Comparing spatial distributions of ALA-PpIX and indocyanine green in a whole pig brain glioma model using 3D fluorescence cryotomography.

Significance: ALA-PpIX and second-window indocyanine green (ICG) have been studied widely for guiding the resection of high-grade gliomas. These agents have different mechanisms of action and uptake characteristics, which can affect their performance as surgical guidance agents. Elucidating these differences in animal models that approach the size and anatomy of the human brain would help guide the use of these agents. Herein, we report on the use of a new pig glioma model and fluorescence cryotomography to evaluate the 3D distributions of both agents throughout the whole brain. Aim: We aim to assess and compare the 3D spatial distributions of ALA-PpIX and second-window ICG in a glioma-bearing pig brain using fluorescence cryotomography. Approach: A glioma was induced in the brain of a transgenic Oncopig via adeno-associated virus delivery of Cre-recombinase plasmids. After tumor induction, the pro-drug 5-ALA and ICG were administered to the animal 3 and 24 h prior to brain harvest, respectively. The harvested brain was imaged using fluorescence cryotomography. The fluorescence distributions of both agents were evaluated in 3D in the whole brain using various spatial distribution and contrast performance metrics. Results: Significant differences in the spatial distributions of both agents were observed. Indocyanine green accumulated within the tumor core, whereas ALA-PpIX appeared more toward the tumor periphery. Both ALA-PpIX and second-window ICG provided elevated tumor-to-background contrast (13 and 23, respectively). Conclusions: This study is the first to demonstrate the use of a new glioma model and large-specimen fluorescence cryotomography to evaluate and compare imaging agent distribution at high resolution in 3D.

Absolute Metabolite Quantification in Individuals with Glioma and Healthy Individuals Using Whole-Brain Three-dimensional MR Spectroscopic and Echo-planar Time-resolved Imaging.

BACKGROUND: MR spectroscopic imaging (MRSI) can be used to quantify an extended brain metabolic profile but is confounded by changes in tissue water levels due to disease. PURPOSE: To develop a fast absolute quantification method for metabolite concentrations combining whole-brain MRSI with echo-planar time-resolved imaging (EPTI) relaxometry in individuals with glioma and healthy individuals. MATERIALS AND METHODS: In this prospective study performed from August 2022 to August 2023, using internal water as concentration reference, the MRSI-EPTI quantification method was compared with the conventional method using population-average literature relaxation values. Healthy participants and participants with mutant IDH1 gliomas underwent imaging at 3 T with a 32-channel coil. Real-time navigated adiabatic spiral three-dimensional MRSI scans were acquired in approximately 8 minutes and reconstructed with a super-resolution pipeline to obtain brain metabolic images at 2.4-mm isotropic resolution. High-spatial-resolution multiparametric EPTI was performed in 3 minutes, with 1-mm isotropic resolution, to correct the relaxation and proton density of the water reference signal. Bland-Altman analysis and the Wilcoxon signed rank test were used to compare absolute quantifications from the proposed and conventional methods. RESULTS: Six healthy participants (four male; mean age, 37 years ± 11 [SD]) and nine participants with glioma (six male; mean age, 41 years ± 15; one with wild-type IDH1 and eight with mutant IDH1) were included. In healthy participants, there was good agreement (+4% bias) between metabolic concentrations derived using the two methods, with a CI of plus or minus 26%. In participants with glioma, there was large disagreement between the two methods (+39% bias) and a CI of plus or minus 55%. The proposed quantification method improved tumor contrast-to-noise ratio (median values) for total N-acetyl-aspartate (EPTI: 0.541 [95% CI: 0.217, 0.910]; conventional: 0.484 [95% CI: 0.199, 0.823]), total choline (EPTI: 1.053 [95% CI: 0.681, 1.713]; conventional: 0.940 [95% CI: 0.617, 1.295]), and total creatine (EPTI: 0.745 [95% CI: 0.628, 0.909]; conventional: 0.553 [95% CI: 0.444, 0.828]) (P = .03 for all). CONCLUSION: The whole-brain MRSI-EPTI method provided fast absolute quantification of metabolic concentrations with individual-specific corrections at 2.4-mm isotropic resolution, yielding concentrations closer to the true value in disease than the conventional literature-based corrections. © RSNA, 2024 Supplemental material is available for this article.

