Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma
Glioma represents a dominant primary intracranial malignancy in the central nervous
system. Artificial intelligence that mainly includes machine learning, and deep learning …
system. Artificial intelligence that mainly includes machine learning, and deep learning …
Recapitulating the key advances in the diagnosis and prognosis of high-grade gliomas: second half of 2021 update
G Frosina - International Journal of Molecular Sciences, 2023 - mdpi.com
High-grade gliomas (World Health Organization grades III and IV) are the most frequent and
fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively …
fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively …
Vision transformer based classification of gliomas from histopathological images
E Goceri - Expert Systems with Applications, 2024 - Elsevier
Early and accurate detection and classification of glioma types is of paramount importance
in determining treatment planning and increasing the survival rate of patients. At present …
in determining treatment planning and increasing the survival rate of patients. At present …
Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes
Objectives Radiomic features have demonstrated encouraging results for non-invasive
detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data …
detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data …
A fully automated deep-learning model for predicting the molecular subtypes of posterior fossa ependymomas using T2-weighted images
Purpose: We aimed to develop and validate a deep learning (DL) model to automatically
segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group …
segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group …
Artificial intelligence in neuro-oncology
V Nakhate, LN Gonzalez Castro - Frontiers in Neuroscience, 2023 - frontiersin.org
Artificial intelligence (AI) describes the application of computer algorithms to the solution of
problems that have traditionally required human intelligence. Although formal work in AI has …
problems that have traditionally required human intelligence. Although formal work in AI has …
Towards cross-modal causal structure and representation learning
Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an
IDH-mutation cause differences in patients' MRI images? Conventional causal discovery …
IDH-mutation cause differences in patients' MRI images? Conventional causal discovery …
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements
The development, application, and benchmarking of artificial intelligence (AI) tools to
improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid …
improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid …
Automated brain tumor detection using machine learning: A bibliometric review
R Hossain, RB Ibrahim, HB Hashim - World neurosurgery, 2023 - Elsevier
To develop a research overview of brain tumor classification using machine learning, we
conducted a systematic review with a bibliometric analysis. Our systematic review and …
conducted a systematic review with a bibliometric analysis. Our systematic review and …
Combination of MRI-based prediction and CRISPR/Cas12a-based detection for IDH genotyping in glioma
D Yu, Q Zhong, Y Xiao, Z Feng, F Tang, S Feng… - NPJ Precision …, 2024 - nature.com
Early identification of IDH mutation status is of great significance in clinical therapeutic
decision-making in the treatment of glioma. We demonstrate a technological solution to …
decision-making in the treatment of glioma. We demonstrate a technological solution to …