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Science Advances May 2024Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor...
Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. Here, we have utilized orthogonal imaging mass cytometry with single-cell and spatial transcriptomic approaches to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME). Our results show that different myeloid populations have distinct and reproducible compartmentalization patterns in the GBM TME that is driven by tissue hypoxia, regional chemokine signaling, and varied homotypic and heterotypic cellular interactions. We subsequently identified specific tumor subregions in GBM, based on composition of identified myeloid cell populations, that were linked to patient survival. Our results provide insight into the spatial organization of myeloid cell subpopulations in GBM, and how this is predictive of clinical outcome.
Topics: Glioblastoma; Humans; Myeloid Cells; Tumor Microenvironment; Brain Neoplasms; Cell Line, Tumor; Single-Cell Analysis; Hypoxia; Gene Expression Profiling
PubMed: 38758780
DOI: 10.1126/sciadv.adj3301 -
PloS One 2024Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results.... (Meta-Analysis)
Meta-Analysis
Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.
Topics: Meningioma; Humans; Biomarkers, Tumor; Prognosis; Meningeal Neoplasms; DNA Topoisomerases, Type II; Ki-67 Antigen; Tumor Suppressor Protein p53; Vascular Endothelial Growth Factor A; Immunohistochemistry; Poly-ADP-Ribose Binding Proteins
PubMed: 38758750
DOI: 10.1371/journal.pone.0303337 -
Frontiers in Endocrinology 2024The 2022 World Health Organization (WHO) classification of pituitary neuroendocrine tumour (PitNET) supersedes the previous one in 2017 and further consolidates the role...
BACKGROUND
The 2022 World Health Organization (WHO) classification of pituitary neuroendocrine tumour (PitNET) supersedes the previous one in 2017 and further consolidates the role of transcription factors (TF) in the diagnosis of PitNET. Here, we investigated the clinical utility of the 2022 WHO classification, as compared to that of 2017, in a cohort of patients with non-functioning PitNET (NF-PitNET).
METHODS
A total of 113 NF-PitNET patients who underwent resection between 2010 and 2021, and had follow-up at Queen Mary Hospital, Hong Kong, were recruited. Surgical specimens were re-stained for the three TF: steroidogenic factor (SF-1), T-box family member TBX19 (TPIT) and POU class 1 homeobox 1 (Pit-1). The associations of different NF-PitNET subtypes with tumour-related outcomes were evaluated by logistic and Cox regression analyses.
RESULTS
Based on the 2022 WHO classification, the majority of NF-PitNET was SF-1-lineage tumours (58.4%), followed by TPIT-lineage tumours (18.6%), tumours with no distinct lineage (16.8%) and Pit-1-lineage tumours (6.2%). Despite fewer entities than the 2017 classification, significant differences in disease-free survival were present amongst these four subtypes (Log-rank test p=0.003), specifically between SF-1-lineage PitNET and PitNET without distinct lineage (Log-rank test p<0.001). In multivariable Cox regression analysis, the subtype of PitNET without distinct lineage (HR 3.02, 95% CI 1.28-7.16, p=0.012), together with tumour volume (HR 1.04, 95% CI 1.01-1.07, p=0.017), were independent predictors of a composite of residual or recurrent disease.
CONCLUSION
The 2022 WHO classification of PitNET is a clinically useful TF and lineage-based system for subtyping NF-PitNET with different tumour behaviour and prognosis.
Topics: Humans; World Health Organization; Female; Male; Middle Aged; Pituitary Neoplasms; Neuroendocrine Tumors; Adult; Aged; Prognosis; Young Adult; Follow-Up Studies; T-Box Domain Proteins
PubMed: 38756997
DOI: 10.3389/fendo.2024.1368944 -
Scientific Reports May 2024We evaluated the prognostic value of hypoalbuminemia in context of various biomarkers at baseline, including clinical, genomic, transcriptomic, and blood-based markers,...
