AIMC Topic: Glioma
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Deep learning algorithm reveals two prognostic subtypes in patients with gliomas.
BMC bioinformatics
Oct 11, 2022
BACKGROUND: Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-s...
A data augmentation method for fully automatic brain tumor segmentation.
Computers in biology and medicine
Aug 27, 2022
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net archit...
Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Aug 10, 2022
BACKGROUND AND PURPOSE: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue ...
Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
BMJ open
Jul 18, 2022
OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines a...
A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.
Sensors (Basel, Switzerland)
Jul 12, 2022
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction ...
Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.
Scientific reports
Jul 5, 2022
Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary h...
A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.
BMC bioinformatics
Jun 24, 2022
Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a tr...
Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review.
European radiology
Apr 29, 2022
OBJECTIVES: Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim ...
A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.
Scientific reports
Apr 12, 2022
Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the diffi...
Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics.
Scientific reports
Apr 8, 2022
Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a patient cohort diagnosed with high grade gliomas. In this retrospective analysis, we analysed 131 pa...
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