RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Imagenología Tridimensional , Verde de Indocianina , Animales , Verde de Indocianina/farmacocinética , Verde de Indocianina/química , Porcinos , Neoplasias Encefálicas/diagnóstico por imagen , Glioma/diagnóstico por imagen , Glioma/patología , Imagenología Tridimensional/métodos , Ácido Aminolevulínico/farmacocinética , Encéfalo/diagnóstico por imagen , Imagen Óptica/métodos , Modelos Animales de Enfermedad

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Aprendizaje Profundo , Imagen por Resonancia Magnética , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/clasificación , Imagen por Resonancia Magnética/métodos , Meningioma/diagnóstico por imagen , Meningioma/patología , Glioma/diagnóstico por imagen , Glioma/patología , Glioma/clasificación , Masculino , Femenino , Neoplasias Hipofisarias/diagnóstico por imagen , Neoplasias Hipofisarias/patología , Neoplasias Hipofisarias/clasificación

RESUMEN

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.

Asunto(s)

Procedimientos Neuroquirúrgicos , Humanos , Niño , Masculino , Femenino , Adolescente , Preescolar , Procedimientos Neuroquirúrgicos/métodos , Estudios Retrospectivos , Adulto Joven , Hospitales Pediátricos , Glioma/cirugía , Glioma/diagnóstico por imagen , Imagen por Resonancia Magnética/métodos , Neoplasias del Tronco Encefálico/cirugía , Neoplasias del Tronco Encefálico/diagnóstico por imagen , Distonía/cirugía , Distonía/diagnóstico por imagen

RESUMEN

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.

Asunto(s)

Citometría de Flujo , Glioma , Citometría de Flujo/métodos , Animales , Ratas , Glioma/diagnóstico por imagen , Glioma/patología , Glioma/metabolismo , Masculino , Microscopía Fluorescente/métodos , Línea Celular Tumoral , Imagen Óptica/métodos , Humanos , Núcleo Celular/metabolismo , Ensayos Analíticos de Alto Rendimiento/métodos , Colorantes Fluorescentes/química

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Imagen por Resonancia Magnética , Humanos , Glioma/diagnóstico por imagen , Glioma/patología , Imagen por Resonancia Magnética/métodos , Estudios Retrospectivos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Algoritmos , Clasificación del Tumor , Curva ROC , Sensibilidad y Especificidad

RESUMEN

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.

Asunto(s)

Inteligencia Artificial , Neoplasias Encefálicas , Países en Desarrollo , Neuroimagen , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neuroimagen/métodos , Aprendizaje Automático , Glioma/diagnóstico por imagen , Genómica/métodos

Asunto(s)

Imagen por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Glioma/diagnóstico por imagen , Glioma/patología , Glioma del Nervio Óptico/diagnóstico por imagen

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Imágenes Hiperespectrales , Humanos , Imágenes Hiperespectrales/métodos , Biopsia , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Glioma/diagnóstico por imagen , Glioma/patología , Glioblastoma/diagnóstico por imagen , Glioblastoma/patología , Diseño de Equipo , Procesamiento de Imagen Asistido por Computador/métodos , Imagenología Tridimensional/métodos , Encéfalo/diagnóstico por imagen , Encéfalo/patología

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Imagen de Difusión por Resonancia Magnética , Glioma , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Imagen de Difusión por Resonancia Magnética/métodos , Femenino , Masculino , Persona de Mediana Edad , Adulto , Anciano , Glioma/diagnóstico por imagen , Glioma/patología , Diagnóstico Diferencial , Clasificación del Tumor , Adulto Joven , Linfoma/diagnóstico por imagen , Linfoma/patología , Linfoma/diagnóstico , Meningioma/diagnóstico por imagen , Meningioma/patología , Curva ROC , Papiloma del Plexo Coroideo/diagnóstico por imagen , Papiloma del Plexo Coroideo/patología , Sensibilidad y Especificidad , Estudios Retrospectivos , Adolescente

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Aprendizaje Automático , Imagen por Resonancia Magnética , Humanos , Glioma/diagnóstico por imagen , Glioma/patología , Imagen por Resonancia Magnética/métodos , Femenino , Persona de Mediana Edad , Masculino , Estudios Retrospectivos , Neoplasias Encefálicas/diagnóstico por imagen , Adulto , Curva ROC , Valor Predictivo de las Pruebas , Anciano , Medios de Contraste , Radiómica

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Isocitrato Deshidrogenasa , Antígeno Ki-67 , Mutación , Máquina de Vectores de Soporte , Humanos , Isocitrato Deshidrogenasa/genética , Glioma/diagnóstico por imagen , Glioma/genética , Glioma/metabolismo , Antígeno Ki-67/metabolismo , Antígeno Ki-67/genética , Persona de Mediana Edad , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Masculino , Estudios Retrospectivos , Adulto , Anciano , Imagen de Difusión por Resonancia Magnética/métodos , Imagen Multimodal , Adulto Joven , Imagen por Resonancia Magnética/métodos , Curva ROC , Medios de Contraste

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Glioma , Humanos , Niño , Glioma/patología , Glioma/genética , Glioma/diagnóstico por imagen , Femenino , Preescolar , Masculino , Adolescente , Lactante , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/diagnóstico por imagen , Imagen por Resonancia Magnética , Mutación , Recién Nacido , Astrocitoma/genética , Astrocitoma/patología , Astrocitoma/diagnóstico por imagen , Proteínas Proto-Oncogénicas B-raf/genética , Proteína p53 Supresora de Tumor/genética , Proteína p53 Supresora de Tumor/metabolismo

RESUMEN

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.

Asunto(s)

Neoplasias Encefálicas , Cistationina , Glioma , Humanos , Glioma/diagnóstico por imagen , Masculino , Cistationina/análisis , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Persona de Mediana Edad , Adulto , Estudios Prospectivos , Fantasmas de Imagen , Anciano , Espectroscopía de Resonancia Magnética/métodos , Adulto Joven , Biomarcadores de Tumor/análisis , Ácido Aspártico/análogos & derivados , Ácido Aspártico/análisis

RESUMEN

PURPOSE: Gliomas account for 75 % of primary malignant CNS tumors. High-grade glioma (CNS WHO grades 3 and 4) have an unfavorable treatment response and poor outcome. CXCR4 is a G protein-coupled receptor that plays an important part in the signaling pathway between cancer cells and tumor microenvironment. CXCR4 overexpression has been shown in a variety of cancers. In this study, we evaluate the potential value of [68Ga]Ga-Pentixafor as a PET/CT CXCR4-probe for in vivo assessment of CXCR4 expression in patients with high-grade glioma and its correlation with tumor grade. MATERIALS AND METHODS: [68Ga]Ga-CXCR4 PET/CT was performed in the prospective single-center study in treatment-naïve biopsy-proven patients with high-grade glioma. The acquired images were analyzed qualitatively and semi-quantitatively. RESULT: A total of 26 patients (mean age: 53.3±14.4 years, 11 women, 15 men) were enrolled. CNS WHO grade 3 pathology was seen in 19 % (5/26) of the sample. The patient-based sensitivity of 68Ga-CXCR4 was 96.2 %. Overall, 28 pathologic lesions were detected, leading to a lesion-based sensitivity of 96.4 %. The median (IQR) SUVmax of grade 4 lesions was substantially greater than the grade 3(3.03(2.5-3.7) vs. 1.51(1.2-1.8), p = 0.0145).). The highest tracer activity of organs -beside bladder as the main excretion reservoir-was in lymphoid tissue of Waldeyer's ring (mean SUVmax: 7.41), and spleen (mean SUVmax: 6.62). CONCLUSION: In conclusion, this new application for [68Ga]Ga-Pentixafor PET tracer exhibits excellent visual and semi-quantitative diagnostic properties. Further studies are warranted.

Asunto(s)

Neoplasias Encefálicas , Glioma , Tomografía Computarizada por Tomografía de Emisión de Positrones , Radiofármacos , Receptores CXCR4 , Humanos , Receptores CXCR4/metabolismo , Femenino , Masculino , Glioma/diagnóstico por imagen , Glioma/metabolismo , Glioma/patología , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Persona de Mediana Edad , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/metabolismo , Radiofármacos/farmacocinética , Estudios Prospectivos , Radioisótopos de Galio , Clasificación del Tumor , Sensibilidad y Especificidad , Péptidos Cíclicos , Adulto , Anciano , Complejos de Coordinación

RESUMEN

OBJECTIVES: To discriminate between post-treatment changes and tumor recurrence in patients affected by glioma undergoing surgery and chemoradiation with a new enhancing lesion is challenging. We aimed to evaluate the role of ASL, DSC, DCE perfusion MRI, and 18F-DOPA PET/CT in distinguishing tumor recurrence from post-treatment changes in patients with glioma. MATERIALS AND METHODS: We prospectively enrolled patients with treated glioma (surgery plus chemoradiation) and a new enhancing lesion doubtful for recurrence or post-treatment changes. Each patient underwent a 1.5T MRI examination, including ASL, DSC, and DCE PWI, and an 18F-DOPA PET/CT examination. For each lesion, we measured ASL-derived CBF and normalized CBF, DSC-derived rCBV, DCE-derived Ktrans, Vp, Ve, Kep, and PET/CT-derived SUV maximum. Clinical and radiological follow-up determined the diagnosis of tumor recurrence or post-treatment changes. RESULTS: We evaluated 29 lesions (5 low-grade gliomas and 24 high-grade gliomas); 14 were malignancies, and 15 were post-treatment changes. CBF ASL, nCBF ASL, rCBV DSC, and PET SUVmax were associated with tumor recurrence from post-treatment changes in patients with glioma through an univariable logistic regression. Whereas the multivariable logistic regression results showed only nCBF ASL (p = 0.008) was associated with tumor recurrence from post-treatment changes in patients with glioma with OR = 22.85, CI95%: (2.28-228.77). CONCLUSION: In our study, ASL was the best technique, among the other two MRI PWI and the 18F-DOPA PET/CT PET, in distinguishing disease recurrence from post-treatment changes in treated glioma.

