Treatment Of Brain Tumors Research Articles

Cytotoxic effects of nerve growth factor and its combinations with chemotherapeutic drugs on anaplastoc astrocytoma, glioblastoma and medubloblastoma cells <i>in vitro</i>

BACKGROUND: Currently, the effectiveness of the treatment of malignant tumors using surgical resection, radiotherapy and chemotherapy is insufficient. Therefore, new research is needed to find alternative molecules with antitumor effects. It is known that nerve growth factor (NGF) inhibits invasion, migration, and angiogenesis of tumor cells. Studying the effects of NGF on brain tumors, as well as its combinations with chemotherapy drugs used in medicine, may contribute to the development of new treatment regimens for malignant neoplasms in the central nervous system. AIM: The purpose of this study is an exploration the molecular and cellular mechanisms of anticancer effects of individual and combined preparations of NGF and chemotherapeutic drugs on brain tumor cells (gliomas C6, U251, anaplastic astrocytoma, glioblastoma and medulloblastoma). MATERIALS AND METHODS: The study was performed on rat glioma C6, human U251 glioma cell lines, as well as on primary cells of anaplastic astrocytoma (n = 9), glioblastoma (n = 9) and medulloblastoma (n = 38) patients. The cytotoxicity of chemotherapeutic drugs, NGF and their combinations against tumor cells was assessed using the MTT assay. The expression of TrkA and p75 receptors on anaplastic astrocytoma, glioblastoma and medulloblastoma cells was assessed by immunofluorescence analysis using anti-TrkA and anti-p75 monoclonal antibodies. RESULTS: Nerve growth factor exhibits in vitro cytotoxic activity that exceeds the activity of chemotherapy drugs towards rat glioma C6, human U251, anaplastic astrocytoma (AA), glioblastoma (GBM) and medulloblastoma cells. The cytotoxic activity of NGF in combination with chemotherapy drugs is significantly higher than the activity of the individual NGF drug against medulloblastoma cells, while against anaplastic astrocytoma cells it is comparable to the indicators of the isolated action of NGF, and lower for glioblastoma cells. The effectiveness of the cytotoxic effect of the combinations NGF + cisplatin and NGF + temozolomide (TMZ) on AA and GBM cells correlates with both the expression of TrkA, p75 receptors, and their coexpression, indicating that expression indicators can be considered as markers of tumor cell sensitivity to NGF. CONCLUSIONS: The data obtained allow us to consider NGF as a potential anticancer drug for the treatment of brain tumors. Thus, NGF can act as a potential anticancer drug for the development of new therapeutic regimens for brain tumors.

Noninvasive therapy of brain cancer using a unique systemic delivery methodology with a cancer terminator virus.

Primary, glioblastoma, and secondary brain tumors, from metastases outside the brain, are among the most aggressive and therapeutically resistant cancers. A physiological barrier protecting the brain, the blood-brain barrier (BBB), functions as a deterrent to effective therapies. To enhance cancer therapy, we developed a cancer terminator virus (CTV), a unique tropism-modified adenovirus consisting of serotype 3 fiber knob on an otherwise Ad5 capsid that replicates in a cancer-selective manner and simultaneously produces a potent therapeutic cytokine, melanoma differentiation-associated gene-7/interleukin-24(MDA-7/IL-24). A limitation of the CTV and most other viruses, including adenoviruses, is an inability to deliver systemically to treat brain tumors because of the BBB, nonspecific virus trapping, and immune clearance. These obstacles to effective viral therapy of brain cancer have now been overcome using focused ultrasound with a dual microbubble treatment, the focused ultrasound-double microbubble(FUS-DMB) approach. Proof-of-principle is now provided indicating that the BBB can be safely and transiently opened, and the CTV can then be administered in a second set of complement-treated microbubbles and released in the brain using focused ultrasound. Moreover, the FUS-DMB can be used to deliver the CTV multiple times in animals with glioblastoma growing in their brain thereby resulting in a further enhancement in survival. This strategy permits efficient therapy of primary and secondary brain tumors enhancing animal survival without promoting harmful toxic or behavioral side effects. Additionally, when combined with a standard of care therapy, Temozolomide, a further increase in survival is achieved. The FUS-DMB approach with the CTV highlights a noninvasive strategy to treat brain cancers without surgery. This innovative delivery scheme combined with the therapeutic efficacy of the CTV provides a novel potential translational therapeutic approach for brain cancers.

