRI

techniques like fMRI and DTI improve surgical planning by mapping brain function and pathways

complementing PET/CT’s metabolic data for optimal surgical outcomes (Moon et al., 2020;

Kurtipek et al., 2021).

Song et al., 2023 highlights Deuterium Magnetic Resonance Spectroscopy (2H MRS) for

brain tumors, more precisely while distinguishing them from radiation necrosis. Their research

involved mouse model of mixed radiation necrosis and glioblastoma 2H MRS was used to

measure metabolic activity by monitoring the conversion of deuterated glucose to lactate and

glutamate and their statistical results revealed strong linear correlation (Pearson’s r = 0.87)

between the lactate-to-glutamate ratio and tumor fraction with 77% of the variation in this ratio

attributable to tumor percentage (r² = 0.77). High level of correlation supports technique’s

efficacy in quantifying tumor content within mixed lesions and it is also declared that 2H MRS

offers a promising supplementary tool for both pre-operative planning by accurately defining

tumor boundaries, and postoperative assessment, by evaluating residual tumor and distinguishing

it from necrotic tissue.

Altawalbeh et al., 2024 reseach finings shows intraoperative magnetic resonance imaging

(iMRI) enhance surgical planning and outcomes in brain metastasis (BM) resection. Key findings

show that iMRI improved extent of resection (EOR) which help achieving gross total resection

(GTR) in 84% of patients. Real-time imaging feedback facilitated additional tumor removal in

24% of cases as results declared, raising EOR from 91.06% to 95.4%. Surgical duration averaged

219.9 minutes with iMRI adding 61.7 minutes and postoperative neurological status remain stable

in 60% of patients and improved in 24% with complications such as wound healing absent.

Follow-up MRIs revealed local recurrence in 1 of 13 patients at 3 months and 2 of 8 at 6 months

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with significant in-brain progression noted and all these findings revealed iMRI improves

resection precision and patient outcomes, though further prospective studies are needed to confirm

its impact on survival and optimize its clinical applications. Altawalbeh and his team also

compare intraoperative MRI (iMRI) for brain metastases (BMs) to other methods and they

demonstrates that iMRI offers enhanced precision in tumor resection similar to its established

benefits in glioma surgery. They highlights iMRI's effectiveness in improving extent of resection

(EOR) achieving gross total resection (GTR) in 84% of cases while providing real-time feedback

which is cause of EOR improvement. While in traditional methods, including neuronavigation

alone or other imaging modalities like intraoperative ultrasound have shown less impact on EOR

and may involve limitations in image resolution or accuracy. iMRI extended surgical time but it

must be considered that it did not increase the risk of surgical site infections (SSIs) which is a key

advantage over older methods. Even knowing weakness of the study including a small number of

patients as well as a relatively short follow-up period , it is quite clear that iMRI can help to better

define the approach to surgery for BM resections implying the further relevance of this tool for

BMs as is the case for gliomas and the potential to establish new standards of a truly maximally

safe approach.

Comments

Advanced imaging techniques like fMRI and UHF-MRI give surgeons highly detailed

maps of the brain before surgery showing both the tumor and its proximity to critical functions

like speech and motor areas. This enables more precise surgical planning to minimize damage to

healthy tissue.

PET/CT provides metabolic insights that highlight the aggressiveness of tumors. By

understanding both the structural and functional aspects of the tumor, surgeons can make more

informed decisions about the surgical approach and postoperative care.

Tools like intraoperative MRI (iMRI) provide real-time imaging during surgery allow

surgeons to continuously check and adjust their technique so lead to accurate tumor removal.

Real-time feedback reduces the risk of leaving tumor remnants and helps preserve healthy

brain functions. By integrating these imaging technologies, surgeons are better equipped to avoid

critical brain areas, leading to fewer neurological deficits post-surgery. Patients experience faster

recovery and improved long-term functional outcomes.

Combining anatomical imaging with functional and metabolic data helps create a

comprehensive surgical plan. This integration of detailed insights allows for more targeted

interventions, improving both the safety and efficacy of tumor resections.

