MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement.

Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. METHODS: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. RESULTS: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. CONCLUSIONS: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

Advancements in Neurosurgical Intraoperative Histology.

Despite their relatively low incidence globally, central nervous system (CNS) tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and the identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and the rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes.

The relationship between imaging features, therapeutic response, and overall survival in pediatric diffuse intrinsic pontine glioma.

We aimed to evaluate the relationship between imaging features, therapeutic responses (comparative cross-product and volumetric measurements), and overall survival (OS) in pediatric diffuse intrinsic pontine glioma (DIPG). A total of 134 patients (≤ 18 years) diagnosed with DIPG were included. Univariate and multivariate analyses were performed to evaluate correlations of clinical and imaging features and therapeutic responses with OS. The correlation between cross-product (CP) and volume thresholds in partial response (PR) was evaluated by linear regression. The log-rank test was used to compare OS patients with discordant therapeutic response classifications and those with concordant classifications. In univariate analysis, characteristics related to worse OS included lower Karnofsky, larger extrapontine extension, ring-enhancement, necrosis, non-PR, and increased ring enhancement post-radiotherapy. In the multivariate analysis, Karnofsky, necrosis, extrapontine extension, and therapeutic response can predict OS. A 25% CP reduction (PR) correlated with a 32% volume reduction (R2 = 0.888). Eight patients had discordant therapeutic response classifications according to CP (25%) and volume (32%). This eight patients' median survival time was 13.0 months, significantly higher than that in the non-PR group (8.9 months), in which responses were consistently classified as non-PR based on CP (25%) and volume (32%). We identified correlations between imaging features, therapeutic responses, and OS; this information is crucial for future clinical trials. Tumor volume may represent the DIPG growth pattern more accurately than CP measurement and can be used to evaluate therapeutic response.

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma.

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

PDZK1 is correlated with DCE-MRI perfusion parameters in high-grade glioma.

OBJECTIVE: This study investigated the relationship between PDZK1 expression and Dynamic Contrast-Enhanced MRI (DCE-MRI) perfusion parameters in High-Grade Glioma (HGG). METHODS: Preoperative DCE-MRI scanning was performed on 80 patients with HGG to obtain DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), and reverse transfer constant (kep). PDZK1 in HGG patients was detected, and its correlation with DCE-MRI perfusion parameters was assessed by the Pearson method. An analysis of Cox regression was performed to determine the risk factors affecting survival, while Kaplan-Meier and log-rank tests to evaluate PDZK1's prognostic significance, and ROC curve analysis to assess its diagnostic value. RESULTS: PDZK1 was upregulated in HGG patients and predicted poor overall survival and progression-free survival. Moreover, PDZK1 expression distinguished grade III from grade IV HGG. PDZK1 expression was positively correlated with Ktrans 90, and ve_90, and negatively correlated with kep_max, and kep_90. CONCLUSION: PDZK1 is upregulated in HGG, predicts poor survival, and differentiates tumor grading in HGG patients. PDZK1 expression is correlated with DCE-MRI perfusion parameters.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

ReMIND: The Brain Resection Multimodal Imaging Database.

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n = 92), metastases (n = 11), and others (n = 11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Differential tractography and whole brain connectometry in primary motor area gliomas resection: A feasibility study.

OBJECTIVE: Establish the evolution of the connectome before and after resection of motor area glioma using a comparison of connectome maps and high-definition differential tractography (DifT). METHODS: DifT was done using normalized quantitative anisotropy (NQA) with DSI Studio. The quantitative analysis involved obtaining mean NQA and fractional anisotropy (FA) values for the disrupted pathways tracing the corticospinal tract (CST), and white fiber network changes over time. RESULTS: We described the baseline tractography, DifT, and white matter network changes from two patients who underwent resection of an oligodendroglioma (Case 1) and an IDH mutant astrocytoma, grade 4 (Case 2). CASE 1: There was a slight decrease in the diffusion signal of the compromised CST in the immediate postop. The NQA and FA values increased at the 1-year follow-up (0.18 vs. 0.32 and 0.35 vs. 0.44, respectively). CASE 2: There was an important decrease in the immediate postop, followed by an increase in the follow-up. In the 1-year follow-up, the patient presented with radiation necrosis and tumor recurrence, increasing NQA from 0.18 in the preop to 0.29. Fiber network analysis: whole-brain connectome comparison demonstrated no significant changes in the immediate postop. However, in the 1-year follow up there was a notorious reorganization of the fibers in both cases, showing the decreased density of connections. CONCLUSIONS: Connectome studies and DifT constitute new potential tools to predict early reorganization changes in a patient's networks, showing the brain plasticity capacity, and helping to establish timelines for the progression of the tumor and treatment-induced changes.

Comparison of two region-of-interest placement methods for histogram analysis of apparent diffusion coefficient maps for glioma grading.

Objectives: We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas. Methods: We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maxi-mum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden's index. Results: The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively. Conclusions: HA parameters had value for grading gliomas. Ex-cluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Long-term Intravital Investigation of an Orthotopic Glioma Mouse Model via Optical Coherence Tomography Angiography.

BACKGROUND/AIM: Probing brain tumor microvasculature holds significant importance in both basic cancer research and medical practice for tracking tumor development and assessing treatment outcomes. However, few imaging methods commonly used in clinics can noninvasively monitor the brain microvascular network at high precision and without exogenous contrast agents in vivo. The present study aimed to investigate the characteristics of microvasculature during brain tumor development in an orthotopic glioma mouse model. MATERIALS AND METHODS: An orthotopic glioma mouse model was established by surgical orthotopic implantation of U87-MG-luc cells into the mouse brain. Then, optical coherence tomography angiography (OCTA) was utilized to characterize the microvasculature progression within 14 days. RESULTS: The orthotopic glioma mouse model evaluated by bioluminescence imaging and MRI was successfully generated. As the tumor grew, the microvessels within the tumor area slowly decreased, progressing from the center to the periphery for 14 days. CONCLUSION: This study highlights the potential of OCTA as a useful tool to noninvasively visualize the brain microvascular network at high precision and without any exogenous contrast agents in vivo.

Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. METHODS: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. RESULTS: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. CONCLUSION: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

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.

Macrophage membrane-camouflaged nanoclusters of ultrasmall iron oxide nanoparticles for precision glioma theranostics.

Developing effective nanomedicines to cross the blood-brain barrier (BBB) for efficient glioma theranostics is still considered to be a challenging task. Here, we describe the development of macrophage membrane (MM)-coated nanoclusters (NCs) of ultrasmall iron oxide nanoparticles (USIO NPs) with dual pH- and reactive oxygen species (ROS)-responsivenesses for magnetic resonance (MR) imaging and chemotherapy/chemodynamic therapy (CDT) of orthotopic glioma. Surface citrate-stabilized USIO NPs were solvothermally synthesized, sequentially modified with ethylenediamine and phenylboronic acid, and cross-linked with gossypol to form gossypol-USIO NCs (G-USIO NCs), which were further coated with MMs. The prepared MM-coated G-USIO NCs (G-USIO@MM NCs) with a mean size of 99.9 nm display tumor microenvironment (TME)-responsive gossypol and Fe release to promote intracellular ROS production and glutathione consumption. With the MM-mediated BBB crossing and glioma targeting, the G-USIO@MM NCs can specifically inhibit orthotopic glioma in vivo through the gossypol-mediated chemotherapy and Fe-mediated CDT. Meanwhile, USIO NPs can be dissociated from the NCs under the TME, thus allowing for effective T1-weighted glioma MR imaging. The developed G-USIO@MM NCs with simple components and drug as a crosslinker are promising for glioma theranostics, and may be extended to tackle other cancer types.

Magnetic Field-Optimized Paramagnetic Nanoprobe for T2/T1 Switchable Histopathological-Level MRI.

Traditional magnetic resonance imaging (MRI) contrast agents (CAs) are a type of "always on" system that accelerates proton relaxation regardless of their enrichment region. This "always on" feature leads to a decrease in signal differences between lesions and normal tissues, hampering their applications in accurate and early diagnosis. Herein, we report a strategy to fabricate glutathione (GSH)-responsive one-dimensional (1-D) manganese oxide nanoparticles (MONPs) with improved T2 relaxivities and achieve effective T2/T1 switchable MRI imaging of tumors. Compared to traditional contrast agents with high saturation magnetization to enhance T2 relaxivities, 1-D MONPs with weak Ms effectively increase the inhomogeneity of the local magnetic field and exhibit obvious T2 contrast. The inhomogeneity of the local magnetic field of 1-D MONPs is highly dependent on their number of primary particles and surface roughness according to Landau-Lifshitz-Gilbert simulations and thus eventually determines their T2 relaxivities. Furthermore, the GSH responsiveness ensures 1-D MONPs with sensitive switching from the T2 to T1 mode in vitro and subcutaneous tumors to clearly delineate the boundary of glioma and metastasis margins, achieving precise histopathological-level MRI. This study provides a strategy to improve T2 relaxivity of magnetic nanoparticles and construct switchable MRI CAs, offering high tumor-to-normal tissue contrast signal for early and accurate diagnosis.

Metabolic habitat imaging with hemodynamic heterogeneity predicts individual progression-free survival in high-grade glioma.

AIM: We design a feasibility study to obtain a set of metabolic-hemodynamic habitats for tackling tumor spatial metabolic patterns with hemodynamic information. MATERIALS AND METHODS: Preoperative data from 69 high-grade gliomas (HGG) patients with subsequent histologic confirmation of HGG were prospectively collected (January 2016 to March 2020) after concurrent chemoradiotherapy (CCRT). Four vascular habitats were automatically segmented by multiparametric magnetic resonance imaging (MRI). The metabolic information, either at enhancing or edema tumor regions, was obtained by two neuroradiologists. The relative habitat volumes were used for weight estimation procedures for computing the coefficients of a linear regression model using weighted least squares (WLS) for metabolite semiquantifications (i.e. the Cho/NAA ratio and the Cho/Cr ratio) at vascular habitats. Multivariate Cox proportional hazard regression analyses are used to obtain the odds ratio (OR) and develop a nomogram using weighted estimators corresponding to each covariate derived from Cox regression coefficients. RESULTS: There was a strongly correlation between perfusion indexes and the Cho/Cr ratio (rCBV, r=0.71) or Cho/NAA ratio (rCBV, r=0.66) at high-angiogenic enhancing tumor habitats (HAT) habitat. Compared isocitrate dehydrogenase (IDH) mutation to their wild type, the IDH wild type had significantly decreased Cho/Cr ratio (IDH mutation: Cho/Cr ratio = 2.44 ± 0.33, IDH wildtype: Cho/Cr ratio = 2.66 ± 0.36, p=0.02) and Cho/NAA ratio (IDH mutation: Cho/Cr ratio = 4.59 ± 0.61, IDH wildtype: Cho/Cr ratio = 4.99 ± 0.66, p=0.022) at the HAT. The C-index for the median progression-free survival (PFS) prediction was 0.769 for the Cho/NAA nomogram and 0.747 for the Cho/Cr nomogram through 1000 bootstrapping validation. CONCLUSIONS: Our findings suggest that spatial metabolism combined with hemodynamic heterogeneity is associated with individual PFS to HGG patients post-CCRT.

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.

Abnormal expression of long non­coding RNA LINC01270 in glioma and its correlation with X­ray 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 chi­square 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.