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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