3D-ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location-matched basis

DKI can distinguish high-grade gliomas from IDH1-mutant low-grade gliomas and correlate with their different nuclear-to-cytoplasm ratio: a localized biopsy-based study

OBJECTIVES

To explore whether differences in diffusional kurtosis imaging (DKI) between therapy-naïve high-grade gliomas (HGGs) and low-grade gliomas (LGGs) are related to the cellularity and/or the nuclear-to-cytoplasmic (N/C) ratio.

METHODS

We analyzed 44 and 40 diffuse glioma samples that were pathologically confirmed as HGGs and IDH1-mutant LGGs, respectively. The DKI parameters included kurtosis metrics (mean kurtosis [MK], axial kurtosis [K//], and radial kurtosis [K⊥]), and the diffusional metrics (fractional anisotropy [FA], mean diffusion [MD], axial diffusion [λ//], and radial diffusion [λ⊥]). The cellularity and the N/C ratio were compared within LGGs and HGGs using the Mann-Whitney U test (significant level, p < 0.007 [0.05/7]); Bonferroni correction). Spearman's correlation analysis was used to calculate the correlation coefficients among DKI metrics, cellularity, and the N/C ratio at a significant level of p = 0.05.

RESULTS

Excluding FA, all DKI metrics showed significant differences between HGGs and LGGs (all p ≤ 0.001). The N/C ratio of HGGs was significantly higher than that of LGGs; however, differences in cellularity were not significant between the two glioma groups (p = 0.525). Similarly, excluding FA, all DKI metrics were significantly correlated with the N/C ratio in LGGs, with correlation coefficients of - 0.365 (MD), - 0.313 (λ//), - 0.376 (λ⊥), 0.859 (MK), 0.772 (K//), and 0.842 (K//). There was a non-significant correlation between any DKI parameters and the cellularity in LGGs. Additionally, the cellularity and N/C ratios in HGGs did not correlate with any DKI metrics.

CONCLUSIONS

DKI differentiate LGGs from HGGs associated with their different N/C ratios.

CLINICAL RELEVANCE STATEMENT

This study shows that DKI differentiates LGGs from HGGs may correlated with their different N/C ratios, this could provide a possible histopathological mechanism about why DKI can DKI differentiate LGGs from HGGs.

KEY POINTS

• Excluding FA, all DKI metrics showed a significant difference between high-grade gliomas and IDH1-mutant low-grade gliomas. • The nuclear-to-cytoplasm ratios in high-grade gliomas were significantly more extensive than that in IDH1-mutant low-grade gliomas, but not the cellularity. • Significant associations were seen between DKI measures and the N/C ratio; a non-significant correlation was noted between any DKI metric and cellularity in glioma specimens.

CLINICAL APPLICATION STUDY OF 3D-ASL PERFUSION IMAGING AND MAGNETIC RESONANCE DIFFUSION IMAGING IN TRANSIENT ISCHEMIC ATTACK

ABSTRACT

Objective: This study aimed to explore the clinical application of three-dimensional arterial spin labeling (3D-ASL) and diffusion-weighted magnetic resonance imaging (DWI) in transient ischemic attacks. Methods: Forty patients with transient cerebral ischemia in our hospital were selected and included from July 2020 to March 2022. All subjects were detected by DWI and 3D-ASL technology. The positive rate, relative cerebral blood flow (rCBF), and the receiver operating characteristic curve of the two methods in the diagnosis of transient cerebral ischemia were compared; the objective was to compare the relationship between the frequency of transient ischemic attack and hypoperfusion, and vascular stenosis. Results: The 3D-ASL examination showed two cases of hypoperfusion in the healthy control group (5.00), and the magnetic resonance imaging examination showed four cases of vascular stenosis in the healthy control group (10.00). The rCBF ratio in the cerebral ischemia group was significantly lower than that in the cerebral ischemia group, which was significantly lower than that in the healthy control group ( P < 0.05). The area under the curve (AUC) of 3D-ASL in the diagnosis of transient cerebral ischemia was 0.800, and the AUC of DWI in the diagnosis of transient cerebral ischemia was 0.725. The AUC of the combination of the two methods in transient cerebral ischemia was 0.850. There was a significant difference in the attack frequency of patients with transient cerebral ischemia with different perfusion ( P < 0.05). There was a significant difference in attack frequency between patients with transient ischemic attack and patients without vascular stenosis ( P < 0.05). Conclusion: 3D-ASL and DWI technology have higher diagnostic efficiency for transient cerebral ischemia.

