Study on the Relationship Between MRI Functional Imaging and Multiple Immunohistochemical Features of Glioma: A Noninvasive and More Precise Glioma Management.

Objective: To investigate the performance of diffusion-tensor imaging (DTI) and hydrogen proton magnetic resonance spectroscopy (1H-MRS) parameters in predicting the immunohistochemistry (IHC) biomarkers of glioma. Methods: Patients with glioma confirmed by pathology from March 2015 to September 2019 were analyzed, the preoperative DTI and 1H-MRS images were collected, apparent diffusion coefficient (ADC) and fractional anisotropy (FA), in the lesion area were measured, the relative values relative ADC (rADC) and relative FA (rFA) were obtained by the ratio of them in the lesion area to the contralateral normal area. The peak of each metabolite in the lesion area of 1H-MRS image: N-acetylaspartate (NAA), choline (Cho), and creatine (Cr), and metabolite ratio: NAA/Cho, NAA/(Cho + Cr) were selected and calculated. The preoperative IHC data were collected including CD34, Ki-67, p53, S-100, syn, vimentin, NeuN, Nestin, and glial fibrillary acidic protein. Results: One predicting parameter of DTI was screened, the rADC of the Ki-67 positive group was lower than that of the negative group. Two parameters of 1H-MRS were found to have significant reference values for glioma grades, the NAA and Cr decreased as the grade of glioma increased, moreover, Ki-67 Li was negatively correlated with NAA and Cr. Conclusion: NAA and Cr have potential application value in predicting glioma grades and tumor proliferation activity. Only rADC has predictive value for Ki-67 expression among DTI parameters.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

Trans-temporal trans-choroidal resection of thalamic and thalamopeduncular tumors: how I do it.

BACKGROUND:  Surgical resection is the cornerstone of treatment for low-grade tumors, albeit total excision is beneficial. As the thalamus is surrounded by vital neurovascular system, lesions here present a surgical challenge. METHOD: This article aims to demonstrate the trans-temporal, trans-choroidal fissure approach's effective surgical therapy on patients with thalamic lesions. With this approach, we were able to remove the tumor completely in three patients and almost completely in six more. Here we discuss a few technical details and potential hazards of the procedure with an operative video. CONCLUSION: This approach  provides excellent access to the deep areas of brain.

Unraveling morphology, methylation profiling, and diagnostic challenges in BRAF-Mutant pediatric glial and glioneuronal tumors.

OBJECTIVES: To elucidate the relationship between DNA methylation profiling (DMP) and pathological diagnosis (PD) in pediatric glial and glioneuronal tumors with B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutations, addressing their diagnostic challenges. METHODS: This retrospective study, conducted in Saudi Arabia, analyzed 47 cases from the Children's Brain Tumor Network online database using scanned images, next-generation sequencing data, and methylation profiles processed using the Heidelberg methylation brain tumor classifiers v12.5 and v12.8. The data was last access on 10 November 2023. RESULTS: The highest prevalence of BRAF mutations was observed in pilocytic astrocytoma and ganglioglioma. The DMP was consistent with PD in 23 cases, but discrepancies emerged in others, including diagnostic changes in diffuse leptomeningeal glioneuronal tumor and polymorphous low-grade neuroepithelial tumor of the young. A key inconsistency appeared between a pilocytic astrocytoma MC and a glioneuronal tumor PD. Two high-grade astrocytomas were misclassified as pleomorphic xanthoastrocytomas. Additionally, low variant allelic frequency in gangliogliomas likely contributed to misclassifications as control in 5 cases. CONCLUSION: This study emphasized the importance of integrating DMP with PD in diagnosing pediatric glial and glioneuronal tumors with BRAF mutations. Although DMP offers significant diagnostic insights, its limitations, particularly in cases with low tumor content, necessitate cautious interpretation, as well as its use as a complementary diagnostic tool, rather than a definitive method.

Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model.

Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.

Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis.

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, "non-enhanced, and edematous" areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709-0.963) and 0.739 (0.581-0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

Intracerebral benign fibrous histiocytoma.