DISCUSSION

Recent advancements in imaging modalities for oncology in brain tumor have enhanced

diagnosis capabilities, monitor and treat tumors with greater precision and accuracy. (Sabeghi.,

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2024) Traditional imaging modalities like MRI and CT provide excellent anatomical detail but

mostly, they fall short in distinguishing between healthy and cancerous tissues due to their

reliance on structural rather than metabolic information. And now, the advent of positron emission

tomography (PET) imaging has addressed these limitations. Novel radiotracers has emerged as a

powerful tool in oncology. Radiotracers such as 18F-FDG, [18F] FET and newer protein-based

markers like [18F] PARPi and fibroblast activation protein inhibitor (FAPI) provides details of

metabolic and molecular characteristics of tumors. For instance while 18F-FDG has been a

cornerstone in PET imaging but its effectiveness in brain cancer is limited by the high glucose

uptake in normal brain tissue, which has led to the development of amino acid-based tracers like

[18F]FET which provide better differentiation between malignant and healthy tissues. Protein-

based tracers like [18F] PARPi which target cancer-specific proteins rather than metabolic

activity, are also well known for their even greater specificity making them valuable in identifying

and monitoring tumors with minimal interference from surrounding healthy tissue. All these

modifications have led us to initial diagnosis as well as provide us assistance for response to

treatments such as stereotactic radiosurgery where distinguishing between tumor recurrence and

treatment-induced changes is vital. (Huang., 2024)

PET and PET/MRI in neuro-oncology, offering a complementary approach to traditional

imaging modalities like MRI, while MRI excels in providing high-resolution structural images

with exceptional tissue contrast particularly useful for conditions like epilepsy and tumors yet it

is focused on anatomical detail. PET use along with MRI provides crucial physiological data by

visualizing brain metabolism and functional processes which makes PET more valued in

oncology for differentiating tumor grades, guiding biopsies, assessing treatment response, and

detecting recurrence. (Galldiks., 2024)

MRI and PET integration in the form of PET/MRI capitalizes on strengths of both

modalities as this combination enhances soft tissue contrast and reduces ionizing radiation

exposure which lead overall diagnostic accuracy improvement. PET/MRI is also discussed

pediatric populations and in distinguishing between tumor recurrence and treatment changings

like radiation necrosis. For instance, use of C11-methionine in PET/MRI has shown superior

diagnostic differentiating accurateness between glioma recurrence and post-treatment changings

achieving sensitivity and specificity rates that surpass those of MRI or PET alone. (Jeltmea., 2024)

PET/MRI is vital of primary CNS lymphomas (PCNSLs) and other brain tumors like

meningiomas and brain metastases because they aid in providing multiparametric approach. For

example, [86Ga]-DOTATATE PET/MRI use has proven effective in detecting smaller

meningiomas and distinguishing them from healthy tissue and post-surgical changes. Despite its

advantages, PET/MRI also faces challenges of high costs or limited availability and potential for

false positives in cases of inflammation or infection. Complexity of acquiring and interpreting

PET/MRI data requires specialized training or collaboration between PET and MRI teams.

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Current research support PET/MRI's potential to impact patient care in neuro-oncology for

providing us more inclusive knowledge of both the structural and functional aspects of brain

tumors.

Research by Ouyang., 2024 discussed FDG-PET, commonly used PET imaging technique

which relies on glucose metabolism to differentiate between malignant and normal cells. Its utility

in brain imaging is constrained by the naturally high glucose uptake in healthy brain tissue which

can blur the delineation of tumor boundaries. Amino acid-based PET tracers have emerged as a

more effective alternative which address this limitation, as they allow for clearer visualization of

tumor margins given that normal brain tissue typically shows minimal amino acid uptake.

