AIMC Topic: Brain Neoplasms

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A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Neuro-oncology
BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a high...

Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Neuro-oncology
BACKGROUND: Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics fea...

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.

Neurosurgery
BACKGROUND: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images.

Current medical imaging
BACKGROUND: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review ...

SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to...

A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance Images: A Preliminary Machine Learning Study.

Turkish neurosurgery
AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans.

Machine learning approaches to study glioblastoma: A review of the last decade of applications.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urge...

Applications of artificial intelligence in neuro-oncology.

Current opinion in neurology
PURPOSE OF REVIEW: To discuss recent applications of artificial intelligence within the field of neuro-oncology and highlight emerging challenges in integrating artificial intelligence within clinical practice.

Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

Neuro-oncology
BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hype...

Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis.

Medicine
Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 t...