Could Cerebral Inflammatory Lesions be the Cellular Origin of Primary Central Nervous System Lymphoma?

INTRODUCTION: Primary central nervous system lymphoma (PCNSL) presents a diagnostic enigma due to the inherent absence of lymphoid tissue in the central nervous system (CNS). The hypothesis posits that lymphocytes infiltrating the CNS during inflammatory responses could represent a cellular source for PCNSL, challenging traditional understandings of its etiology. PATIENT CONCERNS: In 2 illustrative cases, patients presented with neurological symptoms initially misdiagnosed as encephalitis and demyelinating disease, respectively. These diagnoses were established based on clinical assessments and initial biopsy findings. DIAGNOSIS: Subsequent biopsies, conducted months after the first signs of disease, confirmed the diagnosis of PCNSL in both patients. Identifying CD20-positive tumor cells was pivotal, indicating a B-cell lymphoma origin. INTERVENTIONS: Treatment strategies included high-dose methotrexate chemotherapy for both patients. In addition, the second patient underwent adjuvant whole-brain radiotherapy after the chemotherapy regimen. OUTCOMES: The therapeutic approach significantly reduced tumor size in both cases, with no evidence of recurrence observed during the follow-up period. This outcome underscores the potential efficacy of the chosen interventions. CONCLUSION: In response to inflammatory lesions, lymphocyte infiltration into the CNS may serve as a pivotal origin for tumor cells in PCNSL. These cases highlight the complexity of diagnosing CNS disorders and suggest that various forms of encephalitis in the early stages could influence the prognosis of lymphoma. This insight into the cellular origins and treatment responses of PCNSL contributes to a broader understanding of its pathophysiology and management.

Brainstem anesthesia during awake craniotomy: illustrative case.

BACKGROUND: Awake craniotomy (AC) is performed to remove the lesions near or in eloquent areas, during which the patients are alert and without any airway instrument. Apnea is a severe complication in AC. Here, the authors describe a case of sudden apnea induced by unexpected local anesthesia of the brainstem during AC. OBSERVATIONS: A 42-year-old male underwent AC for a large, recurrent, bilateral frontal lobe mass and experienced transient apnea and loss of brainstem reflexes during the surgery. The patient recovered spontaneous breath rhythm just a few minutes after the removal of a lidocaine cotton pledget, which was found near the patient's midbrain. Then the patient awoke and cooperated to finish the surgery. LESSONS: The administration of a local anesthetic subdurally in AC is common but risky. The scouring action of cerebral spinal fluid can spread those agents and cause unexpected brainstem anesthesia. A lower concentration of the anesthetic and keeping away from the cistern can make it safer.

Ferroptosis and mitochondrial dysfunction in acute central nervous system injury.

Acute central nervous system injuries (ACNSI), encompassing traumatic brain injury (TBI), non-traumatic brain injury like stroke and encephalomeningitis, as well as spinal cord injuries, are linked to significant rates of disability and mortality globally. Nevertheless, effective and feasible treatment plans are still to be formulated. There are primary and secondary injuries occurred after ACNSI. Most ACNSIs exhibit comparable secondary injuries, which offer numerous potential therapeutic targets for enhancing clinical outcomes. Ferroptosis, a newly discovered form of cell death, is characterized as a lipid peroxidation process that is dependent on iron and oxidative conditions, which is also indispensable to mitochondria. Ferroptosis play a vital role in many neuropathological pathways, and ACNSIs may induce mitochondrial dysfunction, thereby indicating the essentiality of the mitochondrial connection to ferroptosis in ACNSIs. Nevertheless, there remains a lack of clarity regarding the involvement of mitochondria in the occurrence of ferroptosis as a secondary injuries of ACNSIs. In recent studies, anti-ferroptosis agents such as the ferroptosis inhibitor Ferrostain-1 and iron chelation therapy have shown potential in ameliorating the deleterious effects of ferroptosis in cases of traumatic ACNSI. The importance of this evidence is extremely significant in relation to the research and control of ACNSIs. Therefore, our review aims to provide researchers focusing on enhancing the therapeutic outcomes of ACNSIs with valuable insights by summarizing the physiopathological mechanisms of ACNSIs and exploring the correlation between ferroptosis, mitochondrial dysfunction, and ACNSIs.

Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response.

Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.

Maximal safe resection of diffuse lower grade gliomas primarily within central lobe using cortical/subcortical direct electrical stimulation under awake craniotomy.