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma and supratentorial glioblastoma

Objective

To develop a machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma (STEE) and supratentorial glioblastoma (GBM).

Methods

We conducted a retrospective analysis on MRI datasets obtained from 140 patients who were diagnosed with STEE (n=48) and GBM (n=92) from two institutions. Initially, we compared seven different machine learning algorithms to determine the most suitable signature (rad-score). Subsequently, univariate and multivariate logistic regression analyses were performed to identify significant clinical predictors that can differentiate between STEE and GBM. Finally, we developed a nomogram by visualizing the rad-score and clinical features for clinical evaluation.

Results

The TreeBagger (TB) outperformed the other six algorithms, yielding the best diagnostic efficacy in differentiating STEE from GBM, with area under the curve (AUC) values of 0.735 (95% CI: 0.625-0.845) and 0.796 (95% CI: 0.644-0.949) in the training set and test set. Furthermore, the nomogram incorporating both the rad-score and clinical variables demonstrated a robust predictive performance with an accuracy of 0.787 in the training set and 0.832 in the test set.

Conclusion

The nomogram could serve as a valuable tool for non-invasively discriminating between STEE and GBM.

Potent in vivo efficacy of oral gallium maltolate in treatment-resistant glioblastoma

Background

Treatment-resistant glioblastoma (trGBM) is an aggressive brain tumor with a dismal prognosis, underscoring the need for better treatment options. Emerging data indicate that trGBM iron metabolism is an attractive therapeutic target. The novel iron mimetic, gallium maltolate (GaM), inhibits mitochondrial function via iron-dependent and -independent pathways.

Methods

In vitro irradiated adult GBM U-87 MG cells were tested for cell viability and allowed to reach confluence prior to stereotactic implantation into the right striatum of male and female athymic rats. Advanced MRI at 9.4T was carried out weekly starting two weeks after implantation. Daily oral GaM (50mg/kg) or vehicle were provided on tumor confirmation. Longitudinal MRI parameters were processed for enhancing tumor ROIs in OsiriX 8.5.1 (lite) with Imaging Biometrics Software (Imaging Biometrics LLC). Statistical analyses included Cox proportional hazards regression models, Kaplan-Meier survival plots, linear mixed model comparisons, and t-statistic for slopes comparison as indicator of tumor growth rate.

Results

In this study we demonstrate non-invasively, using longitudinal MRI surveillance, the potent antineoplastic effects of GaM in a novel rat xenograft model of trGBM, as evidenced by extended suppression of tumor growth (23.56 mm3/week untreated, 5.76 mm3/week treated, P < 0.001), a blunting of tumor perfusion, and a significant survival benefit (median overall survival: 30 days untreated, 56 days treated; P < 0.001). The therapeutic effect was confirmed histologically by the presence of abundant cytotoxic cellular swelling, a significant reduction in proliferation markers (P < 0.01), and vessel normalization characterized by prominent vessel pruning, loss of branching, and uniformity of vessel lumina. Xenograft tumors in the treatment group were further characterized by an absence of an invasive edge and a significant reduction in both, MIB-1% and mitotic index (P < 0.01 each). Transferrin receptor and ferroportin expression in GaM-treated tumors illustrated cellular iron deprivation. Additionally, treatment with GaM decreased the expression of pro-angiogenic markers (von Willebrand Factor and VEGF) and increased the expression of anti-angiogenic markers, such as Angiopoietin-2.

Conclusion

Monotherapy with the iron-mimetic GaM profoundly inhibits trGBM growth and significantly extends disease-specific survival in vivo.

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics - Methodological Overview and Use Cases

BACKGROUND

Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF).

METHOD

This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine).

RESULTS

ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels.

CONCLUSION

Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring.

KEY POINTS

· ASL is an MRI technique that uses labeled blood-water as an endogenous tracer for brain perfusion imaging.. · It allows the assessment of CBF without the need for administration of a gadolinium-based contrast agent.. · CBF quantification by ASL has been used in several pathologies including brain tumors or neurodegenerative diseases.. · Vessel-selective ASL methods can provide brain perfusion territory mapping in cerebrovascular diseases.. · ASL may be of particular interest in patient cohorts with caveats concerning gadolinium administration..

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma

OBJECTIVES

This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM).

