AIMC Topic: Central Nervous System Neoplasms
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How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review.
Clinical radiology
Mar 19, 2024
BACKGROUND: Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We aimed to perform this meta-analysis to evaluate the diagnostic accuracy ...
Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.
Journal of magnetic resonance imaging : JMRI
Nov 29, 2023
BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classification, integrating molecular markers. However, the impact of this change on radiomics-based machine learning (ML) classifiers remains unexplored.
Ultra-fast deep-learned CNS tumour classification during surgery.
Nature
Oct 11, 2023
Central nervous system tumours represent one of the most lethal cancer types, particularly among children. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of r...
Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.
International journal of computer assisted radiology and surgery
Apr 21, 2023
PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. T...
Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.
International journal of molecular sciences
Dec 10, 2021
Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at...
Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.
Radiation oncology (London, England)
Jun 8, 2021
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...
Radiotherapy, Intensity-Modulated
Central Nervous System Neoplasms
Head and Neck Neoplasms
Tomography, X-Ray Computed
Prostatic Neoplasms
Humans
Male
Workflow
Algorithms
Deep Learning
Health Plan Implementation
Prognosis
Image Processing, Computer-Assisted
Radiotherapy Dosage
Organs at Risk
Radiotherapy Planning, Computer-Assisted
A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.
Biomolecules
Apr 12, 2021
Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces a patient's quality of life and results in high mortality rates. A low incidence also means a low number of cases, which in turn means a low amount o...
Automated histologic diagnosis of CNS tumors with machine learning.
CNS oncology
Jun 30, 2020
The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intraoperative decision-making depends significantly on the histologic diagnosis, which is often establish...
Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
May 16, 2018
PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classificatio...
Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.
AJNR. American journal of neuroradiology
Apr 27, 2017
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...
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