Background: Diffuse lower-grade glioma (DLGG) in the central lobe is a challenge for safe resection procedures. To improve the extent of resection and reduce the risk of postoperative neurological deficits, we performed an awake craniotomy with cortical-subcortical direct electrical stimulation (DES) mapping for patients with DLGG located primarily within the central lobe. We investigated the outcomes of cortical-subcortical brain mapping using DES in an awake craniotomy for central lobe DLGG resection. Methods: We performed a retrospective analysis of clinical data of a cohort of consecutively treated patients from February 2017 to August 2021 with diffuse lower-grade gliomas located primarily within the central lobe. All patients underwent awake craniotomy with DES for cortical and subcortical mapping of eloquent brain areas, neuronavigation, and/or ultrasound to identify tumor location. Tumors were removed according to functional boundaries. Maximum safe tumor resection was the surgical objective for all patients. Results: Thirteen patients underwent 15 awake craniotomies with intraoperative mapping of eloquent cortices and subcortical fibers using DES. Maximum safe tumor resection was achieved according to functional boundaries in all patients. The pre-operative tumor volumes ranged from 4.3 cm3 to 137.3 cm3 (median 19.2 cm3). The mean extent of tumor resection was 94.6%, with eight cases (53.3%) achieving total resection, four (26.7%) subtotal and three (20.0%) partial. The mean tumor residue was 1.2 cm3. All patients experienced early postoperative neurological deficits or worsening conditions. Three patients (20.0%) experienced late postoperative neurological deficits at the 3-month follow-up, including one moderate and two mild neurological deficits. None of the patients experienced late onset severe neurological impairments post-operatively. Ten patients with 12 tumor resections (80.0%) had resumed activities of daily living at the 3-month follow-up. Among 14 patients with pre-operative epilepsy, 12 (85.7%) were seizure-free after treatment with antiepileptic drugs 7 days after surgery up to the last follow-up. Conclusions: DLGG located primarily in the central lobe deemed inoperable can be safely resected using awake craniotomy with intraoperative DES without severe permanent neurological sequelae. Patients experienced an improved quality of life in terms of seizure control.

Psychometric properties of the post-traumatic stress disorder checklist for DSM-5 (PCL-5) in Chinese stroke patients.

BACKGROUND: Stroke is a devastating disease and can be sufficiently traumatic to induce post-traumatic stress disorder (PTSD). Post-stroke PTSD is attracting increasing attention, but there was no study assessing the psychometric properties of the PCL-5 in stroke populations. Our study was conducted to examine the psychometric properties of the PTSD Checklist for DSM-5 (PCL-5) in Chinese stroke patients. METHODS: This was a cross-sectional observational study conducted at our hospital. Three hundred and forty-eight Chinese stroke patients came to our hospital for outpatient service were recruited. They were instructed to complete the PCL-5 scales and were interviewed for PTSD diagnosis with the Clinician Administered PTSD Scale for DSM-5 (CAPS-5). The cutoff scores, reliability and validity of the PCL-5 were analyzed. RESULTS: PCL-5 scores in our sample were positively skewed, suggesting low levels of PTSD symptoms. The reliability of PCL-5 was good. Exploratory and confirmatory factor analyses indicated acceptable construct validity, and confirmed the multi-dimensionality of the PCL-5. By CFA analysis, the seven-factor hybrid model demonstrated the best model fit. The PCL-5 also showed good convergent validity and discriminant validity. Receiver operating characteristic (ROC) analyses revealed a PCL-5 score of 37 achieved optimal sensitivity and specificity for detecting PTSD. CONCLUSIONS: Our findings supported the use of PCL-5 as a psychometrically adequate measure of post-stroke PTSD in the Chinese patients.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Supportive psychological therapy can effectively treat post-stroke post-traumatic stress disorder at the early stage.

Post-traumatic stress disorder (PTSD) can develop after stroke attacks, and its rate ranges from 4 to 37% in the stroke population. Suffering from PTSD not only decreases stroke patient's quality of life, but also relates to their non-adherence of treatment. Since strokes often recur and progress, long-term medical management is especially important. However, previous studies generally focused on the epidemiological characteristics of post-stroke PTSD, while there are literally no studies on the psychological intervention. In our study, 170 patients with a first-ever stroke during the acute phase were recruited. They were randomized into Psycho-therapy group 1 and Control group 1, and were administered with preventive intervention for PTSD or routine health education, respectively. At 2-month follow-up, PTSD symptoms were evaluated. Participants who were diagnosed with post-stroke PTSD were further randomized into Psycho-therapy group 2 and Control group 2, and received supportive therapy or routine health counseling, respectively. At 6-month follow-up (1°month after the therapy was completed), PTSD symptoms were re-evaluated. Our results showed that at 2-month, the PTSD incidence in our series was 11.69%, and the severity of stroke was the only risk factor for PTSD development. The preventive intervention was not superior to routine health education for PTSD prevention. At 6-month, results indicated the supportive therapy did have a fine effect in ameliorating symptoms for diagnosed PTSD patients, superior to routine health counseling. Thus, our study was the first to provide evidence that the supportive therapy was effective in treating post-stroke PTSD early after its diagnosis. This clinical trial was preregistered on www.chictr.org.cn (ChiCTR2100048411).