Benign fibrous histiocytoma (BFH) within the intracerebral region is remarkably rare. Our report details 2 cases of unusual BFH instances that exhibit no adhesion to the dura mater or cerebral falx, accompanied by a comprehensive literature review. While magnetic resonance imaging demonstrates specific characteristics for BFH, it does not readily differentiate BFH from more common brain neoplasms like gliomas and metastatic tumors. The definitive diagnosis of BFH depends primarily on histopathological and immunohistochemical examinations. Total surgical resection is considered an efficacious therapeutic approach, emphasizing the necessity for prolonged postoperative surveillance to detect any potential tumor recurrence or metastasis.

Assessment of imaging risks for recurrence after stereotactic radiosurgery for brain metastases (IRRaS-BM).

BACKGROUND: The identification of viable tumors and radiation necrosis after stereotactic radiosurgery (SRS) is crucial for patient management. Tumor habitat analysis involving the grouping of similar voxels can identify subregions that share common biology and enable the depiction of areas of tumor recurrence and treatment-induced change. This study aims to validate an imaging biomarker for tumor recurrence after SRS for brain metastasis by conducting tumor habitat analysis using multi-parametric MRI. METHODS: In this prospective study (NCT05868928), patients with brain metastases will undergo multi-parametric MRI before SRS, and then follow-up MRIs will be conducted every 3 months until 24 months after SRS. The multi-parametric MRI protocol will include T2-weighted and contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. Using k-means voxel-wise clustering, this study will define three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) on T1- and T2-weighted images and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable) on apparent diffusion coefficient maps and cerebral blood volume maps. Using RANO-BM criteria as the reference standard, via Cox proportional hazards analysis, the study will prospectively evaluate associations between parameters of the tumor habitats and the time to recurrence. The DICE similarity coefficients between the recurrence site and tumor habitats will be calculated. DISCUSSION: The tumor habitat analysis will provide an objective and reliable measure for assessing tumor recurrence from brain metastasis following SRS. By identifying subregions for local recurrence, our study could guide the next therapeutic targets for patients after SRS. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov (NCT05868928).

Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.

BACKGROUND: Cancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results. METHODS: In this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features. RESULTS: The fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery. CONCLUSION: The results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

Evaluation of preoperative magnetic resonance imaging features and diagnostic effectiveness of grades II and III intracranial solitary fibroma.

OBJECTIVES: To explore the value of preoperative magnetic resonance imaging (MRI) characterization of intracranial solitary fibrous tumors (ISFT) and to evaluate the effectiveness of preoperative MRI features in predicting pathological grading. MATERIALS AND METHODS: This retrospective analysis comprised the clinical and preoperative MRI characterization of 55 patients with ISFT in our hospital, including 27 grade II cases and 28 grade III cases confirmed by postoperative pathology. Variables included age, sex, tumor location, cross-midline status, signal characteristics of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion­weighted imaging (DWI), peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel, maximum tumor diameter, maximum, minimum, and average values of apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumors enhancement mode, meningeal tail sign, skull invasion, cerebral parenchymal invasion, and venous sinus involvement. The independent samples t test or Mann-Whitney U test was performed to compare continuous data between the two groups, and the Pearson chi-squared test or Fisher's exact test was used to compare categorical data. In addition, bivariate logistic regression was performed to construct a comprehensive model, and receiver operating characteristic (ROC) curves were generated to calculate the areas under the curve (AUCs), thereby determining the value of each parameter in the differential diagnosis of grades II and III ISFT. RESULTS: The mean age at onset was similar between patients with grades II and III ISFT (46.77 ± 14.66 years and 45.82 ± 12.07 years, respectively). The proportions of men among patients with grades II and III ISFT were slightly higher than those of female patients (male/female: 1.25 [15/12] and 1.33 [16/12], respectively). There were significant differences between grades II and III ISFT in the T2-FLAIR and DWI signal characteristics, maximum, minimum, and average values of the apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumor location, and skull invasion (P = 0.001, P = 0.018, P = 0.000, P = 0.000, P = 0.000, P = 0.010, and P = 0.032, respectively). However, no significant differences were noted between grades II and III ISFT in age, sex, cross-midline status, T1WI and T2WI signal characteristics, peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel shadow, enhancement mode, meningeal tail sign, maximum tumor diameter, brain parenchyma invasion, or venous sinus involvement (all P > 0.05). Moreover, binary logistic regression analysis showed that the model accuracy was 89.1% when ADCmin was included in the regression equation. Moreover, ROC curve analysis showed that the AUC of ADCmin was 0.805 (0.688, 0.922), sensitivity was 74.1%, specificity was 75.0%, and the cutoff value was 672 mm2/s. CONCLUSIONS: Grade III ISFT patients displayed more mixed T2-FLAIR signal characteristics and DWI signal characteristics than grade II patients, as shown by higher skull invasion and tumor mass collapse midline distribution and lower ADCmax, ADCmean, and ADCmin values. The ADCmin value was significant in the preoperative assignment of grades II and III ISFT, thereby contributing to enhanced accuracy in the imaging grading diagnosis of the disease.

