Loading...
Please wait, while we are loading the content...
Similar Documents
Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging
| Content Provider | Scilit |
|---|---|
| Author | Zhang, Xi Xu, Xiaopan Tian, Qiang Li, Baojuan Wu, Yuxia Yang, Zengyue Liang, Zhengrong Liu, Yang Cui, Guangbin Lu, Hongbing |
| Copyright Year | 2017 |
| Description | Journal: Journal of Magnetic Resonance Imaging (JMRI) To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3.0T magnetic resonance imaging (MRI). A Mann-Whitney U-test was applied to select features with significant differences between low- and high-grade groups (P < 0.05). Then support vector machine with recursive feature elimination (SVM-RFE) and classification strategy was adopted to find an optimal feature subset and then to establish a classification model for grading. A total 102 features were derived from each VOI and among them, 47 candidate features were selected, which showed significant intergroup differences (P < 0.05). By the SVM-RFE method, an optimal feature subset including 22 features was further selected from candidate features. The SVM classifier using the optimal feature subset achieved the best performance in bladder cancer grading, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.861, 82.9%, 78.4%, and 87.1%, respectively. Textural features from DWI and ADC maps can reflect the difference between low- and high-grade bladder cancer, especially those GLCM features from ADC maps. The proposed radiomics strategy using these features, combined with the SVM classifier, may better facilitate image-based bladder cancer grading preoperatively. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1281-1288. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557707/pdf |
| Ending Page | 1288 |
| Page Count | 8 |
| Starting Page | 1281 |
| e-ISSN | 15222586 |
| DOI | 10.1002/jmri.25669 |
| Journal | Journal of Magnetic Resonance Imaging (JMRI) |
| Issue Number | 5 |
| Volume Number | 46 |
| Language | English |
| Publisher | Wiley-Blackwell |
| Publisher Date | 2017-02-15 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Magnetic Resonance Imaging (JMRI) Radiology, Nuclear Medicine and Imaging Apparent Diffusion Coefficient Bladder Cancer Grade Support Vector Machine Texture Feature |
| Content Type | Text |
| Resource Type | Article |
