The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas
| Autor: | Naci Kocer, Cihan Isler, Yeseren Deniz Senli, Omer Bagcilar, Civan Islak, Osman Kizilkilic, Deniz Alis, Mert Yergin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Contrast Media Feature selection Fluid-attenuated inversion recovery 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Histogram Image Interpretation Computer-Assisted Medicine Humans Radiology Nuclear Medicine and imaging Aged Reproducibility Receiver operating characteristic medicine.diagnostic_test Artificial neural network business.industry Brain Neoplasms Reproducibility of Results Pattern recognition Magnetic resonance imaging General Medicine Glioma Middle Aged Magnetic Resonance Imaging 030220 oncology & carcinogenesis Test set Female Artificial intelligence Neural Networks Computer Neoplasm Grading business |
| Zdroj: | Clinical radiology. 75(5) |
| ISSN: | 1365-229X |
| Popis: | AIM To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG). MATERIALS AND METHODS A total of 181 patients, 97 with HGG (53.5%) and 84 with LGG (46.5%) with brain MRI having T2-weighted (W) fluid attenuation inversion recovery (FLAIR), and contrast-enhanced T1W images were enrolled in the present study. Histogram parameters and high-order texture features were extracted using manually placed regions of interest (ROIs) on T2W-FLAIR and contrast-enhanced T1W images covering the whole volume of the tumours. The reproducibility of the features was assessed by interobserver reliability analyses. The cohort was divided into training (n=121) and test partitions (n=60). The training set was used for attribute selection and model development, and the test set was used to evaluate the diagnostic performance of the pre-trained ANNs in discriminating HGG and LGG. RESULTS In the test cohort, the ANN models using texture data of T2W-FLAIR and contrast-enhanced T1W images achieved an area under the receiver operating characteristic curve (AUC) of 0.87 and 0.86, respectively. The combined ANN model with selected texture features achieved the highest diagnostic accuracy equating 88.3% with an AUC of 0.92. CONCLUSIONS Quantitative texture analysis of T2W-FLAIR and contrast-enhanced T1W enhanced by ANN can accurately discriminate HGG from LGG and might be of clinical value in tailoring the management strategies in patients with gliomas. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect