Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-018-31007-2.pdf
Reference44 articles.
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3. Topkan, E., Topuk, S., Oymak, E., Parlak, C. & Pehlivan, B. Pseudoprogression in patients with glioblastoma multiforme after concurrent radiotherapy and temozolomide. American journal of clinical oncology 35, 284–289 (2012).
4. Chu, H. H. et al. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology 269, 831–840 (2013).
5. Park, J. E., Kim, H. S., Goh, M. J., Kim, S. J. & Kim, J. H. Pseudoprogression in patients with glioblastoma: assessment by using volume-weighted voxel-based multiparametric clustering of MR imaging data in an independent test set. Radiology 275, 792–802 (2015).
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