Abstract
This paper proposed a radiomics model from magnetic resonance imaging (MRI) for Glioblastoma Multiforme (GBM) patients. One challenge of radiomics study is to reduce the redundancy of the features. Totally 466 radiomics features were extracted from automatically segmented tumors from T1, T1 contrast, T2, and FLAIR MRIs. The consensus clustering method was used and 10 feature clusters were obtained. All clusters had a prognostic association with survival, where three clusters had a mean C-index \(\ge \)0.60. The medoid features in each clusters with highest C-index were selected as radiomics signature candidates. The maximum and mean C-indices of the medoids are 0.75 and 0.68. The results demonstrated that the clusters reduced the data redundancy as well as generated clinical relevant radiomics features.
Z.-C. Li—This work was supported by the National Natural Science Foundation of China (No. 61571432), National High-Tech R&D Program of China for Young Scientist (863 program, No. 2015AA020933), National Basic Research Program of China (973 Program, No. 2015CB755500), Outstanding Young Scholar Program of Guangdong Province (2014TQ01R060), Shenzhen Basic Research Project (JCYJ20140417113430585), Shenzhen Kongque Overseas Innovation Program (KQCX20140521115045441), and Innovation Team Program in Guangdong Province (2011S013).
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Li, ZC. et al. (2016). Clustering of MRI Radiomics Features for Glioblastoma Multiforme: An Initial Study. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_28
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DOI: https://doi.org/10.1007/978-3-319-43775-0_28
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