Antisense Oligonucleotides for Rapid Translation of Gene Therapy in Glioblastoma

Purpose: The limited efficacy of current treatments for malignant brain tumors necessitates novel therapeutic strategies. This study aimed to assess the potential of antisense oligonucleotides (ASOs) as adjuvant therapy for high-grade gliomas, focusing on their CNS penetration and clinical translation prospects. Methods: A comprehensive review of the existing literature was conducted to evaluate the implications of ASOs in neuro-oncology. Studies that investigated ASO therapy’s efficacy, CNS penetration, and safety profile were analyzed to assess its potential as a therapeutic intervention for high-grade gliomas. Results: ASOs present a promising avenue for enhancing targeted gene therapies in malignant gliomas. Their potent CNS penetration, in vivo durability, and efficient transduction offer advantages over conventional treatments. Preliminary in vivo and in vitro studies suggest ASOs as a viable adjuvant therapy for high-grade gliomas, warranting further exploration in clinical trials. Conclusions: ASOs hold significant promise as adjuvant therapy for high-grade gliomas, offering improved CNS penetration and durability compared with existing treatments. While preliminary studies are encouraging, additional research is needed to establish the safety and efficacy of ASO therapy in clinical settings. Further investigation and clinical trials are warranted to validate ASOs as a transformative approach in neuro-oncology.

DNA-damage RBE assessment for combined boron and gadolinium neutron capture therapy.

Neutron capture therapy (NCT) by 10B and 157Gd agents is a unique irradiation-based method which can be used to treat brain tumors. Current study aims to quantitatively evaluate the relative biological effectiveness (RBE) and dose distributions during the combined BNCT and GdNCT modalities through a hybrid Monte Carlo (MC) simulation approach. Snyder head phantom as well as a cubic hypothetical tumor was at first modeled by Geant4 MC Code. Then, the energy spectra and dose distribution relevant to the released secondary particles during the combined Gd/BNCT were scored for different concentrations of 157Gd and 10B inside tumor volume. Finally, the scored energy spectra were imported to the MCDS code to estimate both RBESSB and RBEDSB values for different 157Gd concentrations. The results showed that combined Gd/BNCT increases the fluence-averaged RBESSB values by about 1.7 times when 157Gd concentration increments from 0 to 2000µg/g for both considered cell oxygen levels (pO2=10% and 100%). Besides, a reduction of about 26% was found for fluence-averaged RBEDSB values with an increment of 157Gd concentration in tumor volume. From the results, it can be concluded that combined Gd/BNCT technique can improve tumor coverage with higher dose levels but in the expense of RBEDSB reduction which can affect the clinical efficacy of the NCT technique.

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.

Receptor Ligand-Free Mesoporous Silica Nanoparticles: A Streamlined Strategy for Targeted Drug Delivery across the Blood-Brain Barrier.

Mesoporous silica nanoparticles (MSNs) represent a promising avenue for targeted brain tumor therapy. However, the blood-brain barrier (BBB) often presents a formidable obstacle to efficient drug delivery. This study introduces a ligand-free PEGylated MSN variant (RMSN25-PEG-TA) with a 25 nm size and a slight positive charge, which exhibits superior BBB penetration. Utilizing two-photon imaging, RMSN25-PEG-TA particles remained in circulation for over 24 h, indicating significant traversal beyond the cerebrovascular realm. Importantly, DOX@RMSN25-PEG-TA, our MSN loaded with doxorubicin (DOX), harnessed the enhanced permeability and retention (EPR) effect to achieve a 6-fold increase in brain accumulation compared to free DOX. In vivo evaluations confirmed the potent inhibition of orthotopic glioma growth by DOX@RMSN25-PEG-TA, extending survival rates in spontaneous brain tumor models by over 28% and offering an improved biosafety profile. Advanced LC-MS/MS investigations unveiled a distinctive protein corona surrounding RMSN25-PEG-TA, suggesting proteins such as apolipoprotein E and albumin could play pivotal roles in enabling its BBB penetration. Our results underscore the potential of ligand-free MSNs in treating brain tumors, which supports the development of future drug-nanoparticle design paradigms.

