Cancer of the Central Nervous System

Dorsey, Jay F.; Salinas, Ryan; Dang, Mai T.; Alonso‐Basanta, Michelle; Judy, Kevin; Maity, Amit; Lustig, Robert A.; Lee, John Y.K.; Phillips, Peter C.B.; Pruitt, Amy A.

doi:10.1016/b978-0-323-47674-4.00063-3

Cited by 13 publications

(5 citation statements)

References 661 publications

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“…48 Emblem KE et al (2015) used a Support Vector Machine (SVM) and a whole-tumor Cerebral Blood Volume (CBR) for this purpose, while Nematollahi M et al ( 2018) proposed the C5.0 decision tree model, with tumor width and Karnofsky performance status scores being the most critical parameters for prediction of survival. 13,37 Reliable methods to predict the survival rates post-surgery help understand the early postoperative complications (EPC) and the long-term prognosis in such patients. A prospective study conducted by Van Niftrik et al (2019) included data of 688 patients undergoing intracranial tumor surgery at a tertiary hospital between 2015 through 2017.…”

Section: Survival Predictionmentioning

confidence: 99%

“…• medulloblastoma and lymphoma are known to respond well to chemotherapy 16,41 • Various systemic side effects 16,41 Targeted medical therapy • Used for its neuroprotective purpose 16,41 • For hormone responsive cancers 16,41 • Prolong use might be required • Prediction of gross tumor resection 4,5,8,10 • Anatomical delineation 13,15,19 • Training of residents and early career surgeons 32,,37,38,45,53,54 • Delineation of tumor margin resection 9 • Histopathological diagnosis 13 • Prediction of brain shifts 11 • Prediction of postoperative survival 6,25 • Differentiation of tumor recurrence versus treatment effects…”

Section: Chemotherapymentioning

confidence: 99%

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Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Agadi¹,

Dominari²,

Tebha³

et al. 2023

J Korean Neurosurg Soc

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Central nervous system (CNS) tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive.Artificial intelligence, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

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Section: Survival Predictionmentioning

confidence: 99%

Section: Chemotherapymentioning

confidence: 99%

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Agadi¹,

Dominari²,

Tebha³

et al. 2023

J Korean Neurosurg Soc

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show abstract

“…While neuroglial tumors, originating from astrocytes, oligodendrocytes, or ependymal cells, represent more than 80% of primary brain tumors, meningiomas developed from meningothelial cells comprise about 20% [ 142 ]. The source of primary brain tumors still remains unknown; however, high-dose ionizing radiation andsome inherited disorders including neurofibromatosis, Von Hippel–Lindau syndrome, Li-Fraumeni syndrome, and Turcot syndrome may increase the risk of brain tumors [ 141 , 143 ].…”

Section: Primary Brain Tumorsmentioning

confidence: 99%

Gangliosides as Biomarkers of Human Brain Diseases: Trends in Discovery and Characterization by High-Performance Mass Spectrometry

Sârbu

Ica

Zamfir

2022

IJMS

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Gangliosides are effective biochemical markers of brain pathologies, being also in the focus of research as potential therapeutic targets. Accurate brain ganglioside mapping is an essential requirement for correlating the specificity of their composition with a certain pathological state and establishing a well-defined set of biomarkers. Among all bioanalytical methods conceived for this purpose, mass spectrometry (MS) has developed into one of the most valuable, due to the wealth and consistency of structural information provided. In this context, the present article reviews the achievements of MS in discovery and structural analysis of gangliosides associated with severe brain pathologies. The first part is dedicated to the contributions of MS in the assessment of ganglioside composition and role in the specific neurodegenerative disorders: Alzheimer’s and Parkinson’s diseases. A large subsequent section is devoted to cephalic disorders (CD), with an emphasis on the MS of gangliosides in anencephaly, the most common and severe disease in the CD spectrum. The last part is focused on the major accomplishments of MS-based methods in the discovery of ganglioside species, which are associated with primary and secondary brain tumors and may either facilitate an early diagnosis or represent target molecules for immunotherapy oriented against brain cancers.

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“…However, other factors cannot be controlled such as age, family history, and genetics. In addition, some diagnosis methods that are based on ionizing radiation, such as prenatal diagnostic X-ray exposure, increase the risk of developing childhood brain tumors [14,15]. According to the American Cancer Society (ACS), the GBM patient's age is associated with the survival rate, which is better for young people than for old people [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

Chato

Latifi

2021

JPM

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Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.

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Cancer of the Central Nervous System

Cited by 13 publications

References 661 publications

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Gangliosides as Biomarkers of Human Brain Diseases: Trends in Discovery and Characterization by High-Performance Mass Spectrometry

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

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