METHODS

We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression. The machine learning models were established with the maximum relevance and minimum redundancy algorithm and the extreme gradient boosting classifier. The area under the receiver operating curve (AUC) and accuracy were calculated for the training and validation sets.

RESULTS

Positive VEGF in GBM was 63.1% (137/217), with a high expression ratio of 53.3% (73/137). To predict the positive and negative VEGF expression, 7 radiomic features were selected, with 3 features from T1CE and 4 from T2WI. The accuracy and AUC were 0.83 and 0.81, respectively, in the training set and were 0.73 and 0.74, respectively, in the validation set. To predict high and low levels, 7 radiomic features were selected, with 2 from T1CE, 1 from T2WI, and 4 from the data combinations of T1CE and T2WI. The accuracy and AUC were 0.88 and 0.88, respectively, in the training set and were 0.72 and 0.72, respectively, in the validation set.

CONCLUSION

The VEGF expression status in GBM can be predicted using a machine learning model. Radiomic features resulting from data combinations of different MRI sequences could be helpful.

The role of angiogenic growth factors in the immune microenvironment of glioma

Angiogenic growth factors (AGFs) are a class of secreted cytokines related to angiogenesis that mainly include vascular endothelial growth factors (VEGFs), stromal-derived factor-1 (SDF-1), platelet-derived growth factors (PDGFs), fibroblast growth factors (FGFs), transforming growth factor-beta (TGF-β) and angiopoietins (ANGs). Accumulating evidence indicates that the role of AGFs is not only limited to tumor angiogenesis but also participating in tumor progression by other mechanisms that go beyond their angiogenic role. AGFs were shown to be upregulated in the glioma microenvironment characterized by extensive angiogenesis and high immunosuppression. AGFs produced by tumor and stromal cells can exert an immunomodulatory role in the glioma microenvironment by interacting with immune cells. This review aims to sum up the interactions among AGFs, immune cells and cancer cells with a particular emphasis on glioma and tries to provide new perspectives for understanding the glioma immune microenvironment and in-depth explorations for anti-glioma therapy.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study

OBJECTIVES

To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas.

METHODS

Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000 s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades.

RESULTS

Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r =  - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006).

CONCLUSIONS

IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas.

KEY POINTS

• Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy

Background

Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones.

Methods

We used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival.

Results

We constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment.

Conclusion

Using the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

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.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Utility of arterial spin labeling for objective assessment of intratumoral microvessels in diffuse hemispheric glioma, H3 G34R-mutant: A case report and literature review

Imaging findings of diffuse hemispheric glioma H3 G34-mutant (DHG, H3 G34m), a new variant of glioma under the World Health Organization classification, have recently been vigorously debated. Here, we report a case of DHG, H3 G34m in which objective assessments of intratumoral microvessels using arterial spin labeling (ASL) were useful for preoperative diagnosis, selection of anti-tumor drugs, and tracking therapeutic responses. The patient was a 34-year-old woman who presented with weakness in the left arm. Preoperative magnetic resonance imaging (MRI) showed no specific findings of hyperintensity on fluid-attenuated inversion recovery imaging and faint enhancement on T1-weighted imaging with contrast media in the tumor. However, ASL showed a convincing finding of high blood flow in the entire tumor, allowing identification of the tumor as malignant glioma. Tumor specimens obtained from biopsy showed that the tumor comprised low-differentiated tumor cells, abundant histiocytes, and highly dense microvessels. Immunohistochemical findings such as positive findings for H3 G34R and p53, and negative findings for IDH-1, ATRX, and OLIG2 led to the diagnosis of DHG, H3 G34m. Based on findings of hyperperfusion on ASL and detection of vascular endothelial growth factor (VEGF), we administered the anti-VEGF antibody bevacizumab. The tumor shrank significantly but remained. However, the residual tumor showed hypoperfusion on ASL, strongly suggesting tumor remission. Objective assessments of blood flow using ASL are useful in clinical practice for patients with DHG, H3 G34 showing non-specific findings on conventional MRI.

Noninvasively evaluating the grade and IDH mutation status of gliomas by using mono-exponential, bi-exponential diffusion-weighted imaging and three-dimensional pseudo-continuous arterial spin labeling

OBJECTIVES

To noninvasively assess the diagnostic performance of diffusion-weighted imaging (DWI), bi-exponential intravoxel incoherent motion imaging (IVIM) and three-dimensional pseudo-continuous arterial spin labeling (3D pCASL) in differentiating lower-grade gliomas (LGGs) from high-grade gliomas (HGGs), and predicting the isocitrate dehydrogenase (IDH) mutation status.