We evaluated the prognostic value of hypoalbuminemia in context of various biomarkers at baseline, including clinical, genomic, transcriptomic, and blood-based markers, in patients with metastatic melanoma treated with anti-PD-1 monotherapy or anti-PD-1/anti-CTLA-4 combination therapy (n = 178). An independent validation cohort (n = 79) was used to validate the performance of hypoalbuminemia compared to serum LDH (lactate dehydrogenase) levels. Pre-treatment hypoalbuminemia emerged as the strongest predictor of poor outcome for both OS (HR = 4.01, 95% CI 2.10-7.67, Cox P = 2.63e-05) and PFS (HR = 3.72, 95% CI 2.06-6.73, Cox P = 1.38e-05) in univariate analysis. In multivariate analysis, the association of hypoalbuminemia with PFS was independent of serum LDH, IFN-γ signature expression, TMB, age, ECOG PS, treatment line, treatment type (combination or monotherapy), brain and liver metastasis (HR = 2.76, 95% CI 1.24-6.13, Cox P = 0.0131). Our validation cohort confirmed the prognostic power of hypoalbuminemia for OS (HR = 1.98, 95% CI 1.16-3.38; Cox P = 0.0127) and was complementary to serum LDH in analyses for both OS (LDH-adjusted HR = 2.12, 95% CI 1.2-3.72, Cox P = 0.00925) and PFS (LDH-adjusted HR = 1.91, 95% CI 1.08-3.38, Cox P = 0.0261). In conclusion, pretreatment hypoalbuminemia was a powerful predictor of outcome in ICI in melanoma and showed remarkable complementarity to previously established biomarkers, including high LDH.
Topics: Humans; Melanoma; Female; Male; Middle Aged; Hypoalbuminemia; Biomarkers, Tumor; Aged; Immune Checkpoint Inhibitors; Prognosis; Programmed Cell Death 1 Receptor; Adult; Neoplasm Metastasis; L-Lactate Dehydrogenase; Aged, 80 and over; Multiomics
PubMed: 38755213
DOI: 10.1038/s41598-024-61150-y -
BMJ Open May 2024Cardiopulmonary complications and cognitive impairment following craniotomy have a significantly impact on the general health of individuals with brain tumours....
INTRODUCTION
Cardiopulmonary complications and cognitive impairment following craniotomy have a significantly impact on the general health of individuals with brain tumours. Observational research indicates that engaging in walking is linked to better prognosis in patient after surgery. This trial aims to explore whether walking exercise prior to craniotomy in brain tumour patients can reduce the incidence of cardiopulmonary complications and preserve patients' cognitive function.
METHODS AND ANALYSIS
In this randomised controlled trial, 160 participants with supratentorial brain tumours aged 18-65 years, with a preoperative waiting time of more than 3-4 weeks and without conditions that would interfere with the trial such as cognitive impairment, will be randomly assigned in a ratio of 1:1 to either receive traditional treatment or additional combined with a period of 3-4 weeks of walking exercise of 10 000-15 000 steps per day. Wearable pedometer devices will be used to record step counts. The researchers will evaluate participants at enrolment, baseline, 14 days preoperatively, 3 days prior to surgery and 1 week after surgery or discharge (select which occurs first). The primary outcomes include the incidence of postoperative cardiopulmonary complications and changes in cognitive function (gauged by the Montreal Cognitive Assessment test). Secondary outcomes include the average length of hospital stay, postoperative pain, participant contentment, healthcare-associated costs and incidence of other postoperative surgery-related complications. We anticipate that short-term preoperative walking exercises will reduce the incidence of surgery-related complications in the short term after craniotomy, protect patients' cognitive function, aid patients' postoperative recovery and reduce the financial cost of treatment.
ETHICS AND DISSEMINATION
The study protocol has been approved by Ethics Committee of Xiangya Hospital of Central South University (approval number: 202305117). The findings of the research will be shared via publications that have been reviewed by experts in the field and through presentations at conferences.
TRIAL REGISTRATION NUMBER
NCT05930288.
Topics: Humans; Craniotomy; Walking; Adult; Middle Aged; Supratentorial Neoplasms; Female; Male; Aged; Preoperative Exercise; Prognosis; Randomized Controlled Trials as Topic; Young Adult; Postoperative Complications; Adolescent; Cognition
PubMed: 38754891
DOI: 10.1136/bmjopen-2023-080787 -
Cancer Medicine May 2024Over the past decade, immune checkpoint inhibitors (ICIs) have significantly transformed cancer treatment. However, ICIs inevitably may cause a spectrum of...