A Scoping Review of Focused Ultrasound Enhanced Drug Delivery for Across the Blood-Brain Barrier for Brain Tumors.

Previous mechanisms of opening the blood-brain barrier (BBB) created a hypertonic environment. Focused ultrasound (FUS) has recently been introduced as a means of controlled BBB opening. Here, we performed a scoping review to assess the advances in drug delivery across the BBB for treatment of brain tumors to identify advances and literature gaps. A review of current literature was conducted through a MEDLINE search inclusive of articles on FUS, BBB, and brain tumor barrier, including human, modeling, and animal studies written in English. Using the Rayyan platform, 2 reviewers (J.P and C.Y) identified 967 publications. 224 were chosen to review after a title screen. Ultimately 98 were reviewed. The scoping review was designed to address the following questions: (1) What FUS technology improvements have been made to augment drug delivery for brain tumors? (2) What drug delivery improvements have occurred to ensure better uptake in the target tissue for brain tumors? Microbubbles (MB) with FUS are used for BBB opening (BBBO) through cavitation to increase its permeability. Drug delivery into the central nervous system can be combined with MB to enhance transport of therapeutic agents to target brain tissue resulting in suppression of tumor growth and prolonging survival rate, as well as reducing systemic toxicity and degradation rate. There is accumulating evidence demonstrating that drug delivery through BBBO with FUS-MB improves drug concentrations and provides a better impact on tumor growth and survival rates, compared with drug-only treatments. Here, we review the role of FUS in BBBO. Identified gaps in the literature include impact of tumor microenvironment and extracellular space, improved understanding and control of MB and drug delivery, further work on ideal pharmacologics for delivery, and clinical use.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Laser interstitial thermal therapy (LITT) for pediatric low-grade glioma—case presentations and lessons learned

BackgroundThe surgical treatment of brain tumors has developed over time, offering customized strategies for patients and their specific lesions. One of the most recent advances in pediatric neuro-oncological surgery is laser interstitial thermal therapy (LITT). However, its effectiveness and indications are still being evaluated. The aim of this work is to review the current literature on LITT for pediatric low-grade gliomas (pLGG) and evaluate our initial results in this context.MethodsWe retrospectively reviewed our pediatric neurosurgery database for patients who received LITT treatment between November 2019 and December 2023. We collected data on the indications for LITT, technical issues during the procedure, and clinical and radiological follow-up.ResultsThree patients underwent 5 LITT procedures for pLGG. The lesion was thalamo-peduncular in one patient, cingulate in one, and deep parietal in one patient. Two patients had a previous open resection done and were diagnosed with pLGG. One patient underwent a stereotaxic biopsy during the LITT procedure that was non-diagnostic. The same patient underwent a later open resection of the tumor in the cingulate gyrus. There were no surgical complications and all patients were discharged home on the first post-operative day. The follow-up period was between 20 and 40 months. Radiological follow-up showed a progressive reduction of the tumor in patients with LGG.ConclusionLaser interstitial thermal therapy is a minimally invasive treatment that shows promise in treating deep-seated pLGG in children. The treatment has demonstrated a reduction in tumor volume, and the positive results continue over time. LITT can be used as an alternative treatment for tumors located in areas that are difficult to access surgically or in cases where other standard treatment options have failed.

Brain MR image segmentation for tumor detection based on Riesz probability distributions

ABSTRACT This research introduces a new approach using the Riesz mixture model for medical image segmentation, specifically for diagnosing and treating brain tumors. We developed a novel technique for pixel classification based on the Riesz distribution, which is generated using an extended Bartlett decomposition. Our work is pioneering, as there are no existing studies addressing this issue in the literature. We aim to demonstrate the effectiveness of this distribution for brain image segmentation. We used the Expectation-Maximization algorithm to estimate the mixture parameters. To validate our segmentation algorithm, we conducted a comparative study with a recent method based on the Wishart distribution using Matlab software. Experiments with the Riesz mixture model showed that our method produces more intuitive results with a recognition rate of 94.52%. These results confirm the reliability of our method in detecting tumors using both synthetic and real brain images.

