Amino acid PET vs. RANO MRI for prediction of overall survival in patients with recurrent high grade glioma under bevacizumab therapy

PURPOSE

To summarize evidence on the comparative value of amino acid (AA) PET and conventional MRI for prediction of overall survival (OS) in patients with recurrent high grade glioma (rHGG) under bevacizumab therapy.

METHODS

Medical databases were screened for studies with individual data on OS, follow-up MRI, and PET findings in the same patient. MRI images were assessed according to the RANO criteria. A receiver operating characteristic curve analysis was used to predict OS at 9 months.

RESULTS

Five studies with a total of 72 patients were included. Median OS was significantly lower in the PET-positive than in the PET-negative group. PET findings predicted OS with a pooled sensitivity and specificity of 76% and 71%, respectively. Corresponding values for MRI were 32% and 82%. Area under the curve and sensitivity were significantly higher for PET than for MRI.

CONCLUSION

For monitoring of patients with rHGG under bevacizumab therapy, AA-PET should be preferred over RANO MRI.

Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM

This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet backbones with atrous convolution in DeepLabV3+ structure. The Grad-CAM tool was also used to interpret the effect of augmentation and network optimization on segmentation performance. A wide investigation was performed to optimize the network hyperparameters. In addition, the study examined 35 different models to evaluate different data augmentation techniques. The results of the study indicated that incorporating data augmentation techniques and optimization can improve the performance of segmenting brain LGG tumors up to 10%. Our extensive investigation of the data augmentation techniques indicated that enlargement of data from 90° and 225° rotated data,up to down and left to right flipping are the most effective techniques. MobilenetV2 as the backbone,"Focal Loss" as the loss function and "Adam" as the optimizer showed the superior results. The optimal model (DLG-Net) achieved an overall accuracy of 96.1% with a loss value of 0.006. Specifically, the segmentation performance for Whole Tumor (WT), Tumor Core (TC), and Enhanced Tumor (ET) reached a Dice Similarity Coefficient (DSC) of 89.4%, 70.1%, and 49.9%, respectively. Simultaneous visual and quantitative assessment of data augmentation and network optimization can lead to an optimal model with a reasonable performance in segmenting the LGG tumors.

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas

PURPOSE

Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value.

MATERIALS AND METHODS

In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram.

RESULTS

The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively.

CONCLUSION

Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.

Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas

AIM

To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models.

RESULTS

The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort.

CONCLUSION

The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.

Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images

BACKGROUND AND OBJECTIVE

Mutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role in the prognosis, diagnosis, and treatment of gliomas. However, current methods for determining its mutation status, such as immunohistochemistry and gene sequencing, are difficult to implement widely in routine clinical diagnosis. Recent studies have shown that using deep learning methods based on pathological images of glioma can predict the mutation status of the IDH1 gene. However, our research focuses on utilizing multi-scale information in pathological images to improve the accuracy of predicting IDH1 gene mutations, thereby providing an accurate and cost-effective prediction method for routine clinical diagnosis.

METHODS

In this paper, we propose a multi-scale fusion gene identification network (MultiGeneNet). The network first uses two feature extractors to obtain feature maps at different scale images, and then by employing a bilinear pooling layer based on Hadamard product to realize the fusion of multi-scale features. Through fully exploiting the complementarity among features at different scales, we are able to obtain a more comprehensive and rich representation of multi-scale features.

RESULTS

Based on the Hematoxylin and Eosin stained pathological section dataset of 296 patients, our method achieved an accuracy of 83.575 % and an AUC of 0.886, thus significantly outperforming other single-scale methods.

CONCLUSIONS

Our method can be deployed in medical aid systems at very low cost, serving as a diagnostic or prognostic tool for glioma patients in medically underserved areas.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification

OBJECTIVE

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL.

METHODS

We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion.

RESULTS

The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL.

CONCLUSIONS

The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

Transmembrane pH gradient imaging in rodent glioma models

A unique feature of the tumor microenvironment is extracellular acidosis in relation to intracellular milieu. Metabolic reprogramming in tumors results in overproduction of H+ ions (and lactate), which are extruded from the cells to support tumor survival and progression. As a result, the transmembrane pH gradient (ΔpH), representing the difference between intracellular pH (pHi) and extracellular pH (pHe), is posited to be larger in tumors compared with normal tissue. Controlling the transmembrane pH difference has promise as a potential therapeutic target in cancer as it plays an important role in regulating drug delivery into cells. The current study shows successful development of an MRI/MRSI-based technique that provides ΔpH imaging at submillimeter resolution. We applied this technique to image ΔpH in rat brains with RG2 and U87 gliomas, as well as in mouse brains with GL261 gliomas. pHi was measured with Amine and Amide Concentration-Independent Detection (AACID), while pHe was measured with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS). The results indicate that pHi was slightly higher in tumors (7.40-7.43 in rats, 7.39-7.47 in mice) compared with normal brain (7.30-7.38 in rats, 7.32-7.36 in mice), while pHe was significantly lower in tumors (6.62-6.76 in rats, 6.74-6.84 in mice) compared with normal tissue (7.17-7.22 in rats, 7.20-7.21 in mice). As a result, ΔpH was higher in tumors (0.64-0.81 in rats, 0.62-0.65 in mice) compared with normal brain (0.13-0.16 in rats, 0.13-0.16 in mice). This work establishes an MRI/MRSI-based platform for ΔpH imaging at submillimeter resolution in gliomas.

Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas

AIM

To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set.

RESULTS

The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction.

