Abstract
This study aimed to build a reliable radiomics model from magnetic resonance imaging (MRI) for pretreatment prediction of MGMT methylation status in Glioblastoma. High-throughput radiomics features were automatically extracted from multiparametric MRI, including location features, geometry features, intensity features and texture features. A machine learning method was used to select a minimal set of all-relevant features. Based on these selected features, a radiomics model were built by using a random forest classifier for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing radiomics features and clinical factors were built and evaluated. The radiomics model with 6 all-relevant features allowed pretreatment prediction of MGMT methylation (AUC = 0.88, accuracy = 80%). Combing clinical factors with radiomics features did not benefit the prediction performance. The proposed radiomics model could provide a tool to guide preoperative patient care and made a step forward radiomics-based precision medicine for GBM patients.
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References
Ostrom, Q.T., Gittleman, H., Xu, J. et al: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009–2013. Neuro-Oncology 18:v1-75 (2016). https://doi.org/10.1093/neuonc/now207
Weller, M., Stupp, R., Reifenberger, G. et al: MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat. Rev. Neurol. 6:39–51 (2010). https://doi.org/10.1038/nrneurol.2009.197
Lambin, P., Leijenaar, R.T., Deist, T.M. et al: Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14:749–762 (2017). https://doi.org/10.1038/nrclinonc.2017.141
Korfiatis, P., Kline, T.L., Coufalova, L. et al: MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med. Phys. 43:2835–2844 (2016) https://doi.org/10.1118/1.4948668
Patel, A.P., Tirosh, I., Trombetta, J.J. et al: Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401 (2014). https://doi.org/10.1126/science.1254257
Ltjnen, J.M., Wolz, R, Koikkalainen., J.R. et al: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49:2352–2365 (2010). https://doi.org/10.1016/j.neuroimage.2009.10.026
Pereira, S., Pinto, A., Alves, V., Silva, C.A. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE. T. Med. Imaging. 35:1240–1251 (2016) https://doi.org/10.1109/tmi.2016.2538465
Kursa, M.B., Rudnicki, W.R.: Feature selection with the Boruta package. J. Stat. Softw. 36:1–13 (2010). https://doi.org/10.18637/jss.v036.i11
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No.61571432), and Shenzhen Basic Research Project (JCYJ20170413162354654).
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Li, ZC. et al. (2019). Multiregional Radiomics Phenotypes at MR Imaging Predict MGMT Promoter Methylation in Glioblastoma. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_25
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DOI: https://doi.org/10.1007/978-981-10-9035-6_25
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