Meningioma Tumor Research Articles

A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

Radiological modalities in the Colli Sinisitra tumor

We reported that a 66-year-old male patient was suspected of having coli dd meningioma tumor. Although 90% of all patients with colitis can be diagnosed on conventional clinical and radiological basis, a biopsy is mandatory for histological confirmation and treatment planning.

Spinal cord involvement in tuberculous meningitis: a case report and brief review of literature

Introduction: Tuberculosis (TB) continues to pose a significant public health problem worldwide. Tuberculous meningitis (TBM) is the most devastating form of extrapulmonary TB however other forms of central nervous system (CNS) disease include tuberculoma and spinal arachnoiditis. TBM carries high mortality even for a patient who is already receiving treatment. The difficulty in diagnosis often leads to a delay in treatment and subsequent mortality. The emergence of Xpert ultra has improved the rapid detection of MTB and rifampicin resistance in CSF and is the preferred diagnostic tool in TBM. Case: In this case report we present a 33years patient of concern who presented with progressive lower limb weakness associated with pain and paresthesia for 4 months, admitted via the Orthopedic unit with a diagnosis of spinal mass (meningioma, neurofibroma, or nerve sheath tumor) for which biopsy was done and revealed a chronic inflammatory process, necrotic bone lesions with no granulomas and no malignancy, he was later diagnosed with tuberculous meningitis and promptly started anti-tuberculous therapy with a dramatic recovery and improvement in neurological function. Conclusion: Tuberculous meningitis conditions have high morbidity and mortality yet diagnosis and start of treatment continue to experience an important delay. Clinicians should keep in mind the limitations of clinical presentation due to pleiotropy and current diagnostics and should employ a combination of diagnostic modalities in addition to a high index of suspicion to prevent morbidity in patients with TBM.

Iterative brain tumor retrieval for MR images based on user’s intention model

Generally, medical content-based image retrieval (CBIR) systems select low-level visual features as image descriptors. However, these descriptors fail to provide clues for understanding the content of medical images in a similar way as a human expert, which makes the retrieval results inconsistent with the user’s intention. To solve this problem, we propose a closed-loop brain tumor retrieval system for MR images with an eye-tracking based relevance feedback mechanism. In our method, we first model the intention of the user by training a convolutional neural network based on the temporal and spatial features extracted from his/her eye-tracking data collected when inspecting the relevance between different images. Upon using visual features as a bridge, the relevancy degree to the query image of any of the database images is computed with our user’s intention model by transferring to it the eye movement data from the most visually similar image amongst images iteratively accumulated in the canvas. Our proposed retrieval system is implemented in an iterative manner. In each round of iteration, user’s eye movement data when inspecting the system returns are collected and the canvas collection of images is also updated by appending to it the user inspected system returns. With the updated canvas collections, the relevancy degree of database images can be recomputed and the system can begin a new round search of the most relevant images. Extensive experiments have been performed on a publicly available T1-weighted contrast-enhanced magnetic resonance image (CE-MRI) dataset that consists of three types of brain tumors (glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Experimental results of 22 volunteers (11 males and 11 females, with an average age of 24.4 years) from our medical school show that upon implicit involvement of users in the brain tumor retrieving process, our proposed system significantly outperforms state-of-the-art methods and achieves Prec@10 to 99.94%, mAP to 97.95% after the third round of iteration.

A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification

One of the most dangerous diseases in the world is brain tumors. After the brain tumor destroys healthy tissues in the brain, it multiplies abnormally, causing an increase in the internal pressure in the skull. If this condition is not diagnosed early, it can lead to death. Magnetic Resonance Imaging (MRI) is a diagnostic method frequently used in soft tissues with successful results. This study presented a new deep learning-based approach, which automatically detects brain tumors using Magnetic Resonance (MR) images. Convolutional and fully connected layers of a new Residual-CNN (R-CNN) model trained from scratch were used to extract deep features from MR images. The representation power of the deep feature set was increased with the features extracted from all convolutional layers. Among the deep features extracted, the 100 features with the highest distinctiveness were selected with a new multi-level feature selection algorithm named L1NSR. The best performance in the classification stage was obtained by using the SVM algorithm with the Gaussian kernel. The proposed approach was evaluated on two separate data sets composed of 2-class (healthy and tumor) and 4-class (glioma tumor, meningioma tumor, pituitary tumor, and healthy) datasets. Besides, the proposed approach was compared with other state-of-the-art approaches using the respective datasets. The best classification accuracies for 2-class and 4-class datasets were 98.8% and 96.6%, respectively.

