The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization\'s (WHO) classification.

BACKGROUND: Serum albumin reflects nutritional status and is associated with postoperative complications and mortality. Delta albumin (ΔAlb), defined as the difference between preoperative and lowest postoperative levels, could predict complications and mortality, even with post-op levels above 30 g/L prompting albumin infusions. This study aimed to assess how ΔAlb relates to outcomes in craniotomy patients with brain tumors.
METHODS: This retrospective study screened patients diagnosed with a brain tumor who underwent cerebral surgery from a single Chinese hospital between December 2010 and April 2021. Patients were divided into four groups based on their ΔAlb levels: <5 g/L (normal), 5-9.9 g/L (mild ΔAlb), 10-14.9 g/L (moderate ΔAlb), and ≥15 g/L (severe ΔAlb). The primary outcome was postoperative 30-day mortality.
RESULTS: Among the 9660 patients undergoing craniotomy for brain tumors, the median ΔAlb level after craniotomy was 7.3 g/L. ΔAlb was associated with increased postoperative 30-day mortality; Odds ratios (OR) for mild, moderate, and severe ΔAlb were 1.93(95% CI, 1.17-3.18,P=0.01), 2.21(95% CI, 1.28-3.79,P=0.004), and 7.26(95% CI, 4.19-12.58,P<0.01), respectively. Significantly, ΔAlb >5g/L was found to have a strong association with a higher risk of mortality, even when the nadir Alb remained greater than 30 g/L (OR, 1.84; 95% CI, 1.13- 3.00, P=0.014).
CONCLUSIONS: Among patients undergoing craniotomy for brain tumor resection, a mild degree of ΔAlb was associated with increased 30-day mortality, even if the nadir Alb remained greater than 30 g/L. Moreover, ΔAlb was associated with postoperative complications and longer lengths of stay.

OBJECTIVE: An elevated preoperative red cell distribution width (RDW) is associated with adverse prognostic outcomes in various diseases. However, the correlation between changes in RDW (ΔRDW) and the prognosis following brain tumor craniotomy remains unclear. Accordingly, this study aimed to investigate the prognostic significance of perioperative changes in RDW in patients undergoing brain tumor craniotomy.
METHODS: This retrospective cohort study included patients undergoing craniotomy for brain tumors at West China Hospital, Sichuan University, from January 2011 to March 2021. We defined perioperative changes in RDW: group A (non-significant RDW changes, ΔRDW ≤0.4%), group B (drop in RDW, ΔRDW < -0.4%), and group C (rise in RDW, ΔRDW >0.4%). The relationship between the changes in RDW and all-cause mortality was analyzed by categorizing the patients according to perioperative ΔRDW (RDW at postoperative one week - RDW at admission).
RESULTS: The present study included a total of 9589 patients who underwent craniotomy for the treatment of brain tumors. A rise in RDW was significantly associated with increased mortality, with an adjusted OR of 3.56 (95% CI: 2.56-4.95) for 30-day mortality and 1.57 (95% CI: 1.33-1.85) for one-year mortality compared to those with non-significant RDW changes (ΔRDW ≤0.4%). Conversely, a decrease in RDW showed no significant association with 30-day mortality (adjusted OR: 1.04, 95% CI: 0.53-2.04) and one-year mortality (adjusted OR: 1.18, 95% CI: 0.92-1.53). These findings were also supported by restricted cubic spline, which shows that increases in RDW were significantly associated with lower survival rates compared to stable RDW levels during the follow-up period.
CONCLUSIONS: Among patients undergoing craniotomy for a brain tumor, a rise in RDW was associated with 30-day mortality and higher long-term mortality risks, even if patients\' admissions for RDW values were within the normal range. It was worth noting that maintaining stable RDW levels during this period was associated with better survival.

UNASSIGNED: The evaluation of brain tumor recurrence after surgery is based on the comparison between tumor regions on pre-operative and follow-up magnetic resonance imaging (MRI) scans in clinical practice. Accurate alignment of MRI scans is important in this evaluation process. However, existing methods often fail to yield accurate alignment due to substantial appearance and shape changes of tumor regions. The study aimed to improve this misalignment situation through multimodal information and compensation for shape changes.
UNASSIGNED: In this work, a deep learning-based deformation registration method using bilateral pyramid to create multi-scale image features was developed. Moreover, morphology operations were employed to build correspondence between the surgical resection on the follow-up and pre-operative MRI scans.
UNASSIGNED: Compared with baseline methods, the proposed method achieved the lowest mean absolute error of 1.82 mm on the public BraTS-Reg 2022 dataset.
UNASSIGNED: The results suggest that the proposed method is potentially useful for evaluating tumor recurrence after surgery. We effectively verified its ability to extract and integrate the information of the second modality, and also revealed the micro representation of tumor recurrence. This study can assist doctors in registering multiple sequence images of patients, observing lesions and surrounding areas, analyzing and processing them, and guiding doctors in their treatment plans.