CONCLUSION

The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

Glioma segmentation based on dense contrastive learning and multimodal features recalibration

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder. Each encoder is used to extract MR image features of a given modality. To overcome the entangle problems of multimodal semantic features, a dense contrastive learning (DCL) strategy is presented to extract the modality-specific and common features. Following that, feature recalibration block (RFB) based on modality-wise attention is used to recalibrate the semantic features of each modality, enabling the model to focus on the features that are beneficial for glioma segmentation. These recalibrated features are input into the decoder to obtain the segmentation results. To verify the superiority of the proposed method, we compare it with several state-of-the-art (SOTA) methods in terms of Dice, average symmetric surface distance (ASSD), HD95 and volumetric similarity (Vs). The comparison results show that the average Dice, ASSD, HD95 and Vs of DCL-MANet on all tumor regions are improved at least by 0.66%, 3.47%, 8.94% and 1.07% respectively. For small enhance tumor (ET) region, the corresponding improvement can be up to 0.37%, 7.83%, 11.32%, and 1.35%, respectively. In addition, the ablation results demonstrate the effectiveness of the proposed DCL and RFB, and combining them can significantly increase Dice (1.59%) and Vs (1.54%) while decreasing ASSD (40.51%) and HD95 (45.16%) on ET region. The proposed DCL-MANet could disentangle multimodal features and enhance the semantics of modality-dependent features, providing a potential means to accurately segment small lesion regions in gliomas.

Heteroions Radii Matching Produced Intensely Luminescent Bismuth-Ag2S Nanocrystals for through-Skull NIR-II Imaging of Orthotopic Glioma

Heteroions doped Ag2S nanocrystals (NCs) exhibiting enhanced near-infrared-II emission (NIR-II) hold great promise for glioma diagnosis. Nevertheless, current doped Ag2S NCs paradoxically improved properties via toxic dopants, and the blood-brain barrier (BBB) constitutes another challenge for orthotopic glioma imaging. Thus, it is urgent to develop biofriendly high-bright Ag2S NCs with active BBB-penetration for glioma-targeted imaging. Herein, bismuth (Bi) was screened to obtain Bi-Ag2S NCs with high absolute PLQY (∼13.3%) for its matched ionic-radius (1.03 Å) with Ag+. The Bi-Ag2S NCs exhibited a higher luminance and deeper penetration (5-6 mm) than clinical indocyanine green. Upon conjugation with lactoferrin, the NCs acquired BBB-crossing and glioma-targeting abilities. Time-dependent NIR-II-imaging demonstrated their effective accumulation in glioma with skull/scalp intact after intravenous injection. Moreover, the toxic-metal-free NCs exhibited negligible toxicity and great biocompatibility. The success of leveraging the ion-radii comparison may unlock the full potential of doped-Ag2S NCs in bioimaging and inspire the development of various doped NIR-II NCs.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas

BACKGROUND

1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI.

METHODS

This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.

RESULTS

The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.

CONCLUSIONS

Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial

BACKGROUND AND PURPOSE

Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas.

MATERIALS AND METHODS

Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response.

RESULTS

Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories.

CONCLUSIONS

Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.

Folic Acid Functionalized AQ4N/Gd@PDA Nanoplatform with Real-Time Monitoring of Hypoxia Relief and Enhanced Synergistic Chemo/Photothermal Therapy in Glioma

Purpose

Hypoxia is often associated with glioma chemoresistance, and alleviating hypoxia is also crucial for improving treatment efficacy. However, although there are already some methods that can improve efficacy by alleviating hypoxia, real-time monitoring that can truly achieve hypoxia relief and efficacy feedback still needs to be explored.

Methods

AQ4N/Gd@PDA-FA nanoparticles (AGPF NPs) were synthesized using a one-pot method and were characterized. The effects of AGPF NPs on cell viability, cellular uptake, and apoptosis were investigated using the U87 cell line. Moreover, the effectiveness of AGPF NPs in alleviating hypoxia was explored in tumor-bearing mice through photoacoustic imaging. In addition, the diagnosis and treatment effect of AGPF NPs were evaluated by magnetic resonance imaging (MRI) and bioluminescent imaging (BLI) on orthotopic glioma mice respectively.

Results

In vitro experiments showed that AGPF NPs had good dispersion, stability, and controlled release. AGPF NPs were internalized by cells through endocytosis, and could significantly reduce the survival rate of U87 cells and increase apoptosis under irradiation. In addition, we monitored blood oxygen saturation at the tumor site in real-time through photoacoustic imaging (PAI), and the results showed that synergistic mild-photothermal therapy/chemotherapy effectively alleviated tumor hypoxia. Finally, in vivo anti-tumor experiments have shown that synergistic therapy can effectively alleviate hypoxia and inhibit the growth of orthotopic gliomas.

Conclusion

This work not only provides an effective means for real-time monitoring of the dynamic feedback between tumor hypoxia relief and therapeutic efficacy, but also offers a potential approach for the clinical treatment of gliomas.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience

BACKGROUND AND PURPOSE

MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging.

MATERIALS AND METHODS

This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined.

RESULTS

Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone.

CONCLUSIONS

Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.

Prognostic utility and characteristics of MIB-1 labeling index as a proliferative activity marker in childhood low-grade glioma: a retrospective observational study

PURPOSE

The prognostic utility of MIB-1 labeling index (LI) in pediatric low-grade glioma (PLGG) has not yet conclusively been described. We assess the correlation of MIB-1 LI and tumor growth velocity (TGV), aiming to contribute to the understanding of clinical implications and the predictive value of MIB-1 LI as an indicator of proliferative activity and progression-free survival (PFS) in PLGG.

METHODS

MIB-1 LI of a cohort of 172 nonependymal PLGGs were comprehensively characterized. Correlation to TGV, assessed by sequential MRI-based three-dimensional volumetry, and PFS was analyzed.