BACKGROUND
Over the past decade, immune checkpoint inhibitors (ICIs) have significantly transformed cancer treatment. However, ICIs inevitably may cause a spectrum of immune-related adverse events, among which cardiovascular toxicity, particularly myocarditis, while infrequent, has garnered increasing attention due to its high fatality rate.
METHODS
We conducted a multicenter retrospective study to characterize ICI-associated cardiovascular adverse events. Logistic regression was performed to explore the risk factors for the development of myocarditis and severe myocarditis. Receiver operating characteristic curves were conducted to assess the diagnostic abilities of cardiac biomarkers to distinguish different cardiovascular toxicities, and the performance and calibration were evaluated using Hosmer-Lemeshow test.
RESULTS
Forty-four patients were identified, including thirty-five myocarditis, five heart failure, three arrhythmias, and one myocardial infarction. Compared with other patients, myocarditis patients had higher cardiac troponin-I (cTnI) levels (p < 0.001), higher creatine kinase levels (p = 0.003), higher creatine kinase isoenzyme-MB (CK-MB) levels (p = 0.013), and shorter time to the incidence of adverse cardiovascular events (p = 0.022) after ICI treatment. Twenty-one patients (60%) were classified as severe myocarditis, and they presented higher cardiac troponin I (cTnI) levels (p = 0.013), higher N-terminal pro-B-type natriuretic peptide levels (p = 0.031), higher creatine kinase levels (p = 0.018), higher CK-MB levels (p = 0.026), and higher neutrophil to lymphocyte ratio (NLR) levels (p = 0.016) compared to non-severe myocarditis patients after ICI treatment. Multivariate logistic regression showed that CK-MB (adjusted odds ratio [OR]: 1.775, 95% confidence interval [CI]: 1.055-2.984, p = 0.031) was the independent risk factor of the development of ICI-associated myocarditis, and cTnI (adjusted OR: 1.021, 95% CI: 1.002-1.039, p = 0.03) and NLR (adjusted OR: 1.890, 95% CI: 1.026-3.483, p = 0.041) were the independent risk factors of ICI-associated severe myocarditis. The receiver operating characteristic curve showed an area under curve of 0.785 (95% CI: 0.642 to 0.928, p = 0.013) for CK-MB, 0.765 (95% CI: 0.601 to 0.929, p = 0.013) for cTnI, and 0.773 for NLR (95% CI: 0.597 to 0.948, p = 0.016).
CONCLUSIONS
Elevated CK-MB after ICI treatment is the independent risk factor for the incidence of ICI-associated myocarditis, and elevated cTnI and NLR after ICI treatment are the independent risk factors for the development of ICI-associated severe myocarditis. CK-MB, cTnI, and NLR demonstrated a promising predictive utility for the identification of ICI-associated myocarditis and severe myocarditis.
Topics: Humans; Male; Retrospective Studies; Female; Immune Checkpoint Inhibitors; Myocarditis; Middle Aged; Aged; Risk Factors; Biomarkers; Neoplasms; Troponin I; ROC Curve; Cardiovascular Diseases; Creatine Kinase, MB Form; Natriuretic Peptide, Brain; Heart Failure
PubMed: 38752474
DOI: 10.1002/cam4.7233 -
Frontiers in Endocrinology 2024Low socioeconomic status affects not only diagnosis rates and therapy of patients with diabetes mellitus but also their health behavior. Our primary goal was to examine...
Diagnosis rates, therapeutic characteristics, lifestyle, and cancer screening habits of patients with diabetes mellitus in a highly deprived region in Hungary: a cross-sectional analysis.
INTRODUCTION
Low socioeconomic status affects not only diagnosis rates and therapy of patients with diabetes mellitus but also their health behavior. Our primary goal was to examine diagnosis rates and therapy of individuals with diabetes living in Ormánság, one of the most deprived areas in Hungary and Europe. Our secondary goal was to examine the differences in lifestyle factors and cancer screening participation of patients with diagnosed and undiagnosed diabetes compared to healthy participants.
