Quantitative Assessment of Tumor Cell Proliferation in Brain Gliomas with Dynamic Contrast-Enhanced MRI

Dynamic glucose-enhanced MRI of gliomas: A preliminary clinical application

The study aimed to investigate the feasibility of dynamic glucose-enhanced (DGE) MRI technology in the clinical application of glioma. Twenty patients with glioma were examined using a preoperative DGE-MRI protocol before clinical intervention. A brief hyperglycemic state was achieved by injecting 50 mL of 50% w/w D-glucose intravenously during the DGE imaging. The total acquisition time for the DGE was 15 min. Area-under-the-curve (AUC) images were calculated using the DGE images. AUC2-7min values of the glioma core, margin area, edema area, and contralateral brain parenchyma were compared using Mann-Whitney U tests. Overall, gray and white matter areas in the AUC images showed relatively low DGE signal change and bilateral symmetry. However, the tumor cores displayed a significant hyperintensity. A high DGE signal change was also seen in the necrotic, cystic, and cerebrospinal areas. These results show that DGE MRI is a feasible technique for the study of brain tumors as part of a clinical exam. Importantly, DGE MRI showed enhancement in areas confirmed histopathologically as tumors, whereas Gd T1w MRI did not show any enhancement in this area. Since the D-glucose molecule is smaller than Gd-based contrast agents, DGE MRI may be more sensitive to subtle blood-brain barrier disruptions, thus potentially providing early information about possible malignancy. These findings provide a new perspective for the further exploration and analysis of D-glucose uptake in brain tumors.

Distributed parameter model of dynamic contrast-enhanced MRI in the identification of IDH mutation, 1p19q codeletion, and tumor cell proliferation in glioma patients

Objectives

To investigate the clinical value of hemodynamic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting glioma genotypes including isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion status and the tumor proliferation index (Ki-67) noninvasively. And to compare the diagnostic performance of parameters of distributed parameter (DP)model and extended Tofts (Ex-Tofts) model.

Materials and methods

Dynamic contrast-enhanced MRI (DCE-MRI) data of patients with glioma were prospectively enrolled from April 2021 to May 2023. The imaging data were analyzed using DP and Ex-Tofts model for evaluating the perfusion and permeability characteristics of glioma. Comparisons were performed according to IDH genotype in all glioma patients and 1p/19q codeletion in IDH mutation glioma patients. Receiver operating characteristic (ROC) curves were generated for DCE-MRI parameters. The Spearman rank correlation coefficients were calculated between DCE MRI parameters and Ki-67 index.

Results

In IDH-mutation gliomas, a higher blood flow (F) was found in 1p/19q codeletion gliomas than in 1p/19q intact gliomas. No parameter derived from Ex-Tofts model showed significant differences in predicting 1p/19q status. Fractional volume of interstitial space (V e) derived from both the DP and Ex-Tofts models exhibited optimal performance in predicting IDH genotype (AUC = 0.818, 0.828, respectively). V e also showed the highest correlations with Ki-67 LI within their respective models in all gliomas (ρ = 0.62, 0.61), indicating comparable moderate positive associations. Ki-67.

Conclusion

DP model showed a clear advantage in predicting 1p/19q status compared to Ex-Tofts model. The DP and Ex-Tofts models performed similarly in predicting IDH mutation and Ki-67 index.

Ultrahigh-Resolution Visualization of Vascular Heterogeneity in Brain Tumors via Magnetic Nanoparticles-Enhanced Susceptibility-Weighted Imaging