RESULTS

Mean MIB-1 LI accounted for 2.7% (range: < 1-10) and showed a significant decrease to 1.5% at secondary surgery (p = .0013). A significant difference of MIB-1 LI in different histopathological types and a correlation to tumor volume at diagnosis could be shown. Linear regression analysis showed a correlation between MIB-1 LI and preoperative TGV (R2 = .55, p < .0001), while correlation to TGV remarkably decreased after incomplete resection (R2 = .08, p = .013). Log-rank test showed no association of MIB-1 LI and 5-year PFS after incomplete (MIB-1 LI > 1 vs ≤ 1%: 48 vs 46%, p = .73) and gross-total resection (MIB-1 LI > 1 vs ≤ 1%: 89 vs 95%, p = .75).

CONCLUSION

These data confirm a correlation of MIB-1 LI and radiologically detectable TGV in PLGG for the first time. Compared with preoperative TGV, a crucially decreasing correlation of MIB-1 LI and TGV after surgery may result in limited prognostic capability of MIB-1 LI in PLGG.

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients

OBJECTIVES

A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.

MATERIALS AND METHODS

Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.

RESULTS

Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.

CONCLUSION

We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.

CLINICAL RELEVANCE STATEMENT

Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.

KEY POINTS

• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

Survival Analysis of Patients Undergoing Intraoperative Contrast-enhanced Ultrasound in the Surgical Treatment of Malignant Glioma

OBJECTIVE

Complete resection of malignant gliomas is often challenging. Our previous study indicated that intraoperative contrast-enhanced ultrasound (ICEUS) could aid in the detection of residual tumor remnants and the total removal of brain lesions. This study aimed to investigate the survival rates of patients undergoing resection with or without the use of ICEUS and to assess the impact of ICEUS on the prognosis of patients with malignant glioma.

METHODS

A total of 64 patients diagnosed with malignant glioma (WHO grade HI and IV) who underwent surgery between 2012 and 2018 were included. Among them, 29 patients received ICEUS. The effects of ICEUS on overall survival (OS) and progression-free survival (PFS) of patients were evaluated. A quantitative analysis was performed to compare ICEUS parameters between gliomas and the surrounding tissues.

RESULTS

The ICEUS group showed better survival rates both in OS and PFS than the control group. The univariate analysis revealed that age, pathology and ICEUS were significant prognostic factors for PFS, with only age being a significant prognostic factor for OS. In multivariate analysis, age and ICEUS were significant prognostic factors for both OS and PFS. The quantitative analysis showed that the intensity and transit time of microbubbles reaching the tumors were significantly different from those of microbubbles reaching the surrounding tissue.

CONCLUSION

ICEUS facilitates the identification of residual tumors. Age and ICEUS are prognostic factors for malignant glioma surgery, and use of ICEUS offers a better prognosis for patients with malignant glioma.

Surgical treatment of small recurrent gliomas based on MR imaging examination

Microrecurrent glioma is a common neurological tumor, and the key to its surgical treatment is to accurately evaluate the size, location and degree of recurrence of the lesion. The purpose of this study was to explore the surgical treatment of microrecurrent glioma based on MR Imaging, and to provide accurate and reliable basis for clinical decision-making. Before surgery, detailed MR Imaging tests were performed for each patient to accurately locate and evaluate the characteristics of the lesions. Multimodal imaging examination were arranged to accurate the pre-operation diagnosis. Neuro-navigation is necessary for the operation design and tumor confirmation. Function monitor and intraoperation MR were prepared when necessary.Mini was defined by the size, location and symptoms. In all 5 cases requiring reoperation, total resection was achieved. No systemic and local complications occurred. No permeant neurological dysfunction remained. The average stay time after the operation is days. All patients survived in the recent follow-up. Reoperation of mini recurrent glioma is a good treatment choice. We made little injury to patients, which wouldn't affect their conditions and next therapies. Through MR Imaging, the diagnosis and location of microrecurrent glioma, as well as the relationship with surrounding tissues and the degree of infiltration, provide important information for surgeons to evaluate the resectable lesion. By combining MR And functional imaging results, the blood supply and functional area of the lesion can be monitored in real time during surgery, thereby reducing surgical risk and maximizing the protection of surrounding healthy tissue.

Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners

PURPOSE

Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with L-[methyl-11C] methionine ([11C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system.

METHODS

Consecutive 178 preoperative [11C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [11C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment.

RESULTS

The combined [11C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [11C]MET PET/CT radiomics score, patients' age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726-0.998) in the training dataset and 0.866 (95% CI, 0.777-0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541-1.000) in the training dataset and 0.766 (95% CI, 0.612-0.921) in the validation dataset.

CONCLUSION

[11C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.

Relaxation Along a Fictitious Field, continuous wave T1rho, adiabatic T1rho and adiabatic T2rho imaging of human gliomas at 3T: A feasibility study

In pre-clinical models of brain gliomas, Relaxation Along a Fictitious Field in second rotating frame (TRAFF2), continues wave T1rho (T1ρcw), adiabatic T1rho (T1ρadiab), and adiabatic T2rho (T2ρadiab) relaxation time mappings have demonstrated potential to non-invasively characterize brain gliomas. Our aim was to evaluate the feasibility and potential of 4 different spin lock methods at 3T to characterize primary brain glioma. 22 patients (26-72 years) with suspected primary glioma. T1ρcw was performed using pulse peak amplitude of 500Hz and pulse train durations of 40 and 80 ms while the corresponding values for T1ρadiab, T2ρadiab, TRAFF2 were 500/500/500Hz and 48 and 96, 64 and 112, 45 and 90 ms, respectively. The parametric maps were calculated using a monoexponential model. Molecular profiles were evaluated from tissue specimens obtained during the resection. The lesion regions-of-interest were segmented from high intensity FLAIR using automatic segmentation with manual refinement. Statistical descriptors from the voxel intensity values inside each lesion and radiomic features (Pyrad MRC package) were calculated. From extracted radiomics, mRMRe R package version 2.1.0 was used to select 3 features in each modality for statistical comparisons. Of the 22 patients, 10 were found to have IDH-mutant gliomas and of those 5 patients had 1p/19q codeletion group comparisons. Following correction for effects of age and gender, at least one statistical descriptor was able to differentiate between IDH and 1p/19q codeletion status for all the parametric maps. In the radiomic analysis, corner-edge detector features with Harris-Stephens filtered signal showed significant group differences in IDH and 1p/19q codeletion groups. Spin lock imaging at 3T of human glioma was feasible and various qualitative parameters derived from the parametric maps were found to have potential to differentiate IDH and 1p19q codeletion status. Future larger prospective clinical trials are warranted to evaluate these methods further.