Radiomics and deep learning are revolutionizing neuro-oncology as they anable extraction

of subvisual and quantitative data from routine imaging like MRI and PET to create detailed 3D

tumor profiles. Radiomics involves steps such as data acquisition, image preprocessing, tumor

segmentation, and model generation. Radiogenomics connects genetic mutations with imaging

features while deep learning, especially through convolutional neural networks (CNNs), enhances

this process by refining feature selection and modelling for improved diagnostic and prognostic

precision. (Sabeghi., 2024)

These are techniques of neuro-oncology, which altogether have shown great promise in

areas such as diagnosis and treatment response monitoring, prognostication, and the

determination of tumor biomarkers and genomics. For example, radiomics stood successful in

differentiating glioblastoma multiforme (GBM) from solitary metastasis and primary central

nervous system lymphoma (PCNSL) with studies demonstrating potential for three-class

classification models to distinguish between GBM, metastasis, and PCNSL. Deep learning

models like CNNs also give high accuracy in these tasks, with recent research demonstrating their

superiority over traditional machine learning approaches in classification tasks. Recent

advancements include the integration of radiomics and deep CNN models that distinguish GBM

from brain metastases by analyzing oxygen metabolism data obtained from MRI. These models

have demonstrated superior accuracy compared to traditional radiologist evaluations and

radiomics features have been leveraged to differentiate low-grade gliomas (LGGs) from the

peritumoral regions (PTR) of GBM which could help limit the amount of healthy tissue exposed

to radiation during therapy. (Bathla., 2024) (Frosina., 2024) (Chukwujindu., 2024)

Radiomics is used to identify primary sources of different types of metastases for

instance,distinguishing between lung and breast cancer metastases, or between lung cancer and

melanoma metastases or brain cancer metastases with high accuracy. Deep learning has been

beneficial in real-time intra-operative diagnosis with enhanced glioma diagnoses accuracy during

surgery. Deep learning, radiomics, and radiogenomics are proving to be powerful tools in

enhancing the prediction of survival tumor grading, and genetic profiling in gliomas. For example,

radiomics features extracted from MRI scans have been utilized to predict overall survival more

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accurately than conventional methods like the Response Assessment in Neuro-Oncology

(RANO). Radiogenomics models have shown success in identifying key genetic markers,

including IDH mutations, MGMT promoter methylation, and 1p19q codeletion, which are

essential for personalized treatment strategies. (Prajwal., 2024) (Śledzińska., 2024) Despite

encouraging findings from various studies, radiomics and deep learning have yet to achieve

widespread clinical use in neuro-oncology. Current efforts focus on standardizing radiomics

characteristics and investigating their biological foundations which are critical for integrating

these technologies into everyday clinical workflows. Overcoming its barriers will be harnessing

the full potential of radiomics and deep learning to revolutionize patient management by offering

more precise non-invasive diagnostic and prognostic options. (Galldiks., 2024)

Magnetic Resonance (MR) perfusion imaging neuro-oncology enable detailed assessment

of tissue-level blood flow critical for oxygen and nutrient delivery and it is valuable in measuring

cerebral blood volume (CBV) for its role in brain tumor evaluation. Increased CBV correlates

with greater tumor aggressiveness and can assist in glioma grading and biopsy planning, guiding

targeted therapies and tracking timely disease progression. High-grade brain tumors are

associated with increased neovascularization and both CT and MR perfusion methods have shown

that higher CBV and permeability correlate with higher tumor grades. For example, studies

demonstrated, high-grade tumors exhibit higher mean CBV values compared to low-grade

tumors. A relative cerebral blood volume (rCBV) threshold of 1.75 has been shown to

differentiate low- and high-grade tumors with a sensitivity of 95% and a specificity of 57.5%.

MR perfusion can help distinguish between tumor progression and pseudoprogression which was

a challenge in neuro-oncology. Recurrent tumors tend to show elevated rCBV whereas areas of

radiation necrosis typically have reduced rCBV. (Sabeghi et al., 2024)

MR perfusion imaging plays a vital role in differentiating brain tumors, particularly in

distinguishing primary CNS lymphoma from malignant gliomas. CNS lymphomas generally

exhibit low vascularization, resulting in low or moderately elevated intra-tumor cerebral blood

volume (CBV), contrasting with the typically higher CBV seen in gliomas. This differentiation is

crucial since both gliomas and lymphomas can show infiltrative growth that mimics normal brain

tissue. Elevated CBV outside the visible tumor region can indicate the infiltrative zones of

gliomas and lymphomas, aiding in distinguishing them from metastases. MR perfusion imaging

utilizes three main techniques: dynamic susceptibility contrast (DSC), dynamic contrast

enhancement (DCE), and arterial spin labelling (ASL). DSC and DCE require contrast agents and

monitor their concentration dynamically over time using T2-weighted and T1-weighted

sequences, respectively. These methods provide insights into blood volume and permeability,

offering critical data on neoangiogenesis and microvascular density—key factors in evaluating

tumor aggressiveness and therapeutic response. (Sabeghi et al., 2024) (Tan., 2024) (Paschoal.,