The role of focused ultrasound for pediatric brain tumors: current insights and future implications on treatment strategies.

Focused ultrasound (FUS) is an innovative and emerging technology for the treatment of adult and pediatric brain tumors and illustrates the intersection of various specialized fields, including neurosurgery, neuro-oncology, radiation oncology, and biomedical engineering. The authors provide a comprehensive overview of the application and implications of FUS in treating pediatric brain tumors, with a special focus on pediatric low-grade gliomas (pLGGs) and the evolving landscape of this technology and its clinical utility. The fundamental principles of FUS include its ability to induce thermal ablation or enhance drug delivery through transient blood-brain barrier (BBB) disruption, emphasizing the adaptability of high-intensity focused ultrasound (HIFU) and low-intensity focused ultrasound (LIFU) applications. Several ongoing clinical trials explore the potential of FUS in offering alternative therapeutic strategies for pathologies where conventional treatments fall short, specifically centrally-located benign CNS tumors and diffuse intrinsic pontine glioma (DIPG). A case illustration involving the use of HIFU for pilocytic astrocytoma is presented. Discussions regarding future applications of FUS for the treatment of gliomas include improved drug delivery, immunomodulation, radiosensitization, and other technological advancements.

Investigation of high-dose radiotherapy's effect on brain structure aggravated cognitive impairment and deteriorated patient psychological status in brain tumor treatment

This study aims to investigate the potential impact of high-dose radiotherapy (RT) on brain structure, cognitive impairment, and the psychological status of patients undergoing brain tumor treatment. We recruited and grouped 144 RT-treated patients with brain tumors into the Low dose group (N = 72) and the High dose group (N = 72) according to the RT dose applied. Patient data were collected by using the HADS and QLQ-BN20 system for subsequent analysis and comparison. Our analysis showed no significant correlation between the RT doses and the clinicopathological characteristics. We found that a high dose of RT could aggravate cognitive impairment and deteriorate patient role functioning, indicated by a higher MMSE and worsened role functioning in the High dose group. However, the depression status, social functioning, and global health status were comparable between the High dose group and the Low dose group at Month 0 and Month 1, while being worsened in the High dose group at Month 3, indicating the potential long-term deterioration of depression status in brain tumor patients induced by high-dose RT. By comparing patient data at Month 0, Month 1, Month 3, Month 6, and Month 9 after RT, we found that during RT treatment, RT at a high dose could aggravate cognitive impairment in the short term and lead to worsened patient role functioning, and even deteriorate the overall psychological health status of patients in the long term.

The development of fatigue after treatment for pediatric brain tumors does not differ between tumor locations.

Children and adolescents treated for a brain tumor suffer from more fatigue than survivors of other types of childhood cancer. As tumor location might be predictive of fatigue, our aim was to investigate the longitudinal development of fatigue in children with brain tumors and risk factors for fatigue separately for different tumor locations. Fatigue was assessed 1235 times for 425 participants. Self-report versions of PedsQL Multidimensional Fatigue Scale were used to repeatedly assess fatigue from the end of treatment up to 8years later. Mixed models were used to analyze fatigue over time and determinants separately for infratentorial (N=205), supratentorial hemispheric (N=91), and supratentorial midline tumors (N=129). Cognitive fatigue worsened with time, while sleep-rest and general fatigue first decreased and then increased. There was no difference in fatigue between the tumor locations, but the risk factors differed when stratified by location. Radiotherapy was associated with more fatigue for infratentorial tumors, and centralization of care was associated with less fatigue for the supratentorial midline tumors. For supratentorial hemispheric tumors, female sex was associated with more fatigue. Higher parental education was associated with less fatigue regardless of tumor location. The development of fatigue seems to be more related to sociodemographic and treatment variables than to tumor location. Healthcare providers need to be aware that fatigue may develop in the years following end of treatment, and that patients with a low/middle educational family background might be more vulnerable and in need of targeted support.