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. METHODS: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. RESULTS: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. CONCLUSION: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various sizes and shapes, making their manual detection from magnetic resonance imaging (MRI) scans a subjective and challenging task for healthcare professionals, hence necessitating automated solutions. This study investigates the potential of deep learning, specifically the DenseNet architecture, to automate brain tumor classification, aiming to enhance accuracy and generalizability for clinical applications. We utilized the Figshare brain tumor dataset, comprising 3,064 T1-weighted contrast-enhanced MRI images from 233 patients with three prevalent tumor types: meningioma, glioma, and pituitary tumor. Four pre-trained deep learning models-ResNet, EfficientNet, MobileNet, and DenseNet-were evaluated using transfer learning from ImageNet. DenseNet achieved the highest test set accuracy of 96%, outperforming ResNet (91%), EfficientNet (91%), and MobileNet (93%). Therefore, we focused on improving the performance of the DenseNet, while considering it as base model. To enhance the generalizability of the base DenseNet model, we implemented a fine-tuning approach with regularization techniques, including data augmentation, dropout, batch normalization, and global average pooling, coupled with hyperparameter optimization. This enhanced DenseNet model achieved an accuracy of 97.1%. Our findings demonstrate the effectiveness of DenseNet with transfer learning and fine-tuning for brain tumor classification, highlighting its potential to improve diagnostic accuracy and reliability in clinical settings.

Use of artificial intelligence and radio genomics in neuroradiology and the future of brain tumour imaging and surgical planning in low- and middleincome countries.

Brain tumour diagnosis involves assessing various radiological and histopathological parameters. Imaging modalities are an excellent resource for disease monitoring. However, manual inspection of imaging is laborious, and performance varies depending on expertise. Artificial Intelligence (AI) driven solutions a non-invasive and low-cost technology for diagnostics compared to surgical biopsy and histopathological diagnosis. We analysed various machine learning models reported in the literature and assess its applicability to improve neuro-oncological management. A scoping review of 47 full texts published in the last 3 years pertaining to the use of machine learning for the management of different types of gliomas where radiomics and radio genomic models have proven to be useful. Use of AI in conjunction with other factors can result in improving overall neurooncological management within LMICs. AI algorithms can evaluate medical imaging to aid in the early detection and diagnosis of brain tumours. This is especially useful where AI can deliver reliable and efficient screening methods, allowing for early intervention and treatment.

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

BACKGROUND: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. METHODS: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13). RESULTS: The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2. CONCLUSION: The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

Prediction of Glioma enhancement pattern using a MRI radiomics-based model.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologically-proven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid attenuated inversion recovery images, which were divided into enhancing and non-enhancing groups. Predictive radiomics models based on machine learning with 6 different classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation cohort (n = 99). Receiver-operator characteristic curve was used to assess the predictive performance of these radiomics models. This study demonstrated that the radiomics model comprising of 15 features using the Gaussian process as a classifier had the highest predictive performance in both the training cohort and the internal validation cohort, with the area under the curve being 0.88 and 0.80, respectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.82, 0.76 and 0.96, respectively, in the external validation cohort. This study suggests that the T2-FLAIR-based machine learning radiomics model can accurately predict enhancement pattern of glioma.

[An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].

OBJECTIVE: To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG). METHODS: We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model. RESULTS: For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%. CONCLUSION: The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.

High-throughput fluorescence lifetime imaging flow cytometry.

Flow cytometry is a vital tool in biomedical research and laboratory medicine. However, its accuracy is often compromised by undesired fluctuations in fluorescence intensity. While fluorescence lifetime imaging microscopy (FLIM) bypasses this challenge as fluorescence lifetime remains unaffected by such fluctuations, the full integration of FLIM into flow cytometry has yet to be demonstrated due to speed limitations. Here we overcome the speed limitations in FLIM, thereby enabling high-throughput FLIM flow cytometry at a high rate of over 10,000 cells per second. This is made possible by using dual intensity-modulated continuous-wave beam arrays with complementary modulation frequency pairs for fluorophore excitation and acquiring fluorescence lifetime images of rapidly flowing cells. Moreover, our FLIM system distinguishes subpopulations in male rat glioma and captures dynamic changes in the cell nucleus induced by an anti-cancer drug. FLIM flow cytometry significantly enhances cellular analysis capabilities, providing detailed insights into cellular functions, interactions, and environments.