Asunto(s)

Neoplasias Encefálicas , Dihidroxifenilalanina , Glioma , Recurrencia Local de Neoplasia , Tomografía Computarizada por Tomografía de Emisión de Positrones , Radiofármacos , Humanos , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Masculino , Glioma/diagnóstico por imagen , Glioma/terapia , Recurrencia Local de Neoplasia/diagnóstico por imagen , Femenino , Persona de Mediana Edad , Estudios Prospectivos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/terapia , Adulto , Dihidroxifenilalanina/análogos & derivados , Anciano , Diagnóstico Diferencial , Imagen por Resonancia Magnética/métodos , Medios de Contraste

RESUMEN

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Asunto(s)

Neoplasias Encefálicas , Imagen de Difusión Tensora , Glioma , Aprendizaje Automático , Humanos , Glioma/diagnóstico por imagen , Glioma/patología , Imagen de Difusión Tensora/métodos , Masculino , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Persona de Mediana Edad , Adulto , Agua , Clasificación del Tumor , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Anciano

RESUMEN

The study highlights that diffuse glioma, a prevalent type of brain tumor, affect approximately 100,000 individuals worldwide each year. IDH-mutant astrocytoma and oligodendrogliomas typically have a more favorable prognosis compared to IDH-wildtype glioblastomas. However, many IDH-mutant astrocytoma has the potential to progress to grade 4 glioblastomas, leading to a less favorable prognosis. In a recent investigation, Shumpei Onishi et al. examined the T2-FLAIR mismatch sign as a possible imaging biomarker for assessing CDKN2A status in non-enhancing IDH-mutant astrocytoma. The findings indicate that the T2-FLAIR mismatch sign is linked to CDKN2A-intact astrocytoma, providing a valuable tool for diagnostic and prognostic purposes. Additionally, the use of Indocyanine Green (ICG) for real-time visualization during neurosurgical procedures demonstrates potential, though it may have limitations in specificity. While these advancements offer promise in glioma management, there remains a critical need for larger, standardized studies to validate these findings and further improve treatment outcomes.

Asunto(s)

Astrocitoma , Neoplasias Encefálicas , Glioma , Isocitrato Deshidrogenasa , Mutación , Humanos , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/diagnóstico por imagen , Isocitrato Deshidrogenasa/genética , Astrocitoma/genética , Astrocitoma/diagnóstico por imagen , Glioma/diagnóstico por imagen , Glioma/genética , Imagen por Resonancia Magnética/métodos , Biomarcadores de Tumor/genética , Pronóstico

RESUMEN

OBJECTIVE: Sporadic optic chiasmatic-hypothalamic gliomas (OCHGs), though histologically low-grade tumors, manifest as aggressive neoplasms radiologically, leading to difficulty in diagnosis. Molecular alterations of the BRAF gene are detectable in a majority of sporadic OCHGs. The purpose of our study was to elucidate the characteristic imaging features of sporadic OCHGs and to investigate whether imaging phenotypes could potentially correlate with specific BRAF gene alterations associated with these tumors. METHODS: We retrospectively reviewed baseline magnetic resonance (MR) images and medical records of 26 patients with histopathologically proven sporadic OCHGs. MR imaging (MRI) features were systematically evaluated. Statistical analysis was performed to determine whether there was a significant association between imaging findings and BRAF molecular alterations. RESULTS: Twenty-two cases (84.6%) presented with solid-cystic masses, while four (15.4%) presented with purely solid lesions. In all 26 cases, the solid component revealed central necrosis; there was minimal necrosis in 11 cases (42.3%), moderate in 8 (30.7%), and marked in 7 (26.9%). The presence of multiple cysts (>4) and minimal necrosis showed a significant association with BRAFV600E mutation (P < 0.005). Marked necrosis in the solid component significantly correlated with BRAF wild genotype (P < 0.001). The presence of a single peripheral cyst significantly correlated with BRAF fusion (P = 0.04). CONCLUSION: Sporadic OCHGs have a distinctive appearance on imaging. The solid-cystic composition coupled with varying degrees of central necrosis are clues to the radiological diagnosis of this entity and can facilitate early recognition in clinical practice. Imaging could potentially serve as a non-invasive predictor of the BRAF alteration status, thereby serving as a prognostic marker and guiding personalized management.