METHODS
Our study is a cross-sectional analysis using data from the "Ormánság Health Program". The "Ormánság Health Program" was launched to improve the health of individuals in a deprived region of Hungary. Participants in the program were coded as diagnosed diabetes based on diagnosis by a physician as a part of the program, self-reported diabetes status, and self-reported prescription of antidiabetic medication. Undiagnosed diabetes was defined as elevated blood glucose levels without self-reported diabetes and antidiabetic prescription. Diagnosis and therapeutic characteristics were presented descriptively. To examine lifestyle factors and screening participation, patients with diagnosed and undiagnosed diabetes were compared to healthy participants using linear regression or multinomial logistic regression models adjusted for sex and age.
RESULTS
Our study population consisted of 246 individuals, and 17.9% had either diagnosed (n=33) or undiagnosed (n=11) diabetes. Metformin was prescribed in 75.8% (n=25) of diagnosed cases and sodium-glucose cotransporter-2 inhibitors (SGLT-2) in 12.1% (n=4) of diagnosed patients. After adjustment, participants with diagnosed diabetes had more comorbidities (adjusted [aOR]: 3.50, 95% confidence interval [95% CI]: 1.34-9.18, p<0.05), consumed vegetables more often (aOR: 2.49, 95% CI: 1.07-5.78, p<0.05), but desserts less often (aOR: 0.33, 95% CI: 0.15-0.75, p<0.01) than healthy individuals. Patients with undiagnosed diabetes were not different in this regard from healthy participants. No significant differences were observed for cancer screening participation between groups.
CONCLUSIONS
To increase recognition of diabetes, targeted screening tests should be implemented in deprived regions, even among individuals without any comorbidities. Our study also indicates that diagnosis of diabetes is not only important for the timely initiation of therapy, but it can also motivate individuals in deprived areas to lead a healthier lifestyle.
Topics: Humans; Cross-Sectional Studies; Hungary; Female; Male; Middle Aged; Life Style; Early Detection of Cancer; Adult; Aged; Diabetes Mellitus; Neoplasms; Diabetes Mellitus, Type 2; Hypoglycemic Agents
PubMed: 38752177
DOI: 10.3389/fendo.2024.1299148 -
Transplant International : Official... 2024The main limitation to increased rates of lung transplantation (LT) continues to be the availability of suitable donors. At present, the largest source of lung... (Review)
Review
The main limitation to increased rates of lung transplantation (LT) continues to be the availability of suitable donors. At present, the largest source of lung allografts is still donation after the neurologic determination of death (brain-death donors, DBD). However, only 20% of these donors provide acceptable lung allografts for transplantation. One of the proposed strategies to increase the lung donor pool is the use of donors after circulatory-determination-of-death (DCD), which has the potential to significantly alleviate the shortage of transplantable lungs. According to the Maastricht classification, there are five types of DCD donors. The first two categories are uncontrolled DCD donors (uDCD); the other three are controlled DCD donors (cDCD). Clinical experience with uncontrolled DCD donors is scarce and remains limited to small case series. Controlled DCD donation, meanwhile, is the most accepted type of DCD donation for lungs. Although the DCD donor pool has significantly increased, it is still underutilized worldwide. To achieve a high retrieval rate, experience with DCD donation, adequate management of the potential DCD donor at the intensive care unit (ICU), and expertise in combined organ procurement are critical. This review presents a concise update of lung donation after circulatory-determination-of-death and includes a step-by-step protocol of lung procurement using abdominal normothermic regional perfusion.
Topics: Humans; Lung Transplantation; Perfusion; Tissue and Organ Procurement; Tissue Donors; Brain Death; Organ Preservation; Death
PubMed: 38751771
DOI: 10.3389/ti.2024.12659 -
BMC Medical Imaging May 2024Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is...
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.
Topics: Humans; Brain Neoplasms; Magnetic Resonance Imaging; Deep Learning; Neural Networks, Computer; Machine Learning; Image Interpretation, Computer-Assisted
PubMed: 38750436
DOI: 10.1186/s12880-024-01261-0 -
Scientific Reports May 2024We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic...
We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.
Topics: Humans; Deep Learning; Brain Neoplasms; Magnetic Resonance Imaging; Radiosurgery; Female; Male; Middle Aged; Aged; Treatment Outcome; Neural Networks, Computer; Longitudinal Studies; Adult; Aged, 80 and over; Radiomics
PubMed: 38750084
DOI: 10.1038/s41598-024-60781-5