The pursuit of effective treatments for brain tumors has increasingly focused on the promising area of nanoparticle-enhanced radiotherapy (NERT). This review elucidates the context and significance of NERT, with a particular emphasis on its application in brain tumor therapy-a field where traditional treatments often encounter obstacles due to the blood-brain barrier (BBB) and tumor cells\' inherent resistance. The aims of this review include synthesizing recent advancements, analyzing action mechanisms, and assessing the clinical potential and challenges associated with nanoparticle (NP) use in radiotherapy enhancement. Preliminary preclinical studies have established a foundation for NERT, demonstrating that nanoparticles (NPs) can serve as radiosensitizers, thereby intensifying radiotherapy\'s efficacy. Investigations into various NP types, such as metallic, magnetic, and polymeric, have each unveiled distinct interactions with ionizing radiation, leading to an augmented destruction of tumor cells. These interactions, encompassing physical dose enhancement and biological and chemical radio sensitization, are crucial to the NERT strategy. Although clinical studies are in their early phases, initial trials have shown promising results in terms of tumor response rates and survival, albeit with mindful consideration of toxicity profiles. This review examines pivotal studies affirming NERT\'s efficacy and safety. NPs have the potential to revolutionize radiotherapy by overcoming challenges in targeted delivery, reducing off-target effects, and harmonizing with other modalities. Future directions include refining NP formulations, personalizing therapies, and navigating regulatory pathways. NERT holds promise to transform brain tumor treatment and provide hope for patients.

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Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

UNASSIGNED: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer\'s disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.
UNASSIGNED: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.
UNASSIGNED: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.
UNASSIGNED: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

To explore the techniques, safety, and feasibility of minimally invasive neurosurgery through the supraorbital eyebrow arch keyhole approach by neuroendoscopy. Retrospective analysis of clinical data of patients with various cranial diseases treated by transcranial neuroendoscopic supraorbital eyebrow keyhole approach in our hospital from March 2021 to October 2023. A total of 39 complete cases were collected, including 21 cases of intracranial aneurysms, 9 cases of intracranial space occupying lesions, 5 cases of brain trauma, 3 cases of cerebrospinal fluid rhinorrhea, and 1 case of cerebral hemorrhage. All patients\' surgeries were successful. The good prognosis rate of intracranial aneurysms was 17/21 (81%), and the symptom improvement rate of intracranial space occupying lesions was 8/9 (88.9%). Among them, the initial symptoms of one patient with no improvement were not related to space occupying, while the total effective rate of the other three types of patients was 9/9 (100%). The average length of the craniotomy bone window of the supraorbital eyebrow arch keyhole is 3.77 ± 0.31 cm, and the average width is 2.53 ± 0.23 cm. The average postoperative hospital stay was 14.77 ± 6.59 days. The average clearance rate of hematoma by neuroendoscopy is 95.00% ± 1.51%. Our results indicate that endoscopic surgery through the supraorbital eyebrow arch keyhole approach is safe and effective for the treatment of anterior skull base lesions and cerebral hemorrhage. However, this retrospective study is a single center, small sample study, and the good surgical results do not exclude the subjective screening of suitable patients by clinical surgeons, which may have some bias. Although the clinical characteristics such as indications and contraindications of this surgical method still require further prospective and multicenter clinical research validation, our study still provides a new approach and choice for minimally invasive surgical treatment of anterior skull base lesions.

OBJECTIVE: The aim of this study was to evaluate the occurrence and factors predisposing to delirium following brain tumor resection.
METHODS: Data from patients who underwent brain tumor resection surgery from 2016 to 2019 were extracted from the National Inpatient Sample database and retrospectively analyzed. The difference between the 2 groups was compared by Wilcoxon rank test or χ2 test was used. Univariate and multivariate logistic regression analyses were used to identify the risk factors of delirium after brain tumor resection.
RESULTS: From 2016 to 2019, 28,340 patients who underwent brain tumor resection were identified in the National Inpatient Sample database, with the incidence of delirium being 4.79% (1357/28,340). It was found that increased incidence of delirium was significantly associated with age over 75 years and males (all P < 0.001). Besides, patients with delirium were more likely to have multiple comorbidities and to receive elective surgery (all P < 0.001). The results of logistic regression analysis showed that self-pay (odds ratio [OR] = 0.51; confidence interval [CI] = 0.31-0.83; P = 0.007), elective admission (OR = 0.53; CI = 0.47-0.60; P < 0.001), obesity (OR = 0.77; CI = 0.66-0.92; P = 0.003), females (OR = 0.79; CI = 0.71-0.88; P < 0.001), and private insurance (OR = 0.80; CI = 0.67-0.95; P = 0.012) were associated with lower occurrence of delirium. Besides, delirium was related to extra total hospital charges (P < 0.001), increased length of stay (P < 0.001), higher inpatient mortality (P = 0.001), and perioperative complications (including heart failure, acute renal failure, urinary tract infection, urinary retention, septicemia, pneumonia, blood transfusion, and cerebral edema) (P < 0.001).
CONCLUSIONS: Many factors were associated with the occurrence of delirium after brain tumor resection. Therefore, clinicians should identify high-risk patients prone to delirium in a timely manner and take effective management measures to reduce adverse outcomes.