Molecular neuropathology of brain-invasive meningiomas.

Invasion of brain tissue by meningiomas has been identified as one key factor for meningioma recurrence. The identification of meningioma tumor tissue surrounded by brain tissue in neurosurgical samples has been touted as a criterion for atypical meningioma (CNS WHO grade 2), but is only rarely seen in the absence of other high‐grade features, with brain‐invasive otherwise benign (BIOB) meningiomas remaining controversial. While post‐surgery irradiation therapy might be initiated in brain‐invasive meningiomas to prevent recurrences, specific treatment approaches targeting key molecules involved in the invasive process are not established. Here we have compiled the current knowledge about mechanisms supporting brain tissue invasion by meningiomas and summarize preclinical models studying targeted therapies with potential inhibitory effects.

Brain Tumor Classification Based on Hybrid Optimized Multi-features Analysis Using Magnetic Resonance Imaging Dataset

ABSTRACT Brain tumors are deadly but become deadliest because of delayed and inefficient diagnosis process. Large variations in tumor types also instigate additional complexity. Machine vision brain tumor diagnosis addresses the problem. This research’s objective was to develop a brain tumor classification model based on machine vision techniques using brain Magnetic Resonance Imaging (MRI). For this purpose, a novel hybrid-brain-tumor-classification (HBTC) framework was designed and evaluated for the classification of cystic (cyst), glioma, meningioma (menin), and metastatic (meta) brain tumors. The proposed framework lessens the inherent complexities and boosts performance of the brain tumor diagnosis process. The brain MRI dataset was input to the HBTC framework, pre-processed, segmented to localize the tumor region. From the segmented dataset Co-occurrence matrix (COM), run-length matrix (RLM), and gradient features were extracted. After the application of hybrid multi-features, the nine most optimized features were selected and input to the framework’s classifiers, namely multilayer perception (MLP), J48, meta bagging (MB), and random tree (RT) to classify cyst, glioma, menin, and meta tumors. Maximum brain tumor classification performance achieved by the HBTC framework was 98.8%. The components and performance of the proposed framework show that it is a novel and robust classification framework.

Raman hyperspectral imaging coupled to three-dimensional discriminant analysis: Classification of meningiomas brain tumour grades

Meningiomas remains a clinical dilemma. They are the commonest “benign” types of brain tumours and, although being typically benign, they are divided into three WHO grades categories (I, II and III) which are associated with the tumour growth rate and likelihood of recurrence. Recurrence depends on extend of surgery as well as histopathological diagnosis. There is a marked variation amongst surgeons in the follow-up arrangements for their patients even within the same unit which has a significant clinical, and financial implication. Knowing the tumour grade rapidly is an important factor to predict surgical outcomes and adequate patient treatment. Clinical follow up sometimes is haphazard and not based on clear evidence. Spectrochemical techniques are a powerful tool for cancer diagnostics. Raman hyperspectral imaging is able to generate spatially-distributed spectrochemical signatures with great sensitivity. Using this technique, 95 brain tissue samples (66 meningiomas WHO grade I, 24 meningiomas WHO grade II and 5 meningiomas that reoccurred) were analysed in order to discriminate grade I and grade II samples. Newly-developed three-dimensional discriminant analysis algorithms were used to process the hyperspectral imaging data in a 3D fashion. Three-dimensional principal component analysis quadratic discriminant analysis (3D-PCA-QDA) was able to distinguish grade I and grade II meningioma samples with 96% test accuracy (100% sensitivity and 95% specificity). This technique is here shown to be a high-throughput, reagent-free, non-destructive, and can give accurate predictive information regarding the meningioma tumour grade, hence, having enormous clinical potential with regards to being developed for intra-operative real-time assessment of disease.

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Simple SummaryTumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Human Endogenous Retrovirus Type K Promotes Proliferation and Confers Sensitivity to Antiretroviral Drugs in Merlin-Negative Schwannoma and Meningioma.