MATERIALS AND METHODS

Ninety-five patients with pathologically confirmed grade 2-4 gliomas with preoperative DWI, IVIM and 3D pCASL were enrolled in this study. The Student's t test and Mann-Whitney U test were used to evaluate differences in parameters of DWI, IVIM and 3D pCASL between LGG and HGG as well as between mutant and wild-type IDH in grade 2 and 3 diffusion astrocytoma; receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance.

RESULTS

The value of ADC_mean, ADC_min, D_mean and D_min in HGGs were lower than in LGGs, while the value of CBF_mean and CBF_max in HGGs were higher than in LGGs. In ROC analysis, the AUC values of D_mean, D_min and CBF_max were 0.827, 0.878 and 0.839, respectively. The combination of CBF_max and D_min displayed the highest diagnostic performance to distinguish LGGs from HGGs, with AUC 0.906, sensitivity 82.4 %, and specificity 86.4 %. In grades 2 and 3 diffusion astrocytoma patients, ADC_min, D_mean, D_min, CBF_mean and CBF_max showed significant differences between IDH_mut and IDH_wt group (p < 0.05, 0.001, 0.001, 0.01 and 0.001, respectively) and the AUC values were 0. 709, 0.849, 0.919, 0.755 and 0.873, respectively. Similarly, the combination of CBF_max and D_min demonstrated the highest AUC value (0.938) in prediction IDH mutation status, with sensitivity 92.9 %, and specificity 95.5 %.

CONCLUSION

The combination of IVIM and 3D pCASL can be used in prediction histologic grade and IDH mutation status of glioma noninvasively.

Three-dimensional arterial spin labeling-guided dose painting radiotherapy for non-enhancing low-grade gliomas

PURPOSE

To investigate the feasibility and dosimetric characteristics of dose painting for non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL).

MATERIALS AND METHODS

Eighteen patients with NE-LGGs were enrolled. 3D-ASL, T2 fluid-attenuated inversion recovery (T2 Flair) and contrast-enhanced T1-weighted magnetic resonance images were obtained. The gross tumor volume (GTV) was delineated on the T2 Flair. The hyper-perfusion region of the GTV (GTV-ASL) was determined by 3D-ASL, and the GTV-SUB was obtained by subtracting the GTV-ASL from the GTV. The clinical target volume (CTV) was created by iso-tropically expanding the GTV by 1 cm. The planning target volume (PTV), PTV-ASL were obtained by expanding the external margins of the CTV, GTV-ASL, respectively. PTV-SUB was generated by subtracting PTV-ASL from PTV. Three plans were generated for each patient: a conventional plan (plan 1) without dose escalation delivering 95-110% of 45-60 Gy in 1.8-2 Gy fractions to the PTV and two dose-painting plans (plan 2 and plan 3) with dose escalating by 10-20% (range, 50-72 Gy) to the PTV-ASL based on plan 1. The plan 3 was obtained from plan 2 without the maximum dose constraint. The dosimetric differences among the three plans were compared.

RESULTS

The volume ratio of the PTV-ASL to the PTV was (23.49 ± 11.94)% (Z = - 3.724, P = 0.000). Compared with plan 1, D_2%, D_98% and D_mean of PTV-ASL increased by 14.67%,16.17% and 14.31% in plan2 and 19.84%,15.52% and 14.27% in plan3, respectively (P < 0.05); the D_2% of the PTV and PTV-SUB increased by 11.89% and 8.34% in plan 2, 15.89% and 8.49% in plan 3, respectively (P < 0.05). The PTV coverages were comparable among the three plans (P > 0.05). In plan 2 and plan 3, the conformity indexes decreased by 18.60% and 12.79%; while the homogeneity index increased by 1.43 and 2 times (P < 0.05). Compared with plan 1, the D_0.1 cc of brain stem and D_max of optic chiasma were slightly increased in plan 2 and plan 3, and the absolute doses met the dose constraint. The doses of the other organs at risk (OARs) were similar among the three plans (P > 0.05).

CONCLUSION

The dose delivered to hyper-perfusion volume derived from 3D-ASL can increased by 10-20% while respecting the constraints to the OARs for NE-LGGs, which provides a basis for future individualized and precise radiotherapy, especially if the contrast agent cannot be injected or when contrast enhancement is uncertain.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study

Background

To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas.