The precise assessment of vascular heterogeneity in brain tumors is vital for diagnosing, grading, predicting progression, and guiding treatment decisions. However, currently, there is a significant shortage of high-resolution imaging approaches. Herein, we propose a contrast-enhanced susceptibility-weighted imaging (CE-SWI) utilizing the minimalist dextran-modified Fe3O4 nanoparticles (Dextran@Fe3O4 NPs) for ultrahigh-resolution mapping of vasculature in brain tumors. The Dextran@Fe3O4 NPs are prepared via a facile coprecipitation method under room temperature, and exhibit small hydrodynamic size (28 nm), good solubility, excellent biocompatibility, and high transverse relaxivity (r2*, 159.7 mM-1 s-1) under 9.4 T magnetic field. The Dextran@Fe3O4 NPs-enhanced SWI can increase the contrast-to-noise ratio (CNR) of cerebral vessels to 2.5 times that before injection and achieves ultrahigh-spatial-resolution visualization of microvessels as small as 0.1 mm in diameter. This advanced imaging capability not only allows for the detailed mapping of both enlarged peritumoral drainage vessels and the intratumoral microvessels, but also facilitates the sensitive imaging detection of vascular permeability deterioration in a C6 cells-bearing rat glioblastoma model. Our proposed Dextran@Fe3O4 NPs-enhanced SWI provides a powerful imaging technique with great clinical translation potential for the precise theranostics of brain tumors.

Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging

Purpose

To investigate the predictive value of multi-parameters derived from advanced MR imaging for Ki-67 labeling index (LI) in glioma patients.

Materials and Methods

One hundred and nine patients with histologically confirmed gliomas were evaluated retrospectively. These patients underwent advanced MR imaging, including dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), MR spectroscopy imaging (MRS), diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), before treatment. Twenty-one parameters were extracted, including the maximum, minimum and mean values of relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative mean transit time (rMTT), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA) and relative mean diffusivity (rMD) respectively, and ration of choline (Cho)/creatine (Cr), Cho/N-acetylaspartate (NAA) and NAA/Cr. Stepwise multivariate regression was performed to build multivariate models to predict Ki-67 LI. Pearson correlation analysis was used to investigate the correlation between imaging parameters and the grade of glioma. One-way analysis of variance (ANOVA) was used to explore the differences of the imaging parameters among the gliomas of grade II, III, and IV.

Results

The multivariate regression showed that the model of five parameters, including rCBVmax (RC=0.282), rCBFmax (RC=0.151), rADCmin (RC= -0.14), rFAmax (RC=0.325) and Cho/Cr ratio (RC=0.157) predicted the Ki-67 LI with a root mean square (RMS) error of 0. 0679 (R2 = 0.8025).The regression check of this model showed that there were no multicollinearity problem (variance inflation factor: rCBVmax, 3.22; rCBFmax, 3.14; rADCmin, 1.96; rFAmax, 2.51; Cho/Cr ratio, 1.64), and the functional form of this model was appropriate (F test: p=0.682). The results of Pearson correlation analysis showed that the rCBVmax, rCBFmax, rFAmax, the ratio of Cho/Cr and Cho/NAA were positively correlated with Ki-67 LI and the grade of glioma, while the rADCmin and rMDmin were negatively correlated with Ki-67 LI and the grade of glioma.

Conclusion

Combining multiple parameters derived from DSC, DTI, DWI and MRS can precisely predict the Ki-67 LI in glioma patients.

MR Perfusion Imaging for Gliomas

Accurate diagnosis and treatment evaluation of patients with gliomas is imperative to make clinical decisions. Multiparametric MR perfusion imaging reveals physiologic features of gliomas that can help classify them according to their histologic and molecular features as well as distinguish them from other neoplastic and nonneoplastic entities. It is also helpful in distinguishing tumor recurrence or progression from radiation necrosis, pseudoprogression, and pseudoresponse, which is difficult with conventional MR imaging. This review provides an update on MR perfusion imaging for the diagnosis and treatment monitoring of patients with gliomas following standard-of-care chemoradiation therapy and other treatment regimens such as immunotherapy.

Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI

OBJECTIVE

Noninvasive detection of molecular status of astrocytoma is of great clinical significance for predicting therapeutic response and prognosis. We aimed to evaluate whether morphological MRI (mMRI), SWI, DWI, and DSC-PWI could predict Ki-67 labeling index (LI), ATRX mutation, and MGMT promoter methylation status in IDH mutant (IDH-mut) astrocytoma.