Asunto(s)

Imagen por Resonancia Magnética , Proteínas Proto-Oncogénicas B-raf , Humanos , Proteínas Proto-Oncogénicas B-raf/genética , Femenino , Masculino , Estudios Retrospectivos , Adulto , Neoplasias Hipotalámicas/genética , Neoplasias Hipotalámicas/diagnóstico por imagen , Neoplasias Hipotalámicas/patología , Mutación , Glioma/genética , Glioma/diagnóstico por imagen , Glioma/patología , Adolescente , Niño , Persona de Mediana Edad , Quiasma Óptico/diagnóstico por imagen , Quiasma Óptico/patología , Adulto Joven , Preescolar , Glioma del Nervio Óptico/genética , Glioma del Nervio Óptico/diagnóstico por imagen , Glioma del Nervio Óptico/patología

RESUMEN

Diffuse intrinsic pontine glioma (DIPG) is a rare childhood malignancy with poor prognosis. There are no effective treatment options other than external beam therapy. We conducted a pilot, first-in-human study using 124I-omburtamab imaging and theranostics as a therapeutic approach using a localized convection-enhanced delivery (CED) technique for administering radiolabeled antibody. We report the detailed pharmacokinetics and dosimetry results of intratumoral delivery of 124I-omburtamab. Methods: Forty-five DIPG patients who received 9.0-370.7 MBq of 124I-omburtamab intratumorally via CED underwent serial brain and whole-body PET/CT imaging at 3-5 time points after injection within 4, 24-48, 72-96, 120-144, and 168-240 h from the end of infusion. Serial blood samples were obtained for kinetic analysis. Whole-body, blood, lesion, and normal-tissue activities were measured, kinetic parameters (uptake and clearance half-life times) estimated, and radiation-absorbed doses calculated using the OLINDA software program. Results: All patients showed prominent activity within the lesion that was retained over several days and was detectable up to the last time point of imaging, with a mean 124I residence time in the lesion of 24.9 h and dose equivalent of 353 ± 181 mSv/MBq. Whole-body doses were low, with a dose equivalent of 0.69 ± 0.28 mSv/MBq. Systemic distribution and activities in normal organs and blood were low. Radiation dose to blood was very low, with a mean value of 0.27 ± 0.21 mGy/MBq. Whole-body clearance was monoexponential with a mean biologic half-life of 62.7 h and an effective half-life of 37.9 h. Blood clearance was biexponential, with a mean biologic half-life of 22.2 h for the rapid α phase and 155 h for the slower ß phase. Conclusion: Intratumoral CED of 124I-omburtamab is a novel theranostics approach in DIPG. It allows for delivery of high radiation doses to the DIPG lesions, with high lesion activities and low systemic activities and high tumor-to-normal-tissue ratios and achieving a wide safety margin. Imaging of the actual therapeutic administration of 124I-omburtamab allows for direct estimation of the therapeutic lesion and normal-tissue-absorbed doses.

Asunto(s)

Neoplasias del Tronco Encefálico , Glioma Pontino Intrínseco Difuso , Radioisótopos de Yodo , Radiometría , Humanos , Masculino , Femenino , Niño , Neoplasias del Tronco Encefálico/diagnóstico por imagen , Neoplasias del Tronco Encefálico/radioterapia , Glioma Pontino Intrínseco Difuso/diagnóstico por imagen , Glioma Pontino Intrínseco Difuso/radioterapia , Preescolar , Adolescente , Convección , Tomografía Computarizada por Tomografía de Emisión de Positrones , Glioma/diagnóstico por imagen , Glioma/radioterapia , Distribución Tisular , Lactante , Adulto Joven

RESUMEN

Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images. The method overcomes the limitations of H&E staining by capturing tissue information at different wavelengths, providing comprehensive and detailed tissue composition information as the realistic H&E staining. In comparison with various generator structures, the Unet exhibits substantial overall advantages, as evidenced by a mean structure similarity index measure (SSIM) of 0.7731 and a peak signal-to-noise ratio (PSNR) of 23.3120, as well as the shortest training and inference time. A comprehensive software system for virtual H&E staining, which integrates CCD control, microscope control, and virtual H&E staining technology, is developed to facilitate fast intraoperative imaging, promote disease diagnosis, and accelerate the development of medical automation. The platform reconstructs large-scale virtual H&E staining images of gliomas at a high speed of 3.81 mm2/s. This innovative approach will pave the way for a novel, expedited route in histological staining.

Asunto(s)

Aprendizaje Profundo , Glioma , Glioma/diagnóstico por imagen , Glioma/patología , Glioma/metabolismo , Humanos , Coloración y Etiquetado/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Imágenes Hiperespectrales/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Eosina Amarillenta-(YS)/química , Hematoxilina/química