Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
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 …
Multimodal classification: Current landscape, taxonomy and future directions
Multimodal classification research has been gaining popularity with new datasets in
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
domains such as satellite imagery, biometrics, and medicine. Prior research has shown the …
Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
Gliomas can be classified into five molecular groups based on the status of IDH mutation,
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …
[HTML][HTML] Molecular pathology of tumors of the central nervous system
BW Kristensen, LP Priesterbach-Ackley, JK Petersen… - Annals of oncology, 2019 - Elsevier
Since the update of the 4th edition of the WHO Classification of Central Nervous System
(CNS) Tumors published in 2016, particular molecular characteristics are part of the …
(CNS) Tumors published in 2016, particular molecular characteristics are part of the …
Radiomics in neuro-oncological clinical trials
The development of clinical trials has led to substantial improvements in the prevention and
treatment of many diseases, including brain cancer. Advances in medicine, such as …
treatment of many diseases, including brain cancer. Advances in medicine, such as …
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 …
Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning
Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse
and anaplastic astrocytic and oligodendroglial tumours as well as in secondary …
and anaplastic astrocytic and oligodendroglial tumours as well as in secondary …
FET PET radiomics for differentiating pseudoprogression from early tumor progression in glioma patients post-chemoradiation
Simple Summary Following chemoradiation with alkylating agents in glioma patients,
structural magnetic resonance imaging (MRI) may suggest tumor progression which …
structural magnetic resonance imaging (MRI) may suggest tumor progression which …