Methods

Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC₁₅₀₀, α₁₅₀₀ fitted by 15 b-values (0-1,500 sec/mm²)and DDC₅₀₀₀ and α₅₀₀₀ fitted by 22 b-values (0-5,000 sec/mm²) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters.

Results

MDWI-derived α₁₅₀₀ negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC₁₅₀₀ and DDC₅₀₀₀ negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α₁₅₀₀(r=-0.485; P=0.005) and α₅₀₀₀(r=-0.395; P=0.025).

Conclusions

SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

Arterial spin labeling of nasopharyngeal carcinoma shows early therapy response

OBJECTIVE

This study aimed to determine the value of arterial spin labeling (ASL) perfusion imaging in assessing the early efficacy of chemoradiotherapy for nasopharyngeal carcinoma (NPC).

METHODS

Fifty-five patients with locoregionally advanced NPC underwent conventional 3.0-T magnetic resonance imaging (MRI) and ASL before and after chemoradiotherapy (prescribed dose reached 40 Gy). Based on the response evaluation criteria for solid tumors (RECIST 1.1), the patients were divided into the partial response and stable disease groups. MRI re-examination was performed one month after chemoradiotherapy completion, and patients were divided into residual and non-residual groups. We investigated inter-group differences in ASL-based tumor blood flow (TBF) parameters (pre-treatment tumor blood flow, post-treatment tumor blood flow, and changes in tumor blood flow, i.e., Pre-TBF, Post-TBF, ΔTBF), correlation between TBF parameters and tumor atrophy rate, and value of TBF parameters in predicting sensitivity to chemoradiotherapy.

RESULTS

There were differences in Pre-TBF, Post-TBF, and ΔTBF between the partial response and stable disease groups (p < 0.01). There were also differences in Pre-TBF and ΔTBF between the residual and non-residual groups (p < 0.01). Pre-TBF and ΔTBF were significantly correlated with the tumor atrophy rate; the correlation coefficients were 0.677 and 0.567, respectively (p < 0.01). Pre-TBF had high diagnostic efficacies in predicting sensitivity to chemoradiotherapy and residual tumors, with areas under the curve of 0.845 and 0.831, respectively.

CONCLUSION

ASL permits a noninvasive approach to predicting the early efficacy of chemoradiotherapy for NPC.

Three-Dimensional Arterial Spin Labeling-Guided Sub-Volume Segmentation of Radiotherapy in Adult Non-Enhancing Low-Grade Gliomas

Objective

The present study aimed to evaluate the feasibility of sub-volume segmentation for radiotherapy planning of adult non-enhancing low-grade gliomas (NE-LGGs) guided by three-dimensional arterial spin labeling (3D-ASL). The differences in high- and low-perfusion areas of NE-LGGs were analyzed using multi-sequence magnetic resonance imaging (MRI) radiomics.

Methods

Fifteen adult patients with NE-LGGs were included in the study. MR images, including T1-weighted imaging (T1WI), T2 Propeller, T2 fluid-attenuated inversion recovery (T2 Flair), 3D-ASL, and contrast-enhanced T1WI (CE-T1WI), were obtained. The gross tumor volume (GTV) was delineated according to the hyperintensity on T2 Flair. The GTV was divided into high- and low-perfusion areas, namely GTV-ASL and GTV-SUB, respectively, based on the differences in cerebral blood flow (CBF) value. The volumes and CBF values of high- and low-perfusion areas were measured and compared. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal features of all MR maps. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic accuracy of the absolute CBF_mean (aCBF_mean), relative CBF_mean (rCBF_mean, normalized by the CBF value of the normal gray matter), and screened features in differentiating high- and low-perfusion areas.

Results

Among the enrolled patients, three (20%) patients with NE-LGGs showed focal intra- and post-radiotherapy contrast enhancement within a prior high-perfusion area of 3D-ASL. The volume ratio of the GTV-ASL to the GTV was (37.08% ± 17.88)% (46.26 ± 44.51 vs. 167.46 ± 209.64 cm³, P = 0.000). The CBF_mean in the high-perfusion area was approximately two times of that in the edema area or normal gray matter (66.98 ± 18.03 vs. 35.19 ± 7.75 or 33.92 ± 8.48 ml/100g/min, P = 0.000). Thirteen features were screened, seven of which were extracted from 3D-ASL. The area undercurve (AUC) values of aCBF_mean, rCBF_mean, and firstorder_10Percentile from 3D-ASL were more than 0.9, of which firstorder_10Percentile was the highest. Their cut-off values were 44.16 ml/100 g/min, 1.49 and 31, respectively.

