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

Ortega-Martorell, S and Olier, I and Delgado-Goni, T and Ciezka, M and Julià-Sapé, M and Lisboa, P and Arús, C (2015) Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy. In: CIDM 2014: Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on . pp. 93-98. (2014 IEEE Symposium on Computation Intelligence and Data Mining, 9th-12th December 2014, Orlando, FL).

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Applied Mathematics
Publisher: IEEE
Date Deposited: 18 Mar 2016 10:21
Last Modified: 07 Oct 2016 11:00
DOI or Identification number: 10.1109/CIDM.2014.7008653
URI: http://researchonline.ljmu.ac.uk/id/eprint/3283

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