MR-guided focused ultrasound in pediatric neurosurgery: current insights, technical challenges, and lessons learned from 45 treatments at Children's National Hospital.

OBJECTIVE: MR-guided focused ultrasound (MRgFUS) is an evolving technology with numerous present and potential applications in pediatric neurosurgery. The aim of this study was to describe the use of MRgFUS, technical challenges, complications, and lessons learned at a single children's hospital. METHODS: A retrospective analysis was performed of a prospectively collected database of all pediatric patients undergoing investigational use of MRgFUS for treatment of various neurosurgical pathologies at Children's National Hospital. Treatment details, clinical workflow, and standard operating procedures are described. Patient demographics, procedure duration, and complications were obtained through a chart review of anesthesia and operative reports. RESULTS: In total, 45 MRgFUS procedures were performed on 14 patients for treatment of diffuse intrinsic pontine glioma (n = 12), low-grade glioma (n = 1), or secondary dystonia (n = 1) between January 2022 and April 2024. The mean age at treatment was 9 (range 5-22) years, and 64% of the patients were male. With increased experience, the total anesthesia time, sonication time, and change in core body temperature during treatment all significantly decreased. Complications affected 4.4% of patients, including 1 case of scalp edema and 1 patient with a postprocedure epidural hematoma. Device malfunction requiring abortion of the procedure occurred in 1 case (2.2%). Technical challenges related to transducer malfunction and sonication errors occurred in 6.7% and 11.1% of cases, respectively, all overcome by subsequent user modifications. CONCLUSIONS: The authors describe the largest series on MRgFUS technical aspects in pediatric neurosurgery at a single institution, comprising 45 total treatments. This study emphasizes potential technical challenges and provides valuable insights into the nuances of its application in pediatric patients.

[Clinicopathological and molecular characteristics of pediatric gliomas: analysis of 111 cases].

Objective: To summarize the clinical, pathological and molecular characteristics of various types of pediatric glioma, and to explore the differences in the morphology and clinical significance among various types of pediatric glioma. Methods: Based on the fifth edition of the World Health Organization classification of central nervous system tumors, this study classified or reclassified 111 pediatric gliomas that were diagnosed at Guangzhou Medical University Affiliated Women and Children's Medical Center from January 2020 to June 2023. The clinical manifestations, imaging findings, histopathology, and molecular characteristics of these tumors were analyzed. Relevant literature was also reviewed. Results: The 111 patients with pediatric glioma included 56 males and 55 females, with the age ranging from 10 days to 13 years (average age, 5.5 years). Clinically, manifestations presented from 5 days to 8 years before the diagnosis, including epilepsy in 16 cases, increased intracranial pressure in 48 cases and neurological impairment in 66 cases. MRI examinations revealed tumor locations as supratentorial in 43 cases, infratentorial in 65 cases, and spinal cord in 3 cases. There were 73 cases presented with a solid mass and 38 cases with cystic-solid lesions. The largest tumor diameter ranged from 1.4 to 10.6 cm. Among the 111 pediatric gliomas, there were 6 cases of pediatric diffuse low-grade glioma (pDLGG), 63 cases of circumscribed astrocytoma glioma (CAG), and 42 cases of pediatric diffuse high-grade glioma (pDHGG). Patients with pDLGG and CAG were younger than those with pDHGG. The incidence of pDLGG and CAG was significantly lower in the midline of the infratentorial region compared to that of pDHGG. They were more likely to be completely resected surgically. The pDLGG and CAG group included 4 cases of pleomorphic xanthoastrocytoma, showing histological features of high-grade gliomas. Among the high-grade gliomas, 13 cases were diffuse midline gliomas and also showed histological features of low-grade glioma. Immunohistochemical studies of H3K27M, H3K27ME3, p53, ATRX, BRAF V600E, and Ki-67 showed significant differences between the pDLGG and CAG group versus the pDHGG group (P<0.01). Molecular testing revealed that common molecular variations in the pDLGG and CAG group were KIAA1549-BRAF fusion and BRAF V600E mutation, while the pDHGG group frequently exhibited mutations in HIST1H3B and H3F3A genes, 1q amplification, and TP53 gene mutations. With integrated molecular testing, 2 pathological diagnoses were revised, and the pathological subtypes of 35.3% (12/34) of the pediatric gliomas that could not be reliably classified by histology were successfully classified. Conclusions: There are significant differences in clinical manifestations, pathological characteristics, molecular variations, and prognosis between the pDLGG, CAG and pDHGG groups. The integrated diagnosis combining histology and molecular features is of great importance for the accurate diagnosis and treatment of pediatric gliomas.

