State of the art: machine learning applications in glioma imaging
OBJECTIVE. Machine learning has recently gained considerable attention because of
promising results for a wide range of radiology applications. Here we review recent work …
promising results for a wide range of radiology applications. Here we review recent work …
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 …
MRI features predict survival and molecular markers in diffuse lower-grade gliomas
Background. Previous studies have shown that MR imaging features can be used to predict
survival and molecular profile of glioblastoma. However, no study of a similar type has been …
survival and molecular profile of glioblastoma. However, no study of a similar type has been …
Radiomic MRI phenotyping of glioblastoma: improving survival prediction
Purpose To investigate whether radiomic features at MRI improve survival prediction in
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …
Radiomic phenotype features predict pathological response in non-small cell lung cancer
TP Coroller, V Agrawal, V Narayan, Y Hou… - Radiotherapy and …, 2016 - Elsevier
Background and purpose Radiomics can quantify tumor phenotype characteristics non-
invasively by applying advanced imaging feature algorithms. In this study we assessed if pre …
invasively by applying advanced imaging feature algorithms. In this study we assessed if pre …
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 …
Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas
Background. High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene
family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate …
family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate …
Radiomics feature robustness as measured using an MRI phantom
Radiomics involves high-throughput extraction of large numbers of quantitative features from
medical images and analysis of these features to predict patients' outcome and support …
medical images and analysis of these features to predict patients' outcome and support …
Classification of the glioma grading using radiomics analysis
Background Grading of gliomas is critical information related to prognosis and survival. We
aimed to apply a radiomics approach using various machine learning classifiers to …
aimed to apply a radiomics approach using various machine learning classifiers to …
Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning
P Sanghani, BT Ang, NKK King, H Ren - Surgical oncology, 2018 - Elsevier
Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor overall
survival (OS) of patients. OS prediction of GBM patients provides useful information for …
survival (OS) of patients. OS prediction of GBM patients provides useful information for …