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

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

Epidemiology and Management Trends of Neuro-Oncology in Nigeria: A Systematic Review and Pooled Analysis.

OBJECTIVE: Access to neuro-oncologic care in Nigeria has grown exponentially since the first reported cases in the mid-1960s. In this systematic review and pooled analysis, we characterize the growth of neurosurgical oncology in Nigeria and build a reference paper to direct efforts to expand this field. METHODS: We performed an initial literature search of several article databases and gray literature sources. We included and subsequently screened articles published between 1962 and 2021. Several variables were extracted from each study, including the affiliated hospital, the number of patients treated, patient sex, tumor pathology, the types of imaging modalities used for diagnosis, and the interventions used for each individual. Change in these variables was assessed using Chi-squared independence tests and univariate linear regression when appropriate. RESULTS: A total of 147 studies were identified, corresponding to 5,760 patients. Over 4000 cases were reported in the past 2 decades from 21 different Nigerian institutions. The types of tumors reported have increased over time, with increasingly more patients being evaluated via computed tomography (CT) and magnetic resonance imaging (MRI). There is also a prevalent use of radiotherapy, though chemotherapy remains an underreported treatment modality. CONCLUSIONS: This study highlights key trends regarding the prevalence and management of neuro-oncologic pathologies within Nigeria. Further studies are needed to continue to learn and guide the future growth of this field in Nigeria.

Indirect functional connectivity does not predict overall survival in glioblastoma.

BACKGROUND: Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). METHODS: A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. RESULTS: The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. CONCLUSIONS: Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

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

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

Advancements in Neurosurgical Intraoperative Histology.

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

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions.

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Optic nerve sheath diameter correlates with both success and failure of hydrocephalus treatment in pediatric patients with pineal region lesions.

BACKGROUND: Pineal region lesions in children are heterogenous pathologies often symptomatic due to occlusive hydrocephalus and thus elevated intracranial pressure (ICP). MRI-derived parameters to assess hydrocephalus are the optic nerve sheath diameter (ONSD) as a surrogate for ICP and the frontal occipital horn ratio (FOHR), representing ventricle volume. As elevated ICP may not always be associated with clinical signs, the adjunct of ONSD could help decision making in patients undergoing treatment. The goal of this study is to assess the available magnetic resonance imaging (MRI) of patients with pineal region lesions undergoing surgical treatment with respect to pre- and postoperative ONSD and FOHR as an indicator for hydrocephalus. METHODS: Retrospective data analysis was performed in all patients operated for pineal region lesions at a tertiary care center between 2010 and 2023. Only patients with pre- and postoperative MRI were selected for inclusion. Clinical data and ONSD at multiple time points, as well as FOHR were analyzed. Imaging parameter changes were correlated with clinical signs of hydrocephalus before and after surgical treatment. RESULTS: Thirty-three patients with forty operative cases met the inclusion criteria. Age at diagnosis was 10.9 ± 4.6 years (1-17 years). Hydrocephalus was seen in 80% of operative cases preoperatively (n = 32/40). Presence of hydrocephalus was associated with significantly elevated preoperative ONSD (p = 0.006). There was a significant decrease in ONSD immediately (p < 0.001) and at 3 months (p < 0.001) postoperatively. FOHR showed a slightly less pronounced decrease (immediately p = 0.006, 3 months p = 0.003). In patients without hydrocephalus, no significant changes in ONSD were observed (p = 0.369). In 6/6 patients with clinical hydrocephalus treatment failure, ONSD increased, but in 3/6 ONSD was the only discernible MRI change with unchanged FOHR. CONCLUSIONS: ONSD measurements may have utility in evaluating intracranial hypertension due to hydrocephalus in patients with pineal region tumors. ONSD changes appear to have value in assessing hydrocephalus treatment failure.

ReMIND: The Brain Resection Multimodal Imaging Database.

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

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). MATERIAL AND METHOD: MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system's performance is evaluated by categorizing glioblastoma patients based on hypoxia. RESULTS: The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. CONCLUSION: The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

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

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

Minimally invasive resection of intracranial lesions using tubular retractors: A single surgeon series.

OBJECTIVE: Tubular retractors are increasingly used due to their low complication rates, providing easier access to lesions while minimizing trauma from brain retraction. Our study presents the most extensive series of cases performed by a single surgeon aiming to assess the effectiveness and safety of a transcortical-transtubular approach for removing intracranial lesions. METHODS: We performed a retrospective review of patients who underwent resection of an intracranial lesion with the use of tubular retractors. Electronic medical records were reviewed for patient demographics, preoperative clinical deficits, diagnosis, preoperative and postoperative magnetic resonance imaging (MRI) scans, lesion characteristics including location, volume, extent of resection (EOR), postoperative complications, and postoperative deficits. RESULTS: 112 transtubular resections for intracranial lesions were performed. Patients presented with a diverse number of pathologies including metastasis (31.3 %), GBM (21.4 %), and colloid cysts (19.6 %) The mean pre-op lesion volume was 14.45 cm3. A gross total resection was achieved in 81 (71.7 %) cases. Seventeen (15.2 %) patients experienced early complications which included confusion, short-term memory difficulties, seizures, meningitis and motor and visual deficits. Four (3.6 %) patients had permanent complications, including one with aphasia and difficulty finding words, another with memory loss, a third with left-sided weakness, and one patient who developed new-onset long-term seizures. Mean post-operative hospitalization length was 3.8 days. CONCLUSION: Tubular retractors provide a minimally invasive approach for the extraction of intracranial lesions. They serve as an efficient tool in neurosurgery, facilitating the safe resection of deep-seated lesions with minimal complications.