2024)

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Arterial Spin Labeling (ASL) is a non-invasive imaging technique that avoids the use of

contrast agents by utilizing magnetically labeled arterial blood, with water serving as a freely

diffusing tracer to measure cerebral blood flow (CBF). This makes ASL especially advantageous

for patients with renal issues, as it eliminates the risks associated with contrast agents. ASL has

proven effective in monitoring glioma recurrence after radiation therapy, with studies indicating

a strong correlation between DSC and ASL for distinguishing recurrent gliomas from radiation-

induced damage. Despite its promise, MR perfusion imaging faces challenges, particularly with

contrast agent leakage, which can skew relative CBV (rCBV) measurements. Accurate evaluation

requires correction methods to address potential underestimation or overestimation of rCBV

caused by blood-brain barrier disruptions. Ongoing clinical trials and research are focused on

improving these techniques and addressing current limitations in MR perfusion imaging. (Yamin.,

2024) (Moltoni,2024)

Recent advancements in 3D CT combined with AI and other emerging technologies have

already revolutionized medical diagnostics and surgical planning. AI-driven algorithms now

enhance image quality by reducing noise and detecting subtle abnormalities while faster imaging

techniques allow for real-time and high-resolution 3D models. Today’s innovations enable precise

anatomical reconstructions for clinicians while performing complex surgeries like brain tumor

resections. Integration of AI with radiomics from CT scans provides predictive understandings

into tumor characteristics and treatment responses. These advancements have transformed 3D

CT into a powerful tool. Magnetic Resonance Fingerprinting (MRF) is also a groundbreaking

advancement in neuro-oncology and is delivering quantitative evaluations of tissue properties that

enhance tumor characterization and differentiation. MRF employs a distinctive single-sequence

pseudorandomized methodology to swiftly acquire T1 and T2 relaxation times and is facilitating

precise tissue identification and tumor boundary delineation. Its is promising in distinguishing

primary brain tumors from metastatic lesions and in differentiating high-grade gliomas (HGGs)

from low-grade gliomas (LGGs). (Sabeghi et al., 2024) (Ali., 2024) (Kumar., 2024) Magnetic

Resonance Fingerprinting (MRF) has rapidly become a key method for accurately characterizing

various tumor regions including solid tumors (STs), peritumoral white matter (PWM),

contralateral white matter (CWM), and perilesional edema. MRF has potentials in distinguishing

solid tumor areas from CWM by analyzing T1 and T2 relaxation times, for instance, MRF is

effective in differentiating glioblastoma multiforme (GBM) and low-grade gliomas (LGGs)

where it distinctly separates PWM from CWM. T1 values have been more reliable in

distinguishing these regions in LGGs while T2 values show a trend but often lack statistical

significance.

MRF's application extends to the differentiation of IDH-mutant gliomas from IDH-

wildtype gliomas. Studies show IDH-mutant gliomas exhibit higher T1 and T2 relaxation times

within both the solid tumor and the adjacent peritumoral edema regions, expressly within 1 cm of

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the tumor margin. IDH-wildtype tumors are characterized by higher T2 and ADC values in

peritumoral edema regions close to tumor although these differences tend to diminish in edema

regions located more than 1 cm away from the tumor margin. When it comes to distinguishing

high-grade gliomas (HGGs) from low-grade gliomas (LGGs), MRF results varies (Abd-Ellah .,

