Cerebral Vasospasm as a “Hidden Danger” after Craniotomy: A Systematic Review of Case Reports and Case Series | Chapter 3 | Medical Science: Updates and Prospects Vol. 5

 

Background: Cerebral vasospasm following tumour craniotomy (CVACT) is a rarely reported yet potentially devastating postoperative complication with significant neurological consequences. Unlike cerebral vasospasm associated with aneurysmal subarachnoid haemorrhage, which has been extensively studied, CVACT remains poorly understood with respect to its pathophysiology, early detection, and management. This lack of clarity often leads to delayed diagnosis and suboptimal therapeutic strategies. In this chapter, we present a comprehensive synthesis of the current literature, drawing upon published case reports and case series to identify emerging clinical patterns, key risk factors, available diagnostic approaches, and therapeutic responses associated with CVACT. The objective is to consolidate and interpret existing evidence regarding predisposing factors, potential underlying mechanisms, clinical presentation, diagnostic modalities, treatment strategies, and patient outcomes, thereby improving clinical awareness and informing future research directions.

 

Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines, utilising the PubMed and ScienceDirect databases. This review employed a Boolean combination of the MeSH terms and keywords "cerebral vasospasm," "craniotomy," and "brain tumour" to identify instances of cerebral vasospasm following tumour craniotomy (CVACT). The study protocol, which outlines the methodologies used in the systematic review, has been registered with the PROSPERO database. The inclusion criteria for this review consisted of case reports and case series published in English and available in full text, with no restrictions on publication year. Furthermore, gray literature was not excluded from the review. The final search was completed in May 2024.

 

Results: We included 60 inclusion patients from 14 case reports and 13 case series, with 33 (55%) females and 27 (45%) males, with a mean age of 44.05 ± 16.8 years. The most common tumours were pituitary adenomas, which were found in 22 (36.66%). The most common tumour location was the middle cranial fossa (75%), and the most common surgery technique used was transsphenoidal surgery (50%). Most of those who experience vasospasm have a craniotomy with the TSS technique (50%), with complications of intraoperative bleeding. The range of onset of VS symptoms postoperatively was 0–30 days (mean 6.59d). The symptoms included asymptomatic, headache, loss of vision, hemiparesis, diplopia, etc. The vascular involvement was mainly anterior circulation (78.33%). The diagnostic tools most commonly used were angiography and transcranial doppler (TCD). The most common management of VS from the included studies was pharmacology. The survival rate was 61.66%. We found that the tumour location and vascular-affected vasospasm were significantly correlated with mortality rates: p = 0.015 and p = 0.02.

 

Conclusions: Cerebral vasospasm after craniotomy tumour removal (CVACT) frequently arises in tumours situated in the medial cranial fossa, predominantly pituitary adenomas and meningiomas. The minimally invasive surgical approach of TSS may contribute to the mechanism of CVACT incidence. The existence of preoperative vascular pathology, such as encasement or narrowing, appears to be a predictor alongside the incidence of intra- or postoperative haemorrhage. The vascular structures most susceptible to vasospasm are located in the anterior circulation of the Willis circle, which appears to correlate with the vascular problems that typically undergo preoperative encasement of the internal carotid artery (ICA). The most reliable and real-time diagnostic instrument employed is TCD, while imaging continues to be the gold standard. Nimodipine treatment continues to be a viable therapeutic option that can enhance patient outcomes.

 

 

Author(s) Details

Khairunnisai Tarimah
Department of Anesthesiology and Intensive Therapy Subdivision Neuroanesthesia and Critical Care,  Dr Hasan Sadikin Hospital, Padjadjaran University, Bandung 40161, West Java, Indonesia and Department of Anesthesiology and Intensive Therapy, RSUD Kota Mataram, Al-Azhar Islamic University Mataram, Mataram 83127, West Nusa Tenggara, Indonesia.

 

Dewi Yulianti Bisri
Department of Anesthesiology and Intensive Therapy Subdivision Neuroanesthesia and Critical Care, Dr Hasan Sadikin Hospital, Padjadjaran University, Bandung 40161, West Java, Indonesia.

 

Radian Ahmad Halimi
Department of Anesthesiology and Intensive Therapy Subdivision Neuroanesthesia and Critical Care, Dr Hasan Sadikin Hospital, Padjadjaran University, Bandung 40161, West Java, Indonesia.

 

Elvan Wiyarta
Intensive Care Department, University of Indonesia Hospital, Depok 16424, West Jawa, Indonesia and Service Department, Risetku, South Jakarta 12820, Jakarta, Indonesia.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v5/6893

Novel Automatic Segmentation Approach for Early Brain Tumour Detection: Comparative Evaluation with AI Approaches | Chapter 5 | Medical Science: Updates and Prospects Vol. 1

 

Background: Automatic object detection in medical images is a crucial step in the diagnostic process. The problem of detecting brain tumours at an early stage is well advanced with deep learning algorithms (DLA) such as convolutional neural networks (CNN). The issue lies in the fact that these algorithms necessitate a training phase involving a large database of several hundred images, which can be time-consuming and require complex computational infrastructure.

 

Objective: This study aimed to comprehensively evaluate a proposed method, which relies on an active contour algorithm, for identifying and distinguishing brain tumours in magnetic resonance images.

 

Methods: The proposed algorithm was tested using brain images from the BRATS Challenges 2021, specifically focusing on glioma tumours. The proposed segmentation method is made up of an active contour algorithm, an anisotropic diffusion filter for pre-processing, active contour segmentation (Chan-Vese), and morphological operations for segmentation refinement.

 

Results: Its performance was evaluated using various metrics, such as accuracy, precision, sensitivity, specificity, Jaccard index, Dice index, and Hausdorff distance. The proposed method exhibited higher performance measures than most classical image segmentation methods and was comparable to the deep learning methods. These results indicate its ability to detect brain tumours accurately and rapidly.

 

Conclusion: The results section provided both numerical and visual insights into the similarity between segmented and ground truth tumour areas. The findings of this study highlighted the potential of computer-based methods in improving brain tumour identification using magnetic resonance imaging. Future work must validate the efficacy of these segmentation approaches across different brain tumour categories and improve computing efficiency to integrate the technology into potential clinical processes.

 

 

Author(s) Details

Mohammed Almijalli
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Faten A. Almusayib
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ghala F. Albugami
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ziyad Aloqalaa
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Omar Altwijri
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

Ali S. Saad
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v1/6551