Optimization and validation of echo times of point-resolved spectroscopy for cystathionine detection in gliomas.

BACKGROUND: Cystathionine accumulates selectively in 1p/19q-codeleted gliomas, and can serve as a possible noninvasive biomarker. This study aims to optimize the echo time (TE) of point-resolved spectroscopy (PRESS) for cystathionine detection in gliomas, and evaluate the diagnostic accuracy of PRESS for 1p/19q-codeletion identification. METHODS: The TE of PRESS was optimized with numerical and phantom analysis to better resolve cystathionine from the overlapping aspartate multiplets. The optimized and 97 ms TE PRESS were then applied to 84 prospectively enrolled patients suspected of glioma or glioma recurrence to examine the influence of aspartate on cystathionine quantification by fitting the spectra with and without aspartate. The diagnostic performance of PRESS for 1p/19q-codeleted gliomas were assessed. RESULTS: The TE of PRESS was optimized as (TE1, TE2) = (17 ms, 28 ms). The spectral pattern of cystathionine and aspartate were consistent between calculation and phantom. The mean concentrations of cystathionine in vivo fitting without aspartate were significantly higher than those fitting with full basis-set for 97 ms TE PRESS (1.97 ± 2.01 mM vs. 1.55 ± 1.95 mM, p < 0.01), but not significantly different for 45 ms method (0.801 ± 1.217 mM and 0.796 ± 1.217 mM, p = 0.494). The cystathionine concentrations of 45 ms approach was better correlated with those of edited MRS than 97 ms counterparts (r = 0.68 vs. 0.49, both p < 0.01). The sensitivity and specificity for discriminating 1p/19q-codeleted gliomas were 66.7% and 73.7% for 45 ms method, and 44.4% and 52.5% for 97 ms method, respectively. CONCLUSION: The 45 ms TE PRESS yields more precise cystathionine estimates than the 97 ms method, and is anticipated to facilitate noninvasive diagnosis of 1p/19q-codeleted gliomas, and treatment response monitoring in those patients. Medium diagnostic performance of PRESS for 1p/19q-codeleted gliomas were observed, and warrants further investigations.

Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies.

Significance: Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. Aim: We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) setup, named HyperProbe1, that can provide in situ, fast biochemical analysis, and mapping of fresh surgical tissue samples, right after excision, and without the need for fixing, staining nor compromising the integrity of the tissue properties. Approach: HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to 5 µ m ), widefield images ( 0.9 × 0.9 mm 2 ) of the surgical samples, where each pixel is associated with a complete spectrum. Results: The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-( HbO 2 ) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas. Conclusions: A proof of concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas.

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (- 0.017) and non-uniformity normalized (- 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.

Preparation and Preclinical Evaluation of 18F-Labeled Olutasidenib Derivatives for Non-Invasive Detection of Mutated Isocitrate Dehydrogenase 1 (mIDH1).

Mutations of isocitrate dehydrogenase 1 (IDH1) are key biomarkers for glioma classification, but current methods for detection of mutated IDH1 (mIDH1) require invasive tissue sampling and cannot be used for longitudinal studies. Positron emission tomography (PET) imaging with mIDH1-selective radioligands is a promising alternative approach that could enable non-invasive assessment of the IDH status. In the present work, we developed efficient protocols for the preparation of four 18F-labeled derivatives of the mIDH1-selective inhibitor olutasidenib. All four probes were characterized by cellular uptake studies with U87 glioma cells harboring a heterozygous IDH1 mutation (U87-mIDH) and the corresponding wildtype cells (U87-WT). In addition, the most promising probe was evaluated by PET imaging in healthy mice and mice bearing subcutaneous U87-mIDH and U87-WT tumors. Although all four probes inhibited mIDH1 with variable potencies, only one of them ([18F]mIDH-138) showed significantly higher in vitro uptake into U87-mIDH compared to U87-WT cells. In addition, PET imaging with [18F]mIDH-138 in mice demonstrated good in vivo stability and low non-specific uptake of the probe, but also revealed significantly higher uptake into U87-WT compared to U87-mIDH tumors. Finally, application of a two-tissue compartment model (2TCM) to the PET data indicated that preferential tracer uptake into U87-WT tumors results from higher specific binding rather than from differences in tracer perfusion. In conclusion, these results corroborate recent findings that mIDH1-selective inhibition may not directly correlate with mIDH1-selective target engagement and indicate that in vivo engagement of wildtype and mutated IDH1 may be governed by factors that are not faithfully reproduced by in vitro assays, both of which could complicate development of PET probes.