2024) While some research successfully utilized both T1 and T2 values to differentiate these

tumor grades, other studies primarily observed significant differences in T1 values, with T2 values

approaching but not consistently reaching significance, especially in cases where tumors were

pathologically confirmed. MRF is also valuable for identifying genetic mutations, particularly in

differentiating IDH-mutant gliomas from IDH-wildtype gliomas, with IDH mutants displaying

higher T1 and T2 values. MRF is non-invasive technique in nature and is avoiding radiation

exposure and prolonged procedures, makes it advantageous for pediatric imaging. In children

MRF shows utility by revealing differences in T1 and T2 values between solid tumor and

peritumoral regions compared to CWM, while echoing similar findings in adult populations. MRF

shows great promise for initial tumor characterization and its role in monitoring treatment

response is indeterminate. Current studies have not yet identified changes in T1 or T2 values

between treated and untreated LGG groups and nor in longitudinal assessments pre- and post-

treatment which suggests although MRF is highly effective for initial diagnosis but its ability to

track tumor progression or therapeutic response warrants further exploration. Recent

advancements in the field, such as the integration of MRF with deep learning approaches like the

Deep Reconstruction Network (DRONE) are promising. DRONE enhances MRF by significantly

shortening scan times and providing high-quality, noise-reduced tissue maps, which could

improve the speed and precision of distinguishing metastatic tumors from normal tissue. (Sabeghi

et al., 2024) (Martinez., 2024)

Magnetic Resonance Spectroscopic Imaging (MRSI) is non-invasive metabolic imaging

technique, known for capturing chemical makeup of brain tumors as it works by detecting signals

from hydrogen nuclei. This advanced tool is cutting-edge neuro-oncology for its ability to

differentiate between tumor grades, track treatment responses, and identify specific biomarkers

like 2-hydroxyglutarate which is linked to isocitrate dehydrogenase (IDH) mutations in gliomas.

Measurements of metabolites such as N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr),

MRSI provide guidance about metabolic state of brain tissue, for instance, elevated Cho and

decreased NAA levels are common in high-grade tumors which help distinguish aggressive

tumors from less severe ones. MRSI's ability to differentiate between tumor recurrence and

radiation-induced damage post-treatment using metabolite ratios like Cho/NAA makes it

indispensable for ongoing patient management. Pediatric neuro-oncology is also getting MRSI

benefits as it is effectively diagnosing and monitoring conditions like medulloblastoma and

ependymoma with a diagnostic accuracy reaching up to 98% when using combined echo time

techniques. This imaging modality provides prognostic insights such as using the myo-inositol to

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creatine ratio to predict responses to anti-angiogenic therapies in glioblastoma, so it stands out

MRSI for its precision in capturing the biochemical landscape of brain tumors, aiding in accurate

diagnosis, treatment planning, and long-term monitoring in neuro-oncology. (Sabeghi et al., 2024)

Limitations: Limitations include small sample sizes, we discuss studies with animal

models or few patients which may limit generalizability. Some studies lack detailed statistical

analyses or comprehensive outcome measures may be affecting reliability of findings. Variability

in methodological rigor also impacts the robustness of the conclusions.

CONCLUSION

It is concluded that advanced imaging techniques has enhanced brain tumor management.

Functional MRI (fMRI) and Diffusion Tensor Imaging (DTI) have shown substantial benefits in

reducing morbidity and improving surgical precision by accurately mapping brain function and

fiber tracts. Ultra-High Field MRI (UHF MRI) provides superior resolution and tumor

characterization while PET/CT offers critical metabolic insights refine surgical planning and

predict outcomes. Deuterium Magnetic Resonance Spectroscopy (2H MRS) excels in

differentiating tumor from necrosis, improving treatment decisions. Intraoperative MRI (iMRI)

further enhances resection accuracy and preserves neurological function through real-time

feedback. PET with novel radiotracers, PET/MRI for multiparametric analysis, and AI-enhanced

radiomics and deep learning models have refined tumor characterization and treatment planning.

Emerging techniques like Magnetic Resonance Fingerprinting (MRF) and Spectroscopic Imaging

(MRSI) provides us more detailed metabolic and genetic perceptions distinguishing between

tumor grades and guiding personalized therapies. Continued research aims to fully harness these

technologies transforming neuro-oncology into a more accurate, targeted, and effective field.

Vol. 11/ Núm. 2 2024 pág. 289

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