Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy

Sandra Ortega-Martorell, Ivan Olier, Teresa Delgado-Goñi, Magdalena Ciezka, Margarida Julià-Sapé, Paulo Lisboa, Carles Arús

Research output: Other contribution

2 Citations (Scopus)


Glioblastomas are one the most aggressive brain tumors. Their usual bad prognosis is due to the heterogeneity of their response to treatment and the lack of early and robust biomarkers to decide whether the tumor is responding to therapy. In this work, we propose the use of a semi-supervised methodology for source extraction to identify the sources representing tumor response to therapy, untreated/unresponsive tumor, and normal brain; and create nosological images of the response to therapy based on those sources. Fourteen mice were used to calculate the sources, and an independent test set of eight mice was used to further evaluate the proposed approach. The preliminary results obtained indicate that was possible to discriminate response and untreated/unresponsive areas of the tumor, and that the color-coded images allowed convenient tracking of response, especially throughout the course of therapy.

Original languageAmerican English
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479945191
Publication statusPublished - 13 Jan 2015

Publication series

NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings


  • brain tumors
  • glioblastoma
  • non-negative matrix factorization
  • nosological imaging
  • response to therapy
  • semi-supervised source extraction


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