Molecular imaging with 68Ga-PSMA PET/CT in high grade glioma: A biomarker for tumour neo-angiogenesis.

There are several promising radiotracers used for both staging and restaging of primary and recurrent brain tumours based on various mechanisms of tracer localization in tumour cells. 68Ga-PSMA PET has extremely low background uptake in normal brain tissue and consequently high tumour-to-brain ratio making it a promising imaging radiotracer for gliomas. 68Ga-PSMA demonstrates utility in evaluating high grade glioma during both initial workup or when suspecting recurrence. Herein the authors evaluate the role of this imaging modality and the potential future it holds in the management of high grade gliomas.

Focused ultrasound blood-brain barrier disruption in high-grade gliomas: Scoping review of clinical studies.

BACKGROUND: This scoping review aims to comprehensively review the available literature on the safety and efficacy of focused ultrasound (FUS) for blood-brain barrier disruption (BBBD) in patients with high-grade gliomas, including glioblastoma (GBM). High-grade gliomas pose significant challenges in neuro-oncology due to their aggressiveness and intricate location, often limiting the efficacy of traditional treatments. FUS offers a promising approach by transiently disrupting the blood-brain barrier, thereby facilitating enhanced drug delivery to tumor cells while minimizing systemic side effects. METHODS: A scoping review adhering to PRISMA guidelines was conducted to explore the literature on FUS-induced BBBD in glioma patients. PubMed and Embase databases were searched from inception to April 2024 using defined keywords. Original clinical studies focusing on FUS for BBBD in gliomas were included. Two reviewers independently screened records, with conflicts resolved by a third reviewer. Data extraction and quality assessment were performed accordingly. RESULTS: A total of 1,310 studies were initially identified, resulting in nine eligible studies after screening and selection. These studies, published between 2016 and 2024, included 106 patients (39.6 % female) with ages ranging from 29 to 80 years. Recurrent GBM was the most common diagnosis (100 patients), with other diagnoses including anaplastic astrocytoma, diffuse infiltrating glioma, and oligodendroglioma. Various FUS devices and microbubble contrast agents were employed across the studies. Safety and efficacy were assessed in both experimental and clinical settings, with no significant adverse events reported during BBBD procedures. Notably, BBBD facilitated enhanced drug delivery to tumor tissue, demonstrating potential therapeutic benefits. CONCLUSION: Studies investigating BBBD using FUS demonstrate promising outcomes in experimental and clinical settings. BBBD procedures in patients with malignant gliomas and recurrent GBM show safety and successful enhancement of drug delivery potential. Overall, FUS-mediated BBBD emerges as a safe and feasible approach for improving therapeutic outcomes in brain tumor patients, warranting further clinical exploration and optimization.

Visualization of Brain Tumors with Infrared-Labeled Aptamers for Fluorescence-Guided Surgery.

Gliomas remain challenging brain tumors to treat due to their infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful resection. This study introduces an innovative intraoperative visualization method utilizing surgical fluorescence microscopy to precisely locate tumor cell dissemination. Here, the focus is on the development of a novel contrasting agent (IR-Glint) for intraoperative visualization of human glial tumors comprising infrared-labeled Glint aptamers. The specificity of IR-Glint is assessed using flow cytometry and microscopy on primary cell cultures. In vivo effectiveness is studied on mouse and rabbit models, employing orthotopic xenotransplantation of human brain gliomas with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. The experiments validate the potential of IR-Glint for the intraoperative visualization of gliomas using infrared imaging. IR-Glint penetrates the blood-brain barrier and can be used for both intravenous and surface applications, allowing clear visualization of the tumor. The surface application directly to the brain reduces the dosage required and mitigates potential toxic effects on the patient. The research shows the potential of infrared dye-labeled aptamers for accurately visualizing glial tumors during brain surgery. This novel aptamer-assisted fluorescence-guided surgery (AptaFGS) may pave the way for future advancements in the field of neurosurgery.