METHODS

We retrospectively analyzed mMRI, SWI, DWI, and DSC-PWI in 136 patients with IDH-mut astrocytoma.The features of mMRI and intratumoral susceptibility signals (ITSS) were compared using Fisher exact test or chi-square tests. Wilcoxon rank sum test was used to compare the minimum ADC (ADCmin), and minimum relative ADC (rADCmin) of IDH-mut astrocytoma in different molecular markers status. Mann-Whitney U test was used to compare the rCBVmax of IDH-mut astrocytoma with different molecular markers status. Receiver operating characteristic curves was performed to evaluate their diagnostic performances.

RESULTS

ITSS, ADCmin, rADCmin, and rCBVmax were significantly different between high and low Ki-67 LI groups. ITSS, ADCmin, and rADCmin were significantly different between ATRX mutant and wild-type groups. Necrosis, edema, enhancement, and margin pattern were significantly different between low and high Ki-67 LI groups. Peritumoral edema was significantly different between ATRX mutant and wild-type groups. Grade 3 IDH-mut astrocytoma with unmethylated MGMT promoter was more likely to show enhancement compared to the methylated group.

CONCLUSIONS

mMRI, SWI, DWI, and DSC-PWI were shown to have the potential to predict Ki-67 LI and ATRX mutation status in IDH-mut astrocytoma. A combination of mMRI and SWI may improve diagnostic performance for predicting Ki-67 LI and ATRX mutation status.

CLINICAL RELEVANCE STATEMENT

Conventional MRI and functional MRI (SWI, DWI, and DSC-PWI) can predict Ki-67 expression and ATRX mutation status of IDH mutant astrocytoma, which may help clinicians determine personalized treatment plans and predict patient outcomes.

KEY POINTS

• A combination of multimodal MRI may improve the diagnostic performance to predict Ki-67 LI and ATRX mutation status. • Compared with IDH-mutant astrocytoma with low Ki-67 LI, IDH-mutant astrocytoma with high Ki-67 LI was more likely to show necrosis, edema, enhancement, poorly defined margin, higher ITSS levels, lower ADC, and higher rCBV. • ATRX wild-type IDH-mutant astrocytoma was more likely to show edema, higher ITSS levels, and lower ADC compared to ATRX mutant IDH-mutant astrocytoma.

Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy

PURPOSE

To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis.

METHODS

Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (K^trans), volume of extravascular-extracellular space (v_e), fractional plasma volume (f_pv), and reflux transfer rate (k_ep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve.

RESULTS

Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in K^trans and v_e were observed between grades except v_e between grade 2 and 3. K^trans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). V_e indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively).

CONCLUSION

Our study had identified K^trans, v_e and the combination of parameters to be an accurate predictor for grading glioma.

Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases

Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis

BACKGROUND/AIM

To provide data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC).

PATIENTS AND METHODS

Data from 4 Centers (200 female patients, mean age, 51.2±11.5 years) were acquired. The following data were collected: histopathological diagnosis, tumor grade, stage, hormone receptor status, KI 67, and DCE MRI values including Ktrans (volume transfer constant), Ve (volume of the extravascular extracellular leakage space (EES) and Kep (diffusion of contrast medium from the EES back to the plasma). DCE MRI values between different groups were compared using the Mann-Whitney U-test and by the Kruskal-Wallis H test. The association between DCE MRI and Ki 67 values was calculated by the Spearman's rank correlation coefficient.

RESULTS

DCE MRI values of different tumor subtypes overlapped significantly. There were no statistically significant differences of DCE MRI values between different tumor grades. All DCE MRI parameters correlated with KI-67: Ktrans, r=0.44, p=0.0001; Ve, r=0.34, p=0.0001; Kep, r=0.28, p=0.002. ROC analysis identified a Ktrans threshold of 0.3 min-1 for discrimination of tumors with low KI-67 expression (<25%) and high KI-67 expression (≥25%): sensitivity, 75.5%, specificity, 73.0%, accuracy, 74.0%, AUC, 0.78. DCE MRI values overlapped between tumors with different T and N stages.