Beating a skullduggerous infantile hemispheric high-grade glioma

A case of infantile hemispheric high grade glioma in a five-month-old boy is presented. Striking images of a 'beaten copper pot' skull were concerning at first, but with a successful surgical and oncological plan he is well three years later, displaying only minor signs of developmental delay.

The effect Of vascular related CeRNA genes and corresponding imaging biomarkers on survival in lower grade glioma

BACKGROUND & AIMS

To investigate the differential expression of vascular related ceRNA regulatory genes in LGG with different mutations of IDH1 and MGMT, and to verify imaging gene markers that can be closely associated with vascular related ceRNA regulatory genes.

METHOD

Five hundred fifteen patients with LGG were collected from TCGA database. CeRNA network analysis, GO analysis and Cox risk regression were used to find vascular ceRNA regulatory genes and their genetic markers related to survival. The preoperative MRI image data and postoperative tumor tissues of 14 patients with WHO grade III glioma were collected for full transcriptome analysis. The correlation between image characteristics of LGG and survival related vascular ceRNA regulatory genes was compared using nonparametric U test and Pearson correlation coefficient analysis.

RESULTS

Vascular related genes ranked first in the functional enrichment analysis of differentially expressed genes in LGG. EPHA2, ETS1, YAP1 and MEIS1 could significantly affect the survival of patients in each group of LGG. The volume of enhanced region was negatively correlated with IDH1 (r = -0.622, P = 0.009) mutation and TMEM100 (r = -0.535, P = 0.024), and positively correlated with MEIS1 (r = 0.551, P = 0.021), rCBFmax value was negatively correlated with TMEM100 (r = -0.492, P = 0.037).

CONCLUSIONS

Under different IDH1 mutations, lncRNA-dominated vascular-related ceRNA regulatory genes were the first differentially expressed subset of each group, and could be used as an effective risk factor affecting the survival of LGG. The image characteristics of LGG was an ideal image gene marker. It was a reliable imaging biological marker which can truly reflect the pathophysiological characteristics of glioma.

Post-ictal changes presenting as late pseudoprogression on MRI and PET in a patient with diffuse glioma: Case report and brief literature review

Following completion of adjuvant radiation and chemotherapy imaging surveillance forms a major role in the management of diffuse gliomas. The primary role of imaging is to detect recurrences earlier than clinical symptomatology. Magnetic resonance imaging (MRI) is considered the gold standard in follow-up protocols owing to better soft tissue delineation and multiparametric nature. True recurrence can often mimic treatment-related changes, it is of paramount importance to differentiate between the two entities as the clinical course is divergent. Addition of functional sequences like perfusion, spectroscopy and metabolic imaging can provide further details into the microenvironment. In equivocal cases, a follow-up short interval imaging might be obtained to settle the diagnostic dilemma. Here, we present a patient with diagnosis of recurrent oligodendroglioma treated with adjuvant chemoradiation, presenting with seizures five years post-completion of chemotherapy for recurrence. On MRI, subtle new onset gyral thickening of the left frontal region with mild increase in perfusion and patchy areas of raised choline. FET-PET (fluoro-ethyltyrosine) showed an increased tumour-to-white matter (T/Wm) ratio favouring tumour recurrence. Based on discussion in a multi-disciplinary joint clinic, short interval follow-up MRI was undertaken at two months showing decrease in gyral thickening and resolution of enhancing areas in left frontal lobe. Repeat imaging one year later demonstrated stable disease status without further new imaging findings. Given the changes resolving completely without any anti-tumoral intervention, we conclude this to be peri-ictal pseudoprogression, being the second such case described in India.

Tumour-infiltrated cortex participates in large-scale cognitive circuits

The extent to which tumour-infiltrated brain tissue contributes to cognitive function remains unclear. We tested the hypothesis that cortical tissue infiltrated by diffuse gliomas participates in large-scale cognitive circuits using a unique combination of intracranial electrocorticography (ECoG) and resting-state functional magnetic resonance (fMRI) imaging in four patients. We also assessed the relationship between functional connectivity with tumour-infiltrated tissue and long-term cognitive outcomes in a larger, overlapping cohort of 17 patients. We observed significant task-related high gamma (70-250 Hz) power modulations in tumour-infiltrated cortex in response to increased cognitive effort (i.e., switch counting compared to simple counting), implying preserved functionality of neoplastic tissue for complex tasks probing executive function. We found that tumour locations corresponding to task-responsive electrodes exhibited functional connectivity patterns that significantly co-localised with canonical brain networks implicated in executive function. Specifically, we discovered that tumour-infiltrated cortex with larger task-related high gamma power modulations tended to be more functionally connected to the dorsal attention network (DAN). Finally, we demonstrated that tumour-DAN connectivity is evident across a larger cohort of patients with gliomas and that it relates to long-term postsurgical outcomes in goal-directed attention. Overall, this study contributes convergent fMRI-ECoG evidence that tumour-infiltrated cortex participates in large-scale neurocognitive circuits that support executive function in health. These findings underscore the potential clinical utility of mapping large-scale connectivity of tumour-infiltrated tissue in the care of patients with diffuse gliomas.