Deficiency of the tumor suppressor Merlin causes development of schwannoma, meningioma, and ependymoma tumors, which can occur spontaneously or in the hereditary disease neurofibromatosis type 2 (NF2). Merlin mutations are also relevant in a variety of other tumors. Surgery and radiotherapy are current first-line treatments; however, tumors frequently recur with limited treatment options. Here, we use human Merlin-negative schwannoma and meningioma primary cells to investigate the involvement of the endogenous retrovirus HERV-K in tumor development. HERV-K proteins previously implicated in tumorigenesis were overexpressed in schwannoma and all meningioma grades, and disease-associated CRL4DCAF1 and YAP/TEAD pathways were implicated in this overexpression. In normal Schwann cells, ectopic overexpression of HERV-K Env increased proliferation and upregulated expression of c-Jun and pERK1/2, which are key components of known tumorigenic pathways in schwannoma, JNK/c-Jun, and RAS/RAF/MEK/ERK. Furthermore, FDA-approved retroviral protease inhibitors ritonavir, atazanavir, and lopinavir reduced proliferation of schwannoma and grade I meningioma cells. These results identify HERV-K as a critical regulator of progression in Merlin-deficient tumors and offer potential strategies for therapeutic intervention. SIGNIFICANCE: The endogenous retrovirus HERV-K activates oncogenic signaling pathways and promotes proliferation of Merlin-deficient schwannomas and meningiomas, which can be targeted with antiretroviral drugs and TEAD inhibitors.

Pleiotropic MLLT10 variation confers risk of meningioma and estrogen-mediated cancers.

BackgroundRisk of tumors of the breast, ovary, and meninges has been associated with hormonal factors and with one another. Genome-wide association studies (GWAS) identified a meningioma risk locus on 10p12 near previous GWAS hits for breast and ovarian cancers, raising the possibility of genetic pleiotropy.MethodsWe performed imputation-based fine-mapping in three case-control datasets of meningioma (927 cases, 790 controls), female breast cancer (28 108 cases, 22 209 controls), and ovarian cancer (25 509 cases, 40 941 controls). Analyses were stratified by sex (meningioma), estrogen receptor (ER) status (breast), and histotype (ovarian), then combined using subset-based meta-analysis in ASSET. Lead variants were assessed for association with additional traits in UK Biobank to identify potential effect-mediators.ResultsTwo-sided subset-based meta-analysis identified rs7084454, an expression quantitative trait locus (eQTL) near the MLLT10 promoter, as lead variant (5.7 × 10–14). The minor allele was associated with increased risk of meningioma in females (odds ratio (OR) = 1.42, 95% Confidence Interval (95%CI):1.20–1.69), but not males (OR = 1.19, 95%CI: 0.91–1.57). It was positively associated with ovarian (OR = 1.09, 95%CI:1.06–1.12) and ER+ breast (OR = 1.05, 95%CI: 1.02–1.08) cancers, and negatively associated with ER– breast cancer (OR = 0.91, 95%CI: 0.86–0.96). It was also associated with several adiposity traits (P < 5.0 × 10–8), but adjusting for body mass index did not attenuate its association with meningioma. MLLT10 and ESR1 expression were positively correlated in normal meninges (P = .058) and meningioma tumors (P = .0065).ConclusionsWe identify a MLLT10 eQTL positively associated with risk of female meningioma, ER+ breast cancer, ovarian cancer, and obesity, and implicate a potential estrogenic mechanism underlying this pleiotropy.

Cartesian Product Based Transfer Learning Implementation for Brain Tumor Classification

Knowledge-based transfer learning techniques have shown good performance for brain tumor classification, especially with small datasets. However, to obtain an optimized model for targeted brain tumor classification, it is challenging to select a pre-trained deep learning (DL) model, optimal values of hyperparameters, and optimization algorithm (solver). This paper first presents a brief review of recent literature related to brain tumor classification. Secondly, a robust framework for implementing the transfer learning technique is proposed. In the proposed framework, a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters: batch size and learning rate. An extensive exercise consisting of 435 simulations for 11 state-of-the-art pre-trained DL models was performed using 16 paired hyperparameters from the Cartesian product matrix to input the model with the three most popular solvers (stochastic gradient descent with momentum (SGDM), adaptive moment estimation (ADAM), and root mean squared propagation (RMSProp)). The 16 pairs were formed using individual hyperparameter values taken from literature, which generally addressed only one hyperparameter for optimization, rather than making a grid for a particular range. The proposed framework was assessed using a multi-class publicly available dataset consisting of glioma, meningioma, and pituitary tumors. Performance assessment shows that ResNet18 outperforms all other models in terms of accuracy, precision, specificity, and recall (sensitivity). The results are also compared with existing state-of-the-art research work that used the same dataset. The comparison was mainly based on performance metric “accuracy” with support of three other parameters “precision,” “recall,” and “specificity.” The comparison shows that the transfer learning technique, implemented through our proposed framework for brain tumor classification, outperformed all existing approaches. To the best of our knowledge, the proposed framework is an efficient framework that helped reduce the computational complexity and the time to attain optimal values of two important hyperparameters and consequently the optimized model with an accuracy of 99.56%.

