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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dolecek, T.A., Propp, J.M., Stroup, N.E., Kruchko, C.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in united states in 2005–2009. Neuro-Oncol. 14(Suppl. 5), v1–v49 (2012)
Reardon, D.A., Wen, P.Y.: Glioma in 2014: unravelling tumour heterogeneity-implications for therapy. Nat. Rev. Clin. Oncol. 12, 69–70 (2015)
Kumar, V., Gu, Y., Basu, S., et al.: Radiomics: the process and the challenges. Magn. Reson. Imaging 30, 1234–1248 (2012)
Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563–577 (2016)
Aerts, H.J.W.L., Velazquez, E.R., Leijenaar, R.T.H., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014)
Gevaert, O., Mitchell, L.A., Achrol, A.S., et al.: Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image feature. Radiology 273(1), 168–174 (2014)
Vallires, M., Freeman, C.R., Skamene, S.R., et al.: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys. Med. Biol. 60, 5471–5496 (2015)
O’Connor, J.P., Rose, C.J., Waterton, J.C., et al.: Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin. Cancer Res. 21(2), 249–257 (2015)
Cui, Y., Tha, K.K., Terasaka, S., et al.: Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 278(2), 546–553 (2016)
Velazquez, E.R., et al.: Fully automatic GBM segmentation in the TCGA-GBM dataset: prognosis and correlation with VASARI features. Sci. Rep. 5, 16822 (2015)
Parmar, C., et al.: Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci. Rep. 5, 11044 (2015)
Parmar, C., et al.: Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 5, 13087 (2015)
Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Zhang, J., Barborial, D.P., Hobbs, H., et al.: A fully automatic extraction of magnetic resonance image features in glioblastoma patients. Med. Phys. 41(4), 042301 (2014)
Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003)
Wilkerson, M.D., Hayes, D.N.: ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010)
Pencina, M.J., D’Agostino, R.B.: Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat. Med. 23, 2109–2123 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-43775-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43774-3
Online ISBN: 978-3-319-43775-0
eBook Packages: Computer ScienceComputer Science (R0)