Ms. Yuxia Wu Profile

Yuxia Wu

at PLA Air Force Military Medical Univ.

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Publications (2)

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Proceedings Article | 3 March 2017 Presentation + Paper

IDH mutation assessment of glioma using texture features of multimodal MR images

Xi Zhang, Qiang Tian, Yu-Xia Wu, Xiao-Pan Xu, Bao-Juan Li, Yi-Xiong Liu, Yang Liu, Hong-Bing Lu

Proceedings Volume 10134, 101341S (2017) https://doi.org/10.1117/12.2254212

KEYWORDS: MATLAB, Statistical analysis, Cancer, Tumors, Magnetic resonance imaging, Diagnostics, Feature extraction, Machine learning, Image classification, Feature selection, 3D image processing

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Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Proceedings Article | 24 February 2017 Paper

Multi-contrast MRI registration of carotid arteries based on cross-sectional images and lumen boundaries

Yu-Xia Wu, Xi Zhang, Xiao-Pan Xu, Yang Liu, Guo-Peng Zhang, Bao-Juan Li, Hui-Jun Chen, Hong-Bing Lu

Proceedings Volume 10133, 1013323 (2017) https://doi.org/10.1117/12.2254191

KEYWORDS: Magnetic resonance imaging, Image registration, Lutetium, Biomedical engineering

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Ischemic stroke has great correlation with carotid atherosclerosis and is mostly caused by vulnerable plaques. It’s particularly important to analysis the components of plaques for the detection of vulnerable plaques. Recently plaque analysis based on multi-contrast magnetic resonance imaging has attracted great attention. Though multi-contrast MR imaging has potentials in enhanced demonstration of carotid wall, its performance is hampered by the misalignment of different imaging sequences. In this study, a coarse-to-fine registration strategy based on cross-sectional images and wall boundaries is proposed to solve the problem. It includes two steps: a rigid step using the iterative closest points to register the centerlines of carotid artery extracted from multi-contrast MR images, and a non-rigid step using the thin plate spline to register the lumen boundaries of carotid artery. In the rigid step, the centerline was extracted by tracking the crosssectional images along the vessel direction calculated by Hessian matrix. In the non-rigid step, a shape context descriptor is introduced to find corresponding points of two similar boundaries. In addition, the deterministic annealing technique is used to find a globally optimized solution. The proposed strategy was evaluated by newly developed three-dimensional, fast and high resolution multi-contrast black blood MR imaging. Quantitative validation indicated that after registration, the overlap of two boundaries from different sequences is 95%, and their mean surface distance is 0.12 mm. In conclusion, the proposed algorithm has improved the accuracy of registration effectively for further component analysis of carotid plaques.

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