Automated Detection and Classification of Meningioma Tumor from MR Images Using Sea Lion Optimization and Deep Learning Models

Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.

Efficient Brain Tumor Detection Based on Deep Learning Models

Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer learning model, a CNN-based model that is designed from scratch, a pre-trained inceptionresnetv2 model and a pre-trained inceptionv3 model. The performance of the four proposed models is tested using evaluation metrics including accuracy, sensitivity, specificity, precision, F1_score, Matthew’s correlation coefficient, error, kappa and false positive rate. The obtained results show that the two proposed models are very effective in achieving accuracies of 93.15% and 91.24% for the transfer learning model and BRAIN-TUMOR-net based on CNN, respectively. The inceptionresnetv2 model achieves an accuracy of 86.80% and the inceptionv3 model achieves an accuracy of 85.34%. Practical implementation of the proposed models is presented.

The Stanford stereotactic radiosurgery experience on 7000 patients over 2 decades (1999-2018): looking far beyond the scalpel.

The CyberKnife (CK) has emerged as an effective frameless and noninvasive method for treating a myriad of neurosurgical conditions. Here, the authors conducted an extensive retrospective analysis and review of the literature to elucidate the trend for CK use in the management paradigm for common neurosurgical diseases at their institution. A literature review (January 1990-June 2019) and clinical review (January 1999-December 2018) were performed using, respectively, online research databases and the Stanford Research Repository of patients with intracranial and spinal lesions treated with CK at Stanford. For each disease considered, the coefficient of determination (r2) was estimated as a measure of CK utilization over time. A change in treatment modality was assessed using a t-test, with statistical significance assessed at the 0.05 alpha level. In over 7000 patients treated with CK for various brain and spinal lesions over the past 20 years, a positive linear trend (r2 = 0.80) in the system's use was observed. CK gained prominence in the management of intracranial and spinal arteriovenous malformations (AVMs; r2 = 0.89 and 0.95, respectively); brain and spine metastases (r2 = 0.97 and 0.79, respectively); benign tumors such as meningioma (r2 = 0.85), vestibular schwannoma (r2 = 0.76), and glomus jugulare tumor (r2 = 0.89); glioblastoma (r2 = 0.54); and trigeminal neuralgia (r2 = 0.81). A statistically significant difference in the change in treatment modality to CK was observed in the management of intracranial and spinal AVMs (p < 0.05), and while the treatment of brain and spine metastases, meningioma, and glioblastoma trended toward the use of CK, the change in treatment modality for these lesions was not statistically significant. Evidence suggests the robust use of CK for treating a wide range of neurological conditions.

BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING

The early detection of the tumor plays an important role in the recovery of the patient. In our proposed model, we have collected MRI scans as it helps with the information about the blood supply inside the brain. Thus, for the recognition of anomaly, for examining the increasing of the ailment, and for the diagnosis, we prepared a data set consisting of various MRI images. We then focused on removing unwanted noise and image enhancement. The image characteristics can be enhanced by using image preprocessing techniques. The image enhancement depends upon different factors like computational time, computational cost, quality of the uncorrupted image, and the techniques used for noise elimination. We have made use of various filters for the image pre-processing. In our next step, image segmentation, an image is divided into several regions. We have implemented different types of segmentation techniques including active contours snakes, fuzzy C means, and regionderived triple thresholding. We have further implemented two hybrid segmentation models and used computer-aided detection techniques. Post-processing of the data is done using artificial bee colony optimization and watershed filtering and extraction. We then classify two images into tumor and non-tumor category using the VGG-16 CNN model. The features of the segmented images were further classified into various types of tumors, including Glioma tumor, Meningioma tumor, Pituitary tumor, and no tumor using one-hot encoding. This approach was further validated using synthetic and real MR image dataset from Kaggle (name of data set), to detect and classify different types of tumor.

