Search results for "Brain metastasis"
showing 3 items of 13 documents
Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI
2015
Purpose To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis. Methods Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance. Results The highest classification accuracy evaluated over test sets was achieved with a subset of ten features…
Comment on “Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibili…
2017
We have read with great interest the article published by Tiwari et al, “Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.”[1][1] In their article, they refer to our work regarding brain metastasis
Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI
2009
Brain metastases and glioblastoma multiforme are the most aggressive and common brain tumours in adults and they require a different clinical management. Anatomical magnetic resonance imaging (MRI) or clinical history, cannot always clearly distinguish between them. This study describes and verifies the use of magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI) in combination with MRI for differential diagnosis of glioblastomas and metastases. Feature selection methods are applied to the magnetic resonance (MR) spectra of 121 patients and relevant features are detected. Different classification methods are used to distinguish glioblastoma multiforme and…