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.

Development of moyamoya arteriopathy following treatment of intracranial tumors: clinical and radiographic characterization.

Moyamoya arteriopathy can develop in patients with brain tumors, particularly when associated with neurofibromatosis type 1 (NF1) or cranial irradiation. The present study aimed to analyze the clinical outcomes of moyamoya after brain tumor treatment and elucidate the effect of revascularization on brain tumors. The authors retrospectively reviewed clinical and radiographic findings in 27 patients with brain tumors who developed moyamoya requiring revascularization surgery between January 1985 and June 2017 at a single institution. The long-term clinical and neuroimaging-based outcomes were analyzed. Among 27 patients, 22 patients underwent radiotherapy, and 12 patients had NF1. The mean ages at diagnosis of brain tumors and moyamoya were 4.4 years and 10.3 years, respectively. The mean interval between radiotherapy and moyamoya diagnosis was 4.0 years. The mean follow-up period after revascularization surgery was 8.5 years. Among 46 affected hemispheres in 27 patients, the patients who underwent radiotherapy (30 hemispheres in 22 patients) had a higher incidence of Suzuki stage 5 or 6 (20% [6/30] vs 0% [0/8]) and infarction (63.6% [14/22] vs 0% [0/5]) compared with patients without radiotherapy (8 hemispheres in 5 patients). After revascularization, stroke occurred in 4 patients, and 6 hemispheres showed Matsushima grade C, all of which occurred in patients with a history of radiotherapy. The residual brain tumors progressed in 4 of 21 patients (19%) after revascularization, comparable to the progression rates of brain tumors without revascularization in previous literature. Patients with brain tumors can develop moyamoya that exhibits characteristic clinical and radiographic features of idiopathic MMD. Moyamoya associated with cranial irradiation has a higher incidence of stroke with less capacity for revascularization, requiring thorough evaluations and timely treatment. Revascularization does not appear to have any effect on the progression of existing brain tumors.

Multi-modal fusion deep transfer learning for accurate brain tumor classification using magnetic resonance imaging images

Early identification and treatment of brain tumors depend critically on accurate classification. Accurate brain tumor classification in medical imaging is essential for clinical decisions and individualized treatment plans. This paper introduces a novel method for classifying brain tumors called multimodal fusion deep transfer learning (MMFDTL) using original, contoured, and annotated magnetic resonance imaging (MRI) images to showcase its capabilities. The MMFDTL can capture complex tumor features frequently missed in analyzing individual modalities. The MMFDTL model employs three deep learning models for extracting features VGG16, Inception V3, and ResNet 50. The accuracy rate improves when combined with decision based multimodal fusion. It produces impressive outcomes of sensitivity 92.96%, specificity 98.54%, precision 93.60%, accuracy 98.80%, F1-score 93.26%, and kappa 91.86%. This research can improve medical imaging and brain tumor analysis through its multi modal fusion approach. It could give healthcare practitioners vital insights for personalized treatment plans and informed decision making.

Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery

PURPOSEDose painting radiotherapy delivers a non-uniform dose to tumours to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife (GK) machines making large-volume brain tumour treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on SRS plan quality has not yet been studied. This research investigates the plan quality achievable for GK SRS dose painting treatments when using optimisation techniques and automated isocentre placement in treatment planning. METHODS & MATERIALSDose painting prescription functions with varying parameters are applied to convert voxel image intensities to prescriptions for ten sample cases. To study achievable plan quality and optimisation, clinically-placed isocentres are used with each dose painting prescription, and optimised using a semi-infinite linear programming formulation. To study automated isocentre placement, a grassfire sphere-packing algorithm and a clinically available Leksell Gamma Plan isocentre fill algorithm are used. Plan quality for each optimised treatment plan is measured with dose painting SRS metrics. RESULTSOptimisation can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show better achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocentre placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocentres improves plan quality. The computational solve time for optimisation is within 5 minutes in most cases, which is suitable for clinical planning. CONCLUSIONSThe impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimisation and automated isocentre placement are shown as possible treatment planning methods to obtain high quality plans.