Conclusion

The difference in blood perfusion in the GTV can be quantified and analyzed based on 3D-ASL images for NE-LGGs, which could guide the sub-volume segmentation of the GTV. 3D-ASL should become a routine method for NE-LGGs during simulation and radiotherapy.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

MGMT promoter methylation status shows no effect on [18F]FET uptake and CBF in gliomas: a stereotactic image-based histological validation study

OBJECTIVES

To investigate the effects of O⁶-methylguanine DNA methyltransferase (MGMT) promoter methylation status of gliomas on O-(2-¹⁸F-fluoroethyl)-L-tyrosine ([¹⁸F]FET) uptake and cerebral blood flow (CBF) of arterial spin labeling (ASL), evaluated by hybrid PET/MR. Stereotactic biopsy was used to validate the findings.

METHODS

A set of whole tumor and reference volumes of interest (VOIs) based on PET/FLAIR imaging were delineated and transferred to the corresponding [¹⁸F]FET PET and CBF maps in 57 patients with newly diagnosed gliomas. The mean and max tumor-to-brain ratio (TBR) and normalized CBF (nCBF) were calculated. The predictive efficacy of [¹⁸F]FET PET and CBF in determining MGMT promoter methylation status of glioma were evaluated by whole tumor analysis and stereotactic biopsy. The correlation between PET/MR parameters and MGMT promoter methylation were analyzed using histological specimens acquired from multiple stereotactic biopsies.

RESULTS

Based on the analysis of whole tumor volume and biopsy site, TBRmean, TBRmax, nCBFmean, and nCBFmax showed no statistically significant differences between gliomas with and without MGMT promoter methylation (all p > 0.05). Furthermore, stereotactic biopsy demonstrated that TBRmean, TBRmax, nCBFmean, and nCBFmax showed no correlation with MGMT promoter methylation (r = -0.117, p = 0.579; r = -0.161, p = 0.443; r = -0.271, p = 0.191; r = -0.300, p = 0.145; respectively).

CONCLUSIONS

MGMT promoter methylation status shows no effect on [¹⁸F]FET uptake and CBF of ASL in gliomas. Stereotactic biopsy validates it and further reveals there is no correlation of [¹⁸F]FET PET uptake and CBF with the percentages of MGMT promoter methylation.

KEY POINTS

• Based on whole tumor VOI assessment, MGMT promoter methylation status shows no effect on [¹⁸F]FET uptake and CBF of ASL in gliomas. • For WHO grade IV glioblastomas, [¹⁸F]FET PET and ASL parameters based on hybrid PET/MR fail to predict the MGMT promoter methylation status. • Stereotactic image-based histology reveals that there is no correlation of [¹⁸F]FET PET uptake and CBF with the status and percentages of MGMT promoter methylation in gliomas.

Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge

OBJECTIVES

Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO₂) and oxygen (O₂) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma.

MATERIALS AND METHODS

Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO₂ and O₂ standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) - tumor, necrosis and edema] to analyze their tissue response patterns.

RESULTS

Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs.

CONCLUSIONS

Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO₂ and O₂ modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.

A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas

PURPOSE

To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas.

METHOD

One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models.

RESULTS

For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919).

CONCLUSIONS

The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.

Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy

OBJECTIVE

To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy.

METHODS

A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test.

RESULTS

Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation.

CONCLUSIONS

Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.

Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI

Objectives

To investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

Chinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (K^trans) and volume of extravascular extracellular space per unit volume of tissue (V_e) derived from DCE-MRI were obtained. Histogram parameters of K^trans, V_e and VEGF expression of IDH1 mutant type (IDH1^mut) gliomas were compared with the IDH1 wildtype (IDH1^wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1^mut from IDH1^wt gliomas. The correlation coefficients were determined between histogram parameters of K^trans, V_e and VEGF expression in gliomas.

Results

In CGGA database, VEGF expression in IDH^mut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of K^transand V_e [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1^mutand IDH1^wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of K^trans (0.019 min⁻¹) and V_e (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1^mutfrom IDH1^wt. Irrespective of IDH1 mutation, histogram parameters of K^transand V_e were correlated with VEGF expression in gliomas (P < 0.05, respectively).

Conclusions

VEGF expression is significantly lower in IDH1^mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.