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Abstract #1913

Combined Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) and Pyradiomics features to predict the prognosis of gliomas.

Xiaoxue Liu1, Baoming Li2, Jianrui Li1, Li Yu2, Jun Xu2, Guangming Lu1, and Zhiqiang Zhang1
1Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China, 2Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing, China

We tried to establish a model based on features extracted from Pyradiomic framework and Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) in MRI to predict the prognosis of gliomas. Fifty-five pathologically confirmed glioma patients were retrospectively collected. All patients underwent 3DTI enhancement, standardized treatment and follow-up for overall survival. 1781 Pyradiomic features and 260 CoLlAGe features were collected. Three Cox proportional hazards models were fitted with Pyradiomics features, CoLlAGe features and Pyradiomics+CoLlAGe features. The C-index in these models were 0.835, 0.820 and 0.844, respectively. The model with Pyradiomics+CoLlAGe features demonstrated improved survival predictive performance than the single model.

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