Intentional Insertion of Air to Predict the Diagnostic Accuracy of Stereotactic Biopsy and a Uniform Grading System for Reporting.

BACKGROUND AND OBJECTIVES: Stereotactic biopsies are a relatively safe and reliable way of tissue diagnosis and characterization of eloquent area lesions/neoplasm. However, predicting the accuracy of the site of biopsy with the desired/planned site is not always possible. We describe a technique to identify the precise location of the biopsy site in the post-operative computed tomography (CT) scan using the injection of a low volume of air into the biopsy cannula. METHODS: Hundred consecutive biopsies were performed in 80 adults/20 children (59 males/41 females, median age 51 years) over 3 years, consisting of 75 frameless and 25 frame-based stereotactic biopsies. After the biopsy specimens had been collected, a small volume of air (median 1 cc) was injected into the site. Post-operative CT was done within 4 hours of the biopsy to see the site of the air bubble, and the same was correlated with the histopathological accuracy. RESULTS: Intra-cranial air in the selected target was present in 95 patients (Grade 1 and 2), while the air was seen in the track (Grade 3) in 3% and at an unrelated site (Grade 4) in 2% of cases. Both Grade 4 biopsies were negative on histopathology (diagnostic yield = 98%). Two negative biopsies were reported, which were both predicted with the Grade 4 biopsy. The grading allowed uniform reporting across series and eliminated the chance of upgrading/downgrading the report due to wrong site sampling within the lesion/neoplasm. CONCLUSION: The air-injection manoeuvre proposed for use in stereotactic biopsies of intra-cranial mass lesions is a safe and reliable technique that allows the exact biopsy site to be located without any related complications.

Development of a Radiolabeled Cyclin-Dependent Kinases 4 and 6 (CDK4/6) Inhibitor for Brain and Cancer PET Imaging.

The synthesis, biochemical evaluation and radiosynthesis of a cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitor and radioligand was performed. NT431, a newly synthesized 4-fluorobenzyl-abemaciclib, exhibited high potency to CDK4/6 and against four cancer cell lines with IC50 similar to that of the parent abemaciclib. We performed a two-step one-pot radiosynthesis to produce [18F]NT431 with good radiochemical yield (9.6 ± 3%, n = 3, decay uncorrected), high radiochemical purity (>95%), and high molar activity (>370 GBq/µmol (>10.0 Ci/µmol). In vitro autoradiography confirmed the specific binding of [18F]NT431 to CDK4/6 in brain tissues. Dynamic PET imaging supports that both [18F]NT431 and the parent abemaciclib crossed the BBB albeit with modest brain uptake. Therefore, we conclude that it is unlikely that NT431 or abemaciclib (FDA approved drug) can accumulate in the brain in sufficient concentrations to be potentially effective against breast cancer brain metastases or brain cancers. However, despite the modest BBB penetration, [18F]NT431 represents an important step towards the development and evaluation of a new generation of CDK4/6 inhibitors with superior BBB penetration for the treatment and visualization of CDK4/6 positive tumors in the CNS. Also, [18F]NT431 may have potential application in peripheral tumors such as breast cancer and other CDK4/6 positive tumors.

Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.

MR-CT image fusion method of intracranial tumors based on Res2Net.

BACKGROUND: Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. PURPOSE: The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. METHODS: This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. RESULTS: Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. CONCLUSION: The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Intraoperative MRI without an intraoperative MRI suite: a workflow for glial tumor surgery.

BACKGROUND: Intraoperative MRI (iMRI) has emerged as a useful tool in glioma surgery to safely improve the extent of resection. However, iMRI requires a dedicated operating room (OR) with an integrated MRI scanner solely for this purpose. Due to physical or economical restraints, this may not be feasible in all centers. The aim of this study was to investigate the feasibility of using a non-dedicated MRI scanner at the radiology department for iMRI and to describe the workflow with special focus on time expenditure and surgical implications. METHODS: In total, 24 patients undergoing glioma surgery were included. When the resection was deemed completed, the wound was temporarily closed, and the patient, under general anesthesia, was transferred to the radiology department for iMRI, which was performed using a dedicated protocol on 1.5 or 3 T scanners. After performing iMRI the patient was returned to the OR for additional tumor resection or final wound closure. All procedural times, timestamps, and adverse events were recorded. RESULT: The median time from the decision to initiate iMRI until reopening of the wound after scanning was 68 (52-104) minutes. Residual tumors were found on iMRI in 13 patients (54%). There were no adverse events during the surgeries, transfers, transportations, or iMRI-examinations. There were no wound-related complications or infections in the postoperative period or at follow-up. There were no readmissions within 30 or 90 days due to any complication. CONCLUSION: Performing intraoperative MRI using an MRI located outside the OR department was feasible and safe with no adverse events. It did not require more time than previously reported data for dedicated iMRI scanners. This could be a viable alternative in centers without access to a dedicated iMRI suite.

Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients.

BACKGROUND: Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS: A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS: The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS: The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.

Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis.

BACKGROUND: Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn't been fully investigated for the differentiation and may have the potential to improve it. METHODS: A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. RESULTS: Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256-0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25). CONCLUSIONS: The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.

Clinical features, MRI, molecular alternations, and prognosis of astrocytoma based on WHO 2021 classification of central nervous system tumors: A single-center retrospective study.

BACKGROUND: The diagnosis of glioma has advanced since the release of the WHO 2021 classification with more molecular alterations involved in the integrated diagnostic pathways. Our study aimed to present our experience with the clinical features and management of astrocytoma, IDH mutant based on the latest WHO classification. METHODS: Patients diagnosed with astrocytoma, IDH-mutant based on the WHO 5th edition classification of CNS tumors at our center from January 2009 to January 2022 were included. Patients were divided into WHO 2-3 grade group and WHO 4 grade group. Integrate diagnoses were retrospectively confirmed according to WHO 2016 and 2021 classification. Clinical and MRI characteristics were reviewed, and survival analysis was performed. RESULTS: A total of 60 patients were enrolled. 21.67% (13/60) of all patients changed tumor grade from WHO 4th edition classification to WHO 5th edition. Of these, 21.43% (6/28) of grade II astrocytoma and 58.33% (7/12) of grade III astrocytoma according to WHO 4th edition classification changed to grade 4 according to WHO 5th edition classification. Sex (p = 0.042), recurrent glioma (p = 0.006), and Ki-67 index (p < 0.001) of pathological examination were statistically different in the WHO grade 2-3 group (n = 27) and WHO grade 4 group (n = 33). CDK6 (p = 0.004), FGFR2 (p = 0.003), and MYC (p = 0.004) alterations showed an enrichment in the WHO grade 4 group. Patients with higher grade showed shorter mOS (mOS = 75.9 m, 53.6 m, 26.4 m for grade 2, 3, and 4, respectively, p = 0.01). CONCLUSIONS: Patients diagnosed as WHO grade 4 according to the 5th edition WHO classification based on molecular alterations are more likely to have poorer prognosis. Therefore, treatment should be tailored to their individual needs. Further research is needed for the management of IDH-mutant astrocytoma is needed in the future.