68 Ga-Pentixafor PET/CT for In Vivo Imaging of CXCR4 Receptors in Glioma Demonstrating a Potential for Response Assessment to Radiochemotherapy: Preliminary Results

PURPOSE

The aim of this study was to evaluate the diagnostic potential of 68 Ga-pentixafor PET/CT for in vivo CXCR4 receptors imaging in glioma and its possible role in response assessment to radiochemotherapy (R-CT).

METHODS

Nineteen (12 men, 7 women) patients with glioblastoma multiforme (GBM) underwent 68 Ga-pentixafor PET/CT, contrast-enhanced MR, and MR spectroscopy. Patients were divided in to 2 groups, that is, group I was the presurgical (n = 9) group in which the scanning was done before surgery, and PET findings were correlated with CXCR4 receptors' density. The group II was the postsurgical (n = 10) group in which the scanning was done before and after R-CT and used for treatment response evaluation. The quantitative analysis of 68 Ga-pentixafor PET/CT evaluated the mean SUV max , SUV mean , SUV peak , and T/B values. MR spectroscopy data evaluated the ratios of tumor metabolites (choline, NAA, creatine).

RESULTS

68 Ga-Pentixafor uptake was noted in all (n = 19) the patients. In the group I, the mean SUV max , SUV mean , SUV peak , and T/B values were found to be 4.5 ± 1.6, 0.60 ± 0.26, 1.95 ± 0.8, and 6.9 ± 4.6, respectively. A significant correlation ( P < 0.005) was found between SUV mean and choline/NAA ratio. Immunohistochemistry performed in 7/9 showed CXCR4 receptors' positivity (intensity 3 + ; stained cells >50.0%). In the group II, the mean SUV max at baseline was 4.6 ± 2.1 and did not differ (4.4 ± 1.6) significantly from the value noted at post-R-CT follow-up PET/CT imaging. At 6 months' clinical follow-up, 4 patients showed stable disease. SUV max and T/B ratios at follow-up imaging were lower (3.70 ± 0.90, 2.64 ± 1.35) than the corresponding values (4.40 ± 2.8; 2.91 ± 0.93) noted at baseline. Six (6/10) patients showed disease progression, and the mean SUV max , and T/B ratio in these patients were significantly ( P < 0.05) higher than the corresponding values at baseline and also higher than that noted in the stable patients.

CONCLUSIONS

68 Ga-Pentixafor PET/CT can be used for in vivo mapping of CXCR4 receptors in GBM. The technique after validation in a large cohort of patients may have added diagnostic value for the early detection of GBM recurrence and for treatment response evaluation.

Methyl-11C-L-methionine positron emission tomography for radiotherapy planning for recurrent malignant glioma

OBJECTIVE

To investigate differences in uptake regions between methyl-11C-L-methionine positron emission tomography (11C-MET PET) and gadolinium (Gd)-enhanced magnetic resonance imaging (MRI), and their impact on dose distribution, including changing of the threshold for tumor boundaries.

METHODS

Twenty consecutive patients with grade 3 or 4 glioma who had recurrence after postoperative radiotherapy (RT) between April 2016 and October 2017 were examined. The study was performed using simulation with the assumption that all patients received RT. The clinical target volume (CTV) was contoured using the Gd-enhanced region (CTV(Gd)), the tumor/normal tissue (T/N) ratios of 11C-MET PET of 1.3 and 2.0 (CTV (T/N 1.3), CTV (T/N 2.0)), and the PET-edge method (CTV(P-E)) for stereotactic RT planning. Differences among CTVs were evaluated. The brain dose at each CTV and the dose at each CTV defined by 11C-MET PET using MRI as the reference were evaluated.

RESULTS

The Jaccard index (JI) for concordance of CTV (Gd) with CTVs using 11C-MET PET was highest for CTV (T/N 2.0), with a value of 0.7. In a comparison of pixel values of MRI and PET, the correlation coefficient for cases with higher JI was significantly greater than that for lower JI cases (0.37 vs. 0.20, P = 0.007). D50% of the brain in RT planning using each CTV differed significantly (P = 0.03) and that using CTV (T/N 1.3) were higher than with use of CTV (Gd). V90% and V95% for each CTV differed in a simulation study for actual treatment using CTV (Gd) (P = 1.0 × 10-7 and 3.0 × 10-9, respectively) and those using CTV (T/N 1.3) and CTV (P-E) were lower than with CTV (Gd).

CONCLUSIONS

The region of 11C-MET accumulation is not necessarily consistent with and larger than the Gd-enhanced region. A change of the tumor boundary using 11C-MET PET can cause significant changes in doses to the brain and the CTV.

Minimally invasive keyhole approach for supramaximal frontal glioma resections: technical note

OBJECTIVE

The authors aimed to review the frontal lobe's surgical anatomy, describe their keyhole frontal lobectomy technique, and analyze the surgical results.

METHODS

Patients with newly diagnosed frontal gliomas treated using a keyhole approach with supramaximal resection (SMR) from 2016 to 2022 were retrospectively reviewed. Surgeries were performed on patients asleep and awake. A human donor head was dissected to demonstrate the surgical anatomy. Kaplan-Meier curves were used for survival analysis.

RESULTS

Of the 790 craniotomies performed during the study period, those in 47 patients met our inclusion criteria. The minimally invasive approach involved four steps: 1) debulking the frontal pole; 2) subpial dissection identifying the sphenoid ridge, olfactory nerve, and optic nerve; 3) medial dissection to expose the falx cerebri and interhemispheric structures; and 4) posterior dissection guided by motor mapping, avoiding crossing the inferior plane defined by the corpus callosum. A fifth step could be added for nondominant lesions by resecting the inferior frontal gyrus. Perioperative complications were recorded in 5 cases (10.6%). The average hospital length of stay was 3.3 days. High-grade gliomas had a median progression-free survival of 14.8 months and overall survival of 23.9 months.

CONCLUSIONS

Keyhole approaches enabled successful SMR of frontal gliomas without added risks. Robust anatomical knowledge and meticulous surgical technique are paramount for obtaining successful resections.

Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

OBJECTIVES

Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes.

METHODS

Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160).

RESULTS

Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone.

CONCLUSION

Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting.

CLINICAL RELEVANCE STATEMENT

Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes.

KEY POINTS

• MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.

Tectal Plate Glioma: A Clinical and Radiologic Analysis of Progression and Management in Adults

BACKGROUND

Tectal plate gliomas (TPGs) are a heterogeneous group of uncommon brain tumors. TPGs are considered indolent and are usually managed conservatively but they have the potential to transform into higher-grade tumors. The aims of this study were to investigate the natural history of adult TPG, treatment outcomes, and overall survival.

METHODS

A retrospective cohort analysis was performed of adult patients with TPG between 1993 and 2021. Baseline clinical, radiologic, and management characteristics were collected. The primary outcome was tumor progression, defined as increasing size on radiologic assessment or new gadolinium contrast enhancement. Secondary outcomes included management and mortality.

RESULTS

Thirty-nine patients were included, of whom 23 (52.2%) were men. Median age at diagnosis was 35 years (interquartile range, 27-53). Radiologic tumor progression was observed in 8 patients (20.5%). The 10-year progression-free survival was 72.6% (95% confidence interval [CI], 0.58-0.91). The 10-year overall survival was 86.5% (95% confidence interval, 0.75-1.0). Cerebrospinal fluid diversion procedures were used in 62% of the cohort (n = 24). Seventeen patients (43.6%) underwent at least 1 endoscopic third ventriculostomy, whereas only 6 patients (15.4%) underwent at least 1 ventriculoperitoneal shunt.

CONCLUSIONS

TPG has an overall favorable clinical prognosis, although progression occurs in 1 in 5 patients. Showing accurate factors by which patients with TPG may be risk stratified should be a key area of further research. A follow-up duration of 10 years would be a reasonable window based on the radiologic progression rates in this study; however, larger cohort studies are needed to answer both questions definitively.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Detection of recurrent high-grade glioma using microstructure characteristics of distinct metabolic compartments in a multimodal and integrative 18F-FET PET/fast-DKI approach

OBJECTIVES

Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-L-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping.

METHODS

Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80-100% and 60-75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann-Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations.

RESULTS

Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p < .05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance - most pronounced for FA60 (AUC = 0.86, p < .001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p < .001) and was negatively correlated with amino acid uptake (rs =  - 0.46, p < .001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p < .001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p = .04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting.

CONCLUSION

Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone.

CLINICAL RELEVANCE STATEMENT

A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings.

KEY POINTS

• Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone.

Nonenhancing motor eloquent gliomas: navigated transcranial magnetic stimulation oncobiological signature

OBJECTIVE

Preoperative grading of nonenhancing motor eloquent gliomas is hampered by a lack of specific imaging surrogates. Tumor grading is crucial for the informed consent discussion before tumor resection. In this paper, the authors hypothesized that navigated transcranial magnetic stimulation (nTMS)-derived metrics could provide significant information to distinguish between high- and low-grade motor eloquent gliomas that present as nonenhancing tumors and therefore contribute to improving patient counseling, timing of treatment, preoperative planning, and intraoperative strategies.

METHODS

The authors conducted a retrospective single-center cohort study of patients admitted for tumor surgery between January 2018 and April 2022 with a nonenhancing motor eloquent glioma and preoperative bilateral nTMS mapping. nTMS data including resting motor threshold (RMT), interhemispheric RMT ratio (iRMTr), Cortical Excitability Score (CES), area and volume of cortical activation, and motor evoked potential (MEP) characteristics were obtained and integrated with demographic and clinical data.

RESULTS

Thirty patients met the inclusion criteria, and 10 healthy participants were recruited for comparison. Seizures were the most common presenting symptom (25 patients) and WHO grade 3 the most common tumor grade (21 patients). The area and volume of functional cortical activation of both the abductor pollicis brevis and first dorsal interosseous muscles were decreased in healthy participants compared with patients with WHO grade 3 glioma (p < 0.05). An abnormal iRMTr for the lower limbs (16.7% [1/6] WHO grade 2, 76.2% [16/21] WHO grade 3, 100% [3/3] WHO grade 4; p = 0.015) and a higher CES (maximal abnormal CES: 0% [0/6] WHO grade 2, 38% [8/21] WHO grade 3, 66.7% [2/3] WHO grade 4; p = 0.010) were associated with the prediction of high-grade lesions. A total of 7280 MEPs were analyzed. A significant increase in the amplitude and a significant decrease in latency in the MEPs for the first dorsal interosseous and abductor digiti minimi muscles (p < 0.0001) were identified in healthy participants compared with WHO grade 3 glioma patients.

CONCLUSIONS

Nonenhancing motor eloquent gliomas have a different impact on both anatomical and functional reorganization of motor areas according to their WHO grading.

Abnormal expression of long non‑coding RNA LINC01270 in glioma and its correlation with X‑ray computed tomography signs

This study focused on the association of LINC01270 and computed tomography (CT) signs with glioma development, to evaluate their potential in the early detection of glioma. Serum LINC01270 was evaluated in glioma patients and healthy individuals using PCR. The involvement of LINC01270 in glioma onset and development was evaluated by ROC and chi‑square test. The association of LINC01270 with the CT signs and their combined effects in the diagnosis in glioma were also estimated. Serum LINC01270 was significantly elevated in glioma patients, which was closely associated with patients' advanced WHO grades and lower KPS. Both LINC01270 upregulation and CT findings showed significant potential in diagnosing glioma, and LINC01270 correlated significantly with the invasion risk and metastasis indicated on CT. The combination of LINC01270 expression and CT findings significantly improved the sensitivity and specificity of glioma diagnosis. Upregulated LINC01270 in glioma is associated with malignant and severe disease development and has significant diagnostic value. Combined detection of LINC01270 and CT findings could improve the diagnostic efficacy in glioma cases, thus optimizing clinical diagnosis.

[Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features]

Objective

The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors. In the segmentation process of brain magnetic resonance imaging (MRI), convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features, which narrows the receptive field and leads to insufficient segmentation accuracy. This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet.

Methods

1) Algorithm construction: A 3D-UNet model with three pathways for more global contextual feature extraction, or 3DGE-UNet, was proposed in the paper. By using publicly available datasets from the Brain Tumor Segmentation Challenge (BraTS) of 2019 (335 patient cases), a global contextual feature extraction (GE) module was designed. This module was integrated at the first, second, and third skip connections of the 3D UNet network. The module was utilized to fully extract global features at different scales from the images. The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales, thereby facilitating end-to-end automatic segmentation of brain tumors. 2) Algorithm validation: The image data were sourced from the BraTs 2019 dataset, which included the preoperative MRI images of 335 patients across four modalities (T1, T1ce, T2, and FLAIR) and a tumor image with annotations made by physicians. The dataset was divided into the training, the validation, and the testing sets at an 8∶1∶1 ratio. Physician-labelled tumor images were used as the gold standard. Then, the algorithm's segmentation performance on the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) was evaluated in the test set using the Dice coefficient (for overall effectiveness evaluation), sensitivity (detection rate of lesion areas), and 95% Hausdorff distance (segmentation accuracy of tumor boundaries). The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup. Additionally, the performance indicators were evaluated using the 3DGE-UNet model, ResUNet, UNet++, nnUNet, and UNETR, and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model.

Results

1) In internal validation, the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%, 87.14%, and 83.35% for segmenting the WT, TC, and ET regions in the test set, respectively, producing the optimal values for comprehensive evaluation. These scores were superior to the corresponding scores of the traditional 3D-UNet model, which were 89.79%, 85.13%, and 80.90%, indicating a significant improvement in segmentation accuracy across all three regions (P<0.05). Compared with the 3D-UNet model, the 3DGE-UNet model demonstrated higher sensitivity for ET (86.46% vs. 80.77%) (P<0.05) , demonstrating better performance in the detection of all the lesion areas. When dealing with lesion areas, the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way, thereby effectively reducing the likelihood of missed diagnoses. The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT, producing a mean 95% Hausdorff distance superior to that of the 3D-UNet model (8.17 mm vs. 13.61 mm, P<0.05). However, its performance for TC (8.73 mm vs. 7.47 mm) and ET (6.21 mm vs. 5.45 mm) was similar to that of the 3D-UNet model. 2) In the external validation, the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC (87.25%), the mean sensitivity for WT (94.59%), the mean sensitivity for TC (86.98%), and the mean 95% Hausdorff distance for ET (5.37 mm). Nonetheless, these differences were not statistically significant (P>0.05). The 3DGE-UNet model demonstrated rapid convergence during the training phase, outpacing the other external models.

Conclusion

The 3DGE-UNet model can effectively extract and fuse feature information on different scales, improving the accuracy of brain tumor segmentation.

Intraoperative ultrasound for surgical resection of high-grade glioma and glioblastoma: a meta-analysis of 732 patients

PURPOSE

Here, we conducted a meta-analysis to explore the use of intraoperative ultrasound (iUS)-guided resection in patients diagnosed with high-grade glioma (HGG) or glioblastoma (GBM). Our aim was to determine whether iUS improves clinical outcomes compared to conventional neuronavigation (CNN).

METHODS

Databases were searched until April 21, 2023 for randomized controlled trials (RCTs) and observational cohort studies that compared surgical outcomes for patients with HGG or GBM with the use of either iUS in addition to standard approach or CNN. The primary outcome was overall survival (OS). Secondary outcomes include volumetric extent of resection (EOR), gross total resection (GTR), and progression-free survival (PFS). Outcomes were analyzed by determining pooled relative risk ratios (RR), mean difference (MD), and standardized mean difference (SMD) using random-effects model.

RESULTS

Of the initial 867 articles, only 7 articles specifically met the inclusion criteria (1 RCT and 6 retrospective cohorts). The analysis included 732 patients. Compared to CNN, the use of iUS was associated with higher OS (SMD = 0.26,95%CI=[0.12,0.39]) and GTR (RR = 2.02; 95% CI=[1.31,3.1]) for both HGG and GBM. There was no significant difference in PFS or EOR.

CONCLUSION

The use of iUS in surgical resections for HGG and GBM can improve OS and GTR compared to CNN, but it did not affect PFS. These results suggest that iUS reduces mortality associated with HGG and GBM but not the risk of recurrence. These results can provide valuable cost-effective interventions for neurosurgeons in HGG and GBM surgery.

Artificial intelligence-based MRI radiomics and radiogenomics in glioma

The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.

Blood-brain barrier-crossing dendrimers for glioma theranostics

Glioma, as a disease of the central nervous system, is difficult to be treated due to the presence of the blood-brain barrier (BBB) that can severely hamper the efficacy of most therapeutic agents. Hence, drug delivery to glioma in an efficient, safe, and specifically targeted manner is the key to effective treatment of glioma. With the advances in nanotechnology, targeted drug delivery systems have been extensively explored to deliver chemotherapeutic agents, nucleic acids, and contrast agents. Among these nanocarriers, dendrimers have played a significant role since they possess highly branched structures, and are easy to be decorated, thus offering numerous binding sites for various drugs and ligands. Dendrimers can be designed to cross the BBB for glioma targeting, therapy or theranostics. In this review, we provide a concise overview of dendrimer-based carrier designs including dendrimer surface modification with hydroxyl termini, peptides, and transferrin etc. for glioma imaging diagnostics, chemotherapy, gene therapy, or imaging-guided therapy. Finally, the future perspectives of dendrimer-based glioma theraputics are also briefly discussed.