Oral pathology following treatment of brain tumors in childhood

Relevance. The limited selectivity of antitumor therapy for central nervous system neoplasms entails the risk of dental morbidity in patients.The aim of the study is to assess the severity of damage to the dental hard tissues in patients with tumors of the posterior cranial fossa (medulloblastoma, astrocytoma) and other tumors of the central nervous system.Materials and methods. Three comparison groups were established based on the toxicity profiles of antitumor therapy among 251 patients at the "Russian Field" treatment and rehabilitation scientific center, to assess the characteristics of oral health status. The assessment of oral health status included diagnosing the carious process and calculating the DMFT index. The degree of damage to the dental hard tissues was evaluated on a four-point scale. The clinical manifestations of oral cavity pathology were compared using the Z-criterion for multiple pairwise comparisons of data, with adjustments for multiple testing (Multiple pairwise comparison Z-test for proportions with multiple testing correction) using the free R software version 3.5.2. The null hypothesis of equal indicators in the compared groups was rejected with a 95% confidence probability when comparing groups of patients.Results. Dental hard tissue pathology was significantly more prevalent in patients with medulloblastoma compared to those diagnosed with astrocytomas and other central nervous system tumors. Additionally, the prevalence of dental hard tissue pathology progressively decreased as the observation period following the completion of antitumor remission was extended.Conclusion. The most severe damage to dental hard tissues is observed in patients with medulloblastoma, who undergo more intensive antitumor therapy compared to those with CNS tumors of other histological types.

Brain Tumor Detection using Deep Learning

Abstract: A tumor is an abnormal swelling or growth that is caused by the division of the cells in the nervous system. A brain tumor is one of the most dangerous types of tumors. There are four different types of brain tumor. The treatment for brain tumors is determined by the type, grade, and locationof the tumor. If the tumor is not detected early, it can be fatal. MRI images are used to diagnose brain tumors. The accuracy of the results depends on the expertise and domain knowledge of the experts, and it is also a very time-consuming and costly process. In order to overcome these limitations, several deep learning algorithms for the detection of brain tumors have been proposed.

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors.

The diagnosis and treatment of brain tumors have greatly benefited from extensive research in traditional radiomics, leading to improved efficiency for clinicians. With the rapid development of cutting-edge technologies, especially deep learning, further improvements in accuracy and automation are expected. In this study, we explored a hybrid deep learning scheme that integrates several advanced techniques to achieve reliable diagnosis of primary brain tumors with enhanced classification performance and interpretability. This study retrospectively included 230 patients with primary brain tumors, including 97 meningiomas, 66 gliomas and 67 pituitary tumors, from the First Affiliated Hospital of Yangtze University. The effectiveness of the proposed scheme was validated by the included data and a commonly used data. Based on super-resolution reconstruction and dynamic learning rate annealing strategies, we compared the classification results of several deep learning models. The multi-classification performance was further improved by combining feature transfer and machine learning. Classification performance metrics included accuracy (ACC), area under the curve (AUC), sensitivity (SEN), and specificity (SPE). In the deep learning tests conducted on two datasets, the DenseNet121 model achieved the highest classification performance, with five-test accuracies of 0.989 ± 0.006 and 0.967 ± 0.013, and AUCs of 0.999 ± 0.001 and 0.994 ± 0.005, respectively. In the hybrid deep learning tests, LightGBM, a promising classifier, achieved accuracies of 0.989 and 0.984, which were improved from the original deep learning scheme of 0.987 and 0.965. Sensitivities for both datasets were 0.985, specificities were 0.988 and 0.984, respectively, and relatively desirable receiver operating characteristic (ROC) curves were obtained. In addition, model visualization studies further verified the reliability and interpretability of the results. These results illustrated that deep learning models combining several advanced technologies can reliably improve the performance, automation, and interpretability of primary brain tumor diagnosis, which is crucial for further brain tumor diagnostic research and individualized treatment.