Recognition of Freezing of Gait in Parkinson's Disease Based on Machine Vision.

Background: Freezing of gait (FOG) is a common clinical manifestation of Parkinson's disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment. Objective: A method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital. Methods: In this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model. Results: We adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%. Conclusion: A method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

A Novel Intraoperative Brain Mapping Integrated Task-Presentation Platform.

BACKGROUND: To be efficient, intraoperative task-presentation systems must accurately present various language and cognitive tasks to patients undergoing awake surgery, and record behavioral data without compromising convenience of surgery. OBJECTIVE: To present an integrated brain mapping task-presentation system we developed and evaluate its effectiveness in intraoperative task presentation. METHODS: The Brain Mapping Interactive Stimulation System (Brain MISS) is a flexible task presentation system that adjusts for patient comfort, needs of the surgeon, and operating team, with multivideo recording for patients' behavior. A total of 48 patients from 3 centers underwent intraoperative language task test during awake brain surgery with the Brain MISS. Each patient was assigned 5 questions each on picture naming, reading, and listening comprehension before and during awake surgeries. The accuracy of intraoperative stimulus-response (without electrical stimulation) was recorded. The Brain MISS was to be considered effective, if the lower limit of 95% CI of patients' intraoperative response was ≥80% and also if the accuracy of intraoperative response of all patients was statistically higher than 80%. RESULTS: All patients successfully underwent intraoperative assessment with the Brain MISS. The overall accuracy of stimulus response was 95.8% (95% CI 90.18%-100.00%), with the lower limit being higher than 80% and the response accuracy also significantly being higher than 80% in all patients (P = .006). CONCLUSION: The Brain MISS is a portable and effective system for presenting and streamlining complicated language and cognitive tasks during awake surgery. It can also record standardized patient response data for neuroscientific research.

Clinical practice guidelines for the management of adult diffuse gliomas.

To follow the revision of the fourth edition of WHO classification and the recent progress on the management of diffuse gliomas, the joint guideline committee of Chinese Glioma Cooperative Group (CGCG), Society for Neuro-Oncology of China (SNO-China) and Chinese Brain Cancer Association (CBCA) updated the clinical practice guideline. It provides recommendations for diagnostic and management decisions, and for limiting unnecessary treatments and cost. The recommendations focus on molecular and pathological diagnostics, and the main treatment modalities of surgery, radiotherapy, and chemotherapy. In this guideline, we also integrated the results of some clinical trials of immune therapies and target therapies, which we think are ongoing future directions. The guideline should serve as an application for all professionals involved in the management of patients with adult diffuse glioma and also a source of knowledge for insurance companies and other institutions involved in the cost regulation of cancer care in China and other countries.

Glioma Growth Prediction via Generative Adversarial Learning from Multi-Time Points Magnetic Resonance Images.

Gliomas are the most dominant and lethal type of brain tumors. Growth prediction is significant to quantify tumor aggressiveness, improve therapy planning, and estimate patients' survival time. This is commonly addressed in literature using mathematical models guided by multi-time point scans of multi/single-modal data for the same subject. However, these models are mechanism-based and heavily rely on complicated mathematical formulations of partial differential equations with few parameters that are insufficient to capture different patterns and other characteristics of gliomas. In this paper, we propose a 3D generative adversarial networks (GANs) for glioma growth prediction. Specifically, we stack 2 GANs with conditional initialization of segmented feature maps. Furthermore, we employ Dice loss in our objective function and devised 3D U-Net architecture for better image generation. The proposed method is trained and validated using 3D patch-based strategy on real magnetic resonance images of 9 subjects with 3 time points. Experimental results show that the proposed method can be successfully used for glioma growth prediction with satisfactory performance.

GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images.

Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressiveness, and estimate patients' survival time towards precision medicine. Studying tumor growth prediction basically requires multiple time points of single or multimodal medical images of the same patient. Recent models are based on complex mathematical formulations that basically rely on a system of partial differential equations, e.g. reaction diffusion model, to capture the diffusion and proliferation of tumor cells in the surrounding tissue. However, these models usually have small number of parameters that are insufficient to capture different patterns and other characteristics of the tumors. In addition, such models consider tumor growth independently for each subject, not being able to get benefit from possible common growth patterns existed in the whole population under study. In this paper, we propose a novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, for growth prediction of glioma. Specifically, we use stacked conditional GANs with a novel objective function that includes both l1 and Dice losses. Moreover, we use segmented feature maps to guide the generator for better generated images. Our generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features and thus have a better generated image. The proposed method is trained and tested on 18 subjects with 3 time points (9 subjects from collaborative hospital and 9 subjects from BRATS 2014 dataset). Results show that our proposed GP-GAN outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.

Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.

PURPOSE: Isocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI). METHODS: In this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non-enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all-relevant feature selection and a random forest classification for predicting IDH1. Four single-region models and a model combining all-region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients). RESULTS: Among the four single-region radiomics models, the edema model achieved the best accuracy of 96% and the best F1-score of 0.75 while the non-enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor-core model (accuracy 0.96, AUC 0.86 and F1-score 0.75) and the whole-tumor model (accuracy 0.96, AUC 0.88 and F1-score 0.75) was slightly better than the single-regional models. The 8-feature all-region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1-score 0.78. Among all models, the model combining all-region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1-score 0.84. CONCLUSIONS: The radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all-region features performed better than the single-region models, while combining age with all-region features achieved the best performance.

A Coupled Modified Reaction Diffusion and Biomechanical Models for Cerebral Tumor Growth Modeling in Presence of Treatment.

Tumor growth modeling at macroscopic level from multimodal images can help in predicting the future evolution of tumor and the treatment planning. This can be achieved using mathematical models where multi time-point images are available. In this paper, we propose a coupled modified reaction diffusion model that measures tumor invasion and infiltration with biomechanical model to consider tumor mass effect. In addition, our model considers treatment effects from radiotherapy and/or chemotherapy if any. The chemotherapy effect is included via a modified log-kill method to consider tissue heterogeneity while radiotherapy effect is considered using the linear quadratic model. We test the proposed model on both synthetic and 6 real datasets of low grade glioma cases with and without treatments. Experimental results of the proposed model on the clinical magnetic resonance images show that our model can simulate the tumor growth with good accuracy and effectively include the treatment effects.

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

OBJECTIVES: To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM). METHODS: In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors. RESULTS: The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis. CONCLUSIONS: Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. KEY POINTS: • Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.

A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme.

In fully-automatic radiomics model for predicting overall survival (OS) of glioblastoma multiforme (GBM) patients, the effect of image standardization parameters such as voxel size, quantization method and gray level on model reproducibility and prognostic performance are still unclear. In this study, 45792 multiregional radiomics features were automatically extracted from multi-modality MR images with different voxel sizes, quantization methods, and gray levels. The feature reproducibility and prognostic performance were assessed. Multiparametric and fixed-parameter radiomics signatures were constructed based on a training cohort (60 patients). In an independent validation cohort (32 patients), the multiparametric signature achieved better performance for OS prediction (C-Index = 0.705, 95% CI: 0.672, 0.738) and significant stratification of patients into high- and low-risk groups (P = 0.0040, HR = 3.29, 95% CI: 1.40, 7.70), which outperformed the fixed-parameter signatures and conventional factors such as age, Karnofsky Performance Score and tumor volume. This study demonstrated that voxel size, quantization method and gray level had influence on reproducibility and prognosis of radiomics features for GBM OS prediction. An automatic method to determine the optimal parameter settings was provided. It indicated that multiparametric radiomics signature had the potential of offering better prognostic performance than fixed-parameter signatures.

Knockdown of HDAC1 expression suppresses invasion and induces apoptosis in glioma cells.

Glioma is the most common malignant tumor of the central nervous system, with a low survival rate of five years worldwide. Although high expression and prognostic value of histone deacetylase 1 (HDAC1) have been recently reported in various types of human tumors, the molecular mechanism underlying the biological function of HDAC1 in glioma is still unclear. We found that HDAC1 was elevated in glioma tissues and cell lines. HDAC1 expression was closely related with pathological grade and overall survival of patients with gliomas. Downregulation of HDAC1 inhibited cell proliferation, prevented invasion of glioma cell lines, and induced cell apoptosis. The expression of apoptosis and metastasis related molecules were detected by RT-PCR and Western blot, respectively, in U251 and T98G cells with HDAC1 knockdown. We found that HDAC1 knockdown upregulated expression of BIM, BAX, cleaved CASPASE3 and E-CADHERIN, and decreased expression of TWIST1, SNAIL and MMP9 in U251 and T98G cells with HDAC1 knockdown. In vivo data showed that knockdown of HDAC1 inhibited tumor growth in nude mice. In summary, HDAC1 may therefore be considered an unfavorable progression indicator for glioma patients, and may also serve as a potential therapeutic target.

Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment.

Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments' effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies.