Radiomics and radiogenomics in gliomas: a contemporary update
The natural history and treatment landscape of primary brain tumours are complicated by the
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …
Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches
Radiomics describes a broad set of computational methods that extract quantitative features
from radiographic images. The resulting features can be used to inform imaging diagnosis …
from radiographic images. The resulting features can be used to inform imaging diagnosis …
Pseudoprogression of brain tumors
SC Thust, MJ van den Bent… - Journal of Magnetic …, 2018 - Wiley Online Library
This review describes the definition, incidence, clinical implications, and magnetic
resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but …
resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but …
Emerging applications of artificial intelligence in neuro-oncology
Due to the exponential growth of computational algorithms, artificial intelligence (AI)
methods are poised to improve the precision of diagnostic and therapeutic methods in …
methods are poised to improve the precision of diagnostic and therapeutic methods in …
MRI biomarkers in neuro-oncology
M Smits - Nature Reviews Neurology, 2021 - nature.com
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in
clinical practice and in clinical trials, to diagnose and monitor disease activity, support …
clinical practice and in clinical trials, to diagnose and monitor disease activity, support …
Incorporating diffusion-and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients
Background Pseudoprogression is a diagnostic challenge in early posttreatment
glioblastoma. We therefore developed and validated a radiomics model using …
glioblastoma. We therefore developed and validated a radiomics model using …
Radiomics in neuro-oncology: Basics, workflow, and applications
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in
patients with brain tumors for routine clinical purposes and the resulting number of imaging …
patients with brain tumors for routine clinical purposes and the resulting number of imaging …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
Background Magnetic resonance imaging (MRI) and amino acid positron-emission
tomography (PET) of the brain contain a vast amount of structural and functional information …
tomography (PET) of the brain contain a vast amount of structural and functional information …
Artificial intelligence in brain tumor imaging: a step toward personalized medicine
M Cè, G Irmici, C Foschini, GM Danesini, LV Falsitta… - Current …, 2023 - mdpi.com
The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-
tailored brain tumor management, achieving optimal onco-functional balance for each …
tailored brain tumor management, achieving optimal onco-functional balance for each …