Predicting cancer outcomes with radiomics and artificial intelligence in radiology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …
application of AI-based cancer imaging analysis to address other, more complex, clinical …
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 …
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 …
Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma
Purpose: To (i) create a survival risk score using radiomic features from the tumor habitat on
routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a …
routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a …
Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine
Alzheimer's disease (AD) is a type of neurological disorder and is a most frequent cause of
dementia across the world. The area of medical imaging has created an advancement in …
dementia across the world. The area of medical imaging has created an advancement in …
Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma
Background Imaging of glioblastoma patients after maximal safe resection and
chemoradiation commonly demonstrates new enhancements that raise concerns about …
chemoradiation commonly demonstrates new enhancements that raise concerns about …
Texture analysis in cerebral gliomas: a review of the literature
Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify
macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity …
macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity …
[HTML][HTML] Machine learning and glioma imaging biomarkers
AIM To review how machine learning (ML) is applied to imaging biomarkers in neuro-
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma
AIM To investigate machine learning based models combining clinical, radiomic, and
molecular information to distinguish between early true progression (tPD) and …
molecular information to distinguish between early true progression (tPD) and …
Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges
Neuro-oncology largely consists of malignancies of the brain and central nervous system
including both primary as well as metastatic tumors. Currently, a significant clinical …
including both primary as well as metastatic tumors. Currently, a significant clinical …