MORPHOLOGICAL SPECTRUM OF INTRACRANIAL SPACE OCCUPYING LESION AT TERTIARY CARE HOSPITAL-A CLINICOPATHOLOGICAL STUDY

Introduction: The annual incidence of CNS tumors ranges from 10 to 17 per 1,00,000 persons for intracranial tumors, [1] the majority of these are primary tumors. An “Intra-cranial space occupying lesion” (ICSOL)is dened as a mass lesion in the cranial cavity with a diverse etiology like benign or malignant neoplasm, inammatory or parasitic lesion, hematoma, or arterio-venous malformation.[3] Many non-neoplastic CNS lesions can clinically &amp; radiologically simulate brain tumors. In such cases, histopathological [5] examination (HPE) can be helpful in differentiating between neoplastic and non-neoplastic etiologies. Aims and Objectives: This studywas undertaken to analyse theincidence and frequency of intracranial space occupying lesions, age and sex wise distribution, associated clinical symptoms and histopathological spectrum. Intracranial space occupying lesion biopsi Materials and Methods: es of 108 cases received indepartment of pathology, B.J. Medical College, Ahmedabad, during the period of March 2021 to August 2021. All specimens were preserved in 10% formalin and allowed to x for 24 hours, parafn embedded sections of 5 microns were cut. The hematoxylin and eosin stained sections of the CNS lesions were studied. One Hundred and Eight cases of Intracranial Spac Result: e Occupying lesions were studied, of which 8 (7.4%) cases were non neoplastic with 6 (75%) being cystic lesions and 2 (25%) were cerebral abscess. The neoplastic lesions comprised of 100 (92.6%) cases, which included 99 (99%) primary and 1 (1%) metastatic lesions. The most frequent type of CNS tumor was astrocytoma and oligodendroglioma (23 cases, 23%) followed by Meningioma (18 cases, 18%), pituitary adenoma (17 cases, 17%), Ependymoma(13 cases, 13 %) and metastatic tumor (1 case, 1%). The surgical pathologist plays an importa Conclusion: nt role in accurate diagnosis of various Intra [3] Cranial Space Occupying lesions which will be of immense help for patient prognosis and treatment.

Receptor Tyrosine Kinases as Candidate Prognostic Biomarkers and Therapeutic Targets in Meningioma.

Meningioma (MGM) is the most common type of intracranial tumor in adults. The validation of novel prognostic biomarkers to better inform tumor stratification and clinical prognosis is urgently needed. Many molecular and cellular alterations have been described in MGM tumors over the past few years, providing a rational basis for the identification of biomarkers and therapeutic targets. The role of receptor tyrosine kinases (RTKs) as oncogenes, including those of the ErbB family of receptors, has been well established in several cancer types. Here, we review histological, molecular, and clinical evidence suggesting that RTKs, including the epidermal growth factor receptor (EGFR, ErbB1), as well as other members of the ErbB family, may be useful as biomarkers and therapeutic targets in MGM.

Transcriptomic deconvolution reveals unique tumor microenvironmental interactions across intracranial meningioma WHO grades

Meningiomas are common tumors arising in the primary central nervous system. Although recent radiotherapy and surgical procedures have improved clinical outcomes, there remains a need for novel therapeutics to treat meningiomas resisting current approaches. To reveal putative therapeutic targets in meningioma, the present exploratory study inferred stromal and tumor compartment gene expression from bulk meningioma tumor RNA-sequencing data and revealed certain ligand-receptor interactions in tumor microenvironment which may direct future studies. Bulk tumor gene expression developed from 152 primary meningiomas encompassing WHO grades I-III were deconvolved into tumor and stromal compartment expression through non-negative linear regression using tumor purity estimates, ultimately revealing previously unknown features of tumor-stromal crosstalk in meningioma. It was found that fibroblast growth factor receptor 4 (FGFR4) and human epidermal growth factor receptor 4 (HER4) was implicated in multiple tumor-stroma interactions. Specific chemokine receptors, such as CCR4 and CCR7, were frequently involved in stroma-stroma crosstalk. Additionally, genes reflecting immune-exhausted T cell responses also displayed distinct expression across the WHO grades. Taken together, the findings reveal potential therapeutic targets which should be investigated further.

Model Selection in Peptide-receptor Radionuclide Therapy for an Accurate Determination of Time Integrated Activity Coefficients

Calculation of accurate time-integrated activity coefficients (TIACs) is desirable in nuclear medicine dosimetry. The accuracy of the calculated TIACs is highly dependent on the fit function. However, systematic studies of determining a good function for peptide-receptor radionuclide therapy (PRRT) in different patients have not been reported in the literature. The aim of this study was to individually determine the best function for the calculation of TIACs in tumor and kidneys using a model selection based on the goodness of fit criteria and Corrected Akaike Information Criterion (AICc). The data used in this study was pharmacokinetic data of 111In-DOTATATE in tumor and kidneys obtained from 4 PRRT patients. Eleven functions with various parameterizations were formulated to describe the biokinetic data of 111In-DOTATATE in tumor and kidneys. The model selection was performed by choosing the best function from the function with sufficient goodness of fit based on the smallest AICc. Based on the results of model selection, function A 1 -(λ 1+λphys )t was selected as the best function for all tumor and kidneys in patients with meningioma tumors. By using this function, the calculated of TIACs could be more accurate for future patients with meningioma tumor.