Intracranial residual lesions following early intensification in a patient with T-cell acute lymphoblastic leukemia: a case report.

BACKGROUND: T-cell acute lymphoblastic leukemia (T-ALL) tends to involve central nervous system (CNS) infiltration at diagnosis. However, cases of residual CNS lesions detected at the end of induction and post early intensification have not been recorded in patients with T-ALL. Also, the ratio and prognosis of patients with residual intracranial lesions have not been defined. CASE PRESENTATION: A 9-year-old boy with T-ALL had multiple intracranial tumors, which were still detected post early intensification. To investigate residual CNS lesions, we used 11C-methionine (MET)-positron emission tomography. Negative MET uptake in CNS lesions and excellent MRD status in bone marrow allowed continuing therapies without hematopoietic cell transplantation. CONCLUSIONS: In cases with residual lesions on imaging studies, treatment strategies should be considered by the systemic response, direct assessment of spinal fluid, along with further development of noninvasive imaging methods in CNS. Further retrospective or prospective studies are required to determine the prognosis and frequency of cases with residual intracranial lesions after induction therapy.

RAPNO efforts in Brain Tumour Outcomes.

Assessing treatment efficacy for brain tumours has evolved since its inception with the introduction of MacDonald's criteria, which pioneered the utility of imaging to determine an objective and quantifiable response to treatment. This criterion failed to distinguish pseudo response or progression from progression and did not account for non-enhancing disease therefore; the response assessment in neuro-oncology (RANO) working group was established to account for these limitations. Since, its commencement it has worked to determine response assessment for multiple tumours. As paediatric tumours exhibit heterogeneous and variable-enhancing characteristics, the response assessment in paediatric neuro-oncology (RAPNO) working group was formed to create separate criteria. Six response criteria have been published to date, and the article summarizes them.

[Surgery for Pineal Region Tumors: Concept and Technical Aspects of Occipital Transtentorial Approach].

This article describes the concept and technical aspects of the occipital transtentorial approach(OTA)for tumor extraction in the pineal region, based on the author's experience and literature review. Awareness of the successful completion of each surgical step is essential. Preoperative preparation and imaging evaluations, with particular attention to the veins and venous sinuses, are especially important. It is also helpful to perform a complete dura incision and inversion up to the edge of confluence, superior sagittal sinus, and transverse sinus. Subsequently, it is necessary to understand the usefulness of adequate dissection in the vicinity of the corpus callosum and internal occipital vein(IOV)so that the occipital lobe can be moved without difficulty. Furthermore, development of the IOV with adequate tentoriotomy facilitates contralateral work. Finally, complete understanding of each step during the bilateral, ambient cistern and cerebellomesencephalic fissure dissection process, where the cerebellar vermis can be moved without difficulty, is necessary for a safe OTA to pineal region tumor extraction.

Quantifying gadolinium-based nanoparticle uptake distributions in brain metastases via magnetic resonance imaging.

AGuIX, a novel gadolinium-based nanoparticle, has been deployed in a pioneering double-blinded Phase II clinical trial aiming to assess its efficacy in enhancing radiotherapy for tumor treatment. This paper moves towards this goal by analyzing AGuIX uptake patterns in 23 patients. A phantom was designed to establish the relationship between AGuIX concentration and longitudinal ( T 1 ) relaxation. A 3T MRI and MP2RAGE sequence were used to generate patient T 1 maps. AGuIX uptake in tumors was determined based on longitudinal relaxivity. AGuIX (or placebo) was administered to 23 patients intravenously at 100 mg/kg 1-5 hours pre-imaging. Each of 129 brain metastases across 23 patients were captured in T 1 maps and examined for AGuIX uptake and distribution. Inferred AGuIX recipients had average tumor uptakes between 0.012 and 0.17 mg/ml, with a mean of 0.055 mg/ml. Suspected placebo recipients appeared to have no appreciable uptake. Tumors presented with varying spatial AGuIX uptake distributions, suspected to be related to differences in accumulation time and patient-specific bioaccumulation factors. This research demonstrates AGuIX's ability to accumulate in brain metastases, with quantifiable uptake via T 1 mapping. Future analyses will extend these methods to complete clinical trial data (~ 134 patients) to evaluate the potential relationship between nanoparticle uptake and possible tumor response following radiotherapy.Clinical Trial Registration Number: NCT04899908.Clinical Trial Registration Date: 25/05/2021.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features.

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

A radiomics-based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma.

BACKGROUND AND PURPOSE: As a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients. METHODS: The study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2-weighted imaging, T1-weighted imaging (T1WI), contrast-enhanced T1WI, and fluid-attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad-score)with clinical features. Receiver-operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes. RESULTS: The least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850-.963) and.899 (95% CI:.708-.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR = 1.767, p = .019). CONCLUSION: The radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.