ImmunoPET imaging of TIGIT in the glioma microenvironment

Glioblastoma (GBM) is the most common primary malignant brain tumor. Currently, there are few effective treatment options for GBM beyond surgery and chemo-radiation, and even with these interventions, median patient survival remains poor. While immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy against non-central nervous system cancers, ICI trials for GBM have typically had poor outcomes. TIGIT is an immune checkpoint receptor that is expressed on activated T-cells and has a role in the suppression of T-cell and Natural Killer (NK) cell function. As TIGIT expression is reported as both prognostic and a biomarker for anti-TIGIT therapy, we constructed a molecular imaging agent, [89Zr]Zr-DFO-anti-TIGIT (89Zr-αTIGIT), to visualize TIGIT in preclinical GBM by immunoPET imaging. PET imaging and biodistribution analysis of 89Zr-αTIGIT demonstrated uptake in the tumor microenvironment of GBM-bearing mice. Blocking antibody and irrelevant antibody tracer studies demonstrated specificity of 89Zr-αTIGIT with significance at a late time point post-tracer injection. However, the magnitude of 89Zr-αTIGIT uptake in tumor, relative to the IgG tracer was minimal. These findings highlight the features and limitations of using 89Zr-αTIGIT to visualize TIGIT in the GBM microenvironment.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas

BACKGROUND

To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas.

METHODS

In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score.

RESULTS

The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance.

CONCLUSIONS

The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Distinguishment between high-grade gliomas and solitary brain metastases in peritumoural oedema: quantitative analysis using synthetic MRI at 3 T

AIM

To investigate the efficacy of synthetic magnetic resonance imaging (MRI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs) in peritumoural oedema.

MATERIALS AND METHODS

Thirty-five patients with HGGs and 25 patients with SBMs were recruited and scanned using synthetic MRI using a 3 T scanner. Two radiologists measured synthetic MRI-derived relaxation values independently (T1, T2, proton density [PD]) in the peritumoural oedema, which was used to generate quantitative metrics before (T1native, T2native, and PDnative) and after (T1post, T2post, and PDpost) contrast agent injection. Student's t-test or the Mann-Whitney U-test was performed to detect statistically significant differences in the aforementioned metrics in peritumoural oedema between HGGs and SBMs. The receiver operating characteristic (ROC) curves were plotted to evaluate the efficacy of each metric in distinguishing the two groups, and the areas under the curves (AUCs) were compared pairwise by performing the Delong test.

RESULTS

The mean T1native, T2native, and T1post values in the peritumoural oedema of HGGs were significantly lower compared with SBMs (all p<0.05). The T1post value had a higher AUC (0.843) in differentiating HGGs and SBMs than all other individual metrics (all p<0.05). The combined T1native, T2native, and T1post model had the best distinguishing performance with an AUC, sensitivity, and specificity of 0.987, 94.3%, and 100%, respectively.

CONCLUSIONS

Synthetic MRI may be a potential supplement to the preoperative diagnosis of HGGs and SBMs in clinical practice, as the synthetic MRI-derived tri-parametric model in the peritumoural oedema showed significantly improved diagnostic performance in distinguishing HGGs from SBMs.

Preoperative vascular heterogeneity based on dynamic susceptibility contrast MRI in predicting spatial pattern of locally recurrent high-grade gliomas

OBJECTIVES

To investigate if spatial recurrence pattern is associated with patient prognosis, and whether MRI vascular habitats can predict spatial pattern.

METHODS

In this retrospective study, 69 patients with locally recurrent high-grade gliomas (HGGs) were included. The cohort was divided into intra-resection cavity recurrence (ICR) and extra-resection cavity recurrence (ECR) patterns, according to the distance between the location of the recurrent tumor and the resection cavity or surgical region. Four vascular habitats, high angiogenic tumor, low angiogenic tumor, infiltrated peripheral edema, and vasogenic peripheral edema, were segmented and vascular heterogeneity parameters were analyzed. The survival and diagnostic performance under different spatial recurrence patterns were analyzed by Kaplan-Meier and ROC. A nomogram model was constructed by regression analysis and validated by bootstrapping technique.

RESULTS

Progression-free survival (PFS) and overall survival (OS) were longer for ICR (n = 32) than those for ECR (n = 37) (median PFS: 8 vs. 5 months, median OS: 17 vs. 13 months, p < 0.05). MRI vascular habitat analyses showed ECR had higher median relative cerebral blood volume (rCBVmedian) at each habitat than ICR (all p < 0.01). The rCBVmedian at IPE had good diagnostic performance (AUC: 0.727, 95%CI: 0.607, 0.828). The AUC of the nomogram based on MRI vascular habitats and clinical factors was 0.834 (95%CI: 0.726, 0.913) and was confirmed as 0.833 (95%CI: 0.830, 0.836) by bootstrapping validation.

CONCLUSIONS

The spatial pattern of locally recurrent HGGs is associated with prognosis. MRI vascular heterogeneity parameter could be used as a non-invasive imaging marker to predict spatial recurrence pattern.

CLINICAL RELEVANCE STATEMENT

Vascular heterogeneity parameters based on MRI vascular habitat analyses can non-invasively predict the spatial patterns of locally recurrent high-grade gliomas, providing a new diagnostic basis for clinicians to develop the extent of surgical resection and postoperative radiotherapy planning.

KEY POINTS

• Intra-resection cavity pattern was associated with longer progression-free survival and overall survival in locally recurrent high-grade gliomas. • Higher vascular heterogeneities in extra-resection cavity recurrence than in intra-resection cavity recurrence and the vascular heterogeneity parameters had good diagnostic performance in discriminating spatial recurrence pattern. • A nomogram model based on MRI vascular habitats and clinical factors had good performance in predicting spatial recurrence pattern.