CONCLUSION

Ktrans, Kep, and Ve cannot be used as reliable a surrogate marker for hormone receptor status, tumor stage and grade in BC. Ktrans may discriminate lesions with high and lower proliferation activity.

Dynamic contrast-enhanced magnetic resonance imaging for differentiating head and neck paraganglioma and schwannoma

BACKGROUND AND PURPOSE

Morphological assessment with conventional magnetic resonance imaging (MRI) sequences has limited specificity to distinguish between paragangliomas and schwannomas. Assessing the differences in microvascular properties through pharmacokinetic parameters of dynamic contrast-enhanced (DCE)-MRI can provide additional information to aid in this differentiation.

MATERIALS AND METHODS

A prospective study on MR characterization of neck masses was performed between January 2017 and March 2019 in our department, out of which 40 patients with head and neck paragangliomas (HNPGLs) (33 lesions) and schwannomas (15 lesions) were included in this analysis. MR perfusion using dynamic axial T1WI fat suppressed fast spoiled gradient recalled sequence with parallel imaging was performed in all the patients, in addition to single-shot turbo spin-echo axial diffusion weighted imaging (DWI) and routine MRI. ROI-based method was used to obtain signal-time curves, permeability measurements, and mean apparent diffusion coefficient (ADC) to differentiate paragangliomas from schwannomas. Statistical analysis was done to assess the significance and establish a cutoff to distinguish between the two entities. The available images of DOTANOC PET/CT (34 lesions) were analyzed retrospectively. Correlations between the perfusion, diffusion, and molecular PET/CT parameters were done.

RESULTS

Paragangliomas had a higher wash-in rate, wash-out rate, Ktrans, Kep , and Vp (p < 0.001); while schwannomas had a higher relative enhancement (p < 0.012), time to peak, time of onset, brevity of enhancement, and Ve (p < 0.001). Among the perfusion parameters, Kep (area under curve (AUC) 0.994) and Vp (AUC 0.992) were found to have the highest diagnostic value. In diffusion-weighted imaging, paragangliomas had a lower mean ADC compared to schwannomas (p < 0.001). The SUVmax and SUVmean were significantly associated with Ktrans , Kep , and Vp in paragangliomas.

CONCLUSION

DCE-MRI in addition to DWI-MRI can accurately distinguish HNPGL from schwannoma and may replace the need for any additional imaging and preoperative biopsy in most cases.

Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

PURPOSE

We aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients.

METHODS

This retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve.

RESULTS

The radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively.

CONCLUSION

Both the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.

Quantitative Imaging Parameters of Contrast-Enhanced Micro-Computed Tomography Correlate with Angiogenesis and Necrosis in a Subcutaneous C6 Glioma Model

The aim of this work was to systematically obtain quantitative imaging parameters with static and dynamic contrast-enhanced (CE) X-ray imaging techniques and to evaluate their correlation with histological biomarkers of angiogenesis in a subcutaneous C6 glioma model. Enhancement (E), iodine concentration (CI), and relative blood volume (rBV) were quantified from single- and dual-energy (SE and DE, respectively) micro-computed tomography (micro-CT) images, while rBV and volume transfer constant (Ktrans) were quantified from dynamic contrast-enhanced (DCE) planar images. CI and rBV allowed a better discernment of tumor regions from muscle than E in SE and DE images, while no significant differences were found for rBV and Ktrans in DCE images. An agreement was found in rBV for muscle quantified with the different imaging protocols, and in CI and E quantified with SE and DE protocols. Significant strong correlations (Pearson r > 0.7, p < 0.05) were found between a set of imaging parameters in SE images and histological biomarkers: E and CI in tumor periphery were associated with microvessel density (MVD) and necrosis, E and CI in the complete tumor with MVD, and rBV in the tumor periphery with MVD. In conclusion, quantitative imaging parameters obtained in SE micro-CT images could be used to characterize angiogenesis and necrosis in the subcutaneous C6 glioma model.

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Purpose

The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test.

Methods

DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV).

Results

The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of K^trans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable.

Conclusions

The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.