Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net

Journal of Neuropathology and Experimental Neurology
M J McKeown, D A Ramsay

Abstract

The classification of astrocytomas, astrocytomas with anaplastic foci and glioblastoma multiformes is not always straightforward because the tumors form a histological continuum. The use of principal component analysis (PCA) and neural nets in the classification of these tumors is explored. PCA was performed on 14 histological features recorded from 52 gliomas classified by the Radiation Therapy Oncology Group method (17 astrocytomas, 18 astrocytomas with anaplastic foci, 17 glioblastoma multiformes). Four of the 14 possible 'scores' derived from this analysis were selected to summarize the histological variability seen in all the tumors. These scores were mostly significantly different between tumor types and were thus used to successfully train a neural net to correctly classify these tumors. The first principal component (score) supported the use of increasing cellularity, mitoses, endothelial proliferation, and necrosis in differentiating between the tumor categories, but accounted for only 39% of the variability seen. Other histological features that were significant components of the other scores included the presence of multinucleated or giant cells, gemistocytes, atypical mitoses and changes in nuclear chromatin. Comput...Continue Reading

Citations

Sep 26, 2013·BioMed Research International·Marlene HumlKurt Schilcher
Feb 8, 2000·Surgery·P J Drew, J R Monson
Nov 13, 2012·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Chamidu AtupelageMichiie Sakamoto
Aug 13, 2005·Artificial Intelligence in Medicine·E I PapageorgiouG N Nikiforidis
Oct 31, 2003·Biopolymers·Gerald SteinerHenry H Mantsch

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