AIMC Topic: Brain Neoplasms

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An ensemble learning approach for brain cancer detection exploiting radiomic features.

Computer methods and programs in biomedicine Oct 22, 2019
BACKGROUND AND OBJECTIVE: The brain cancer is one of the most aggressive tumour: the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection of brain tumours can be fundamental to increase survival rates. The brain ...
Magnetic Resonance Imaging Early Detection of Cancer Machine Learning Survival Rate Brain Neoplasms Image Processing, Computer-Assisted Humans
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Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia Oct 21, 2019
The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. ...
Humans Female Middle Aged Deep Learning Pilot Projects Glioma Aged Adult Brain Neoplasms Male Magnetic Resonance Imaging Neoplasm Recurrence, Local Image Interpretation, Computer-Assisted
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An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Medical hypotheses Oct 15, 2019
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MR...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Early Detection of Cancer Glioblastoma Algorithms Expert Systems Humans Brain Neoplasms Neuroimaging Software Design Diagnosis, Computer-Assisted Deep Learning
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Diagnosing brain tumours by routine blood tests using machine learning.

Scientific reports Oct 9, 2019
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for t...
Aged Aged, 80 and over Young Adult Adult Machine Learning Case-Control Studies Algorithms Humans Diagnosis, Computer-Assisted Retrospective Studies Adolescent Female Hematologic Tests Sensitivity and Specificity Brain Neoplasms Male Middle Aged
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A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.

Medical hypotheses Sep 30, 2019
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tu...
Image Processing, Computer-Assisted Support Vector Machine Magnetic Resonance Imaging Fuzzy Logic Predictive Value of Tests Entropy Neural Networks, Computer Algorithms Humans Brain Neoplasms Deep Learning
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Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.

Clinical cancer research : an official journal of the American Association for Cancer Research Sep 23, 2019
PURPOSE: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q ...
Magnetic Resonance Imaging Mutation Isocitrate Dehydrogenase Chromosome Deletion Cytogenetic Analysis Chromosomes, Human, Pair 1 Chromosomes, Human, Pair 19 Male Middle Aged ROC Curve Glioma Algorithms Female Brain Neoplasms Humans Machine Learning
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Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Scientific reports Aug 29, 2019
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from th...
Image Interpretation, Computer-Assisted Brain Neoplasms Glioma Magnetic Resonance Imaging Neoplasm Staging Humans Neural Networks, Computer Machine Learning Algorithms
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Personalized oncology with artificial intelligence: The case of temozolomide.

Artificial intelligence in medicine Aug 12, 2019
PURPOSE: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.
Models, Biological Computer Simulation Humans Tumor Burden Brain Neoplasms Artificial Intelligence Maximum Tolerated Dose Dose-Response Relationship, Drug Algorithms Temozolomide Bayes Theorem
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MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks.

Magnetic resonance in medicine Aug 2, 2019
PURPOSE: To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact.
Brain Neoplasms Image Processing, Computer-Assisted Signal Processing, Computer-Assisted Diffusion Artifacts Neuroimaging Signal-To-Noise Ratio Connectome Neural Networks, Computer Machine Learning Algorithms Brain Humans Phantoms, Imaging Knee Diffusion Magnetic Resonance Imaging
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Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation.

JAMA network open Aug 2, 2019
IMPORTANCE: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that...
Neurosurgical Procedures Humans Internship and Residency Artificial Intelligence Simulation Training Machine Learning Male Clinical Competence Algorithms Virtual Reality Female Canada Adult Brain Neoplasms
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