Kohonen classification applying ‘missing variables’ criterion to evaluate the p-boronophenylalanine human-body-concentration decreasing profile of boron neutron capture therapy patients
Article first published online: 17 MAR 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Journal of Chemometrics
Volume 25, Issue 6, pages 340–348, June 2011
How to Cite
Magallanes, J., García-Reiriz, A., Líberman, S. and Zupan, J. (2011), Kohonen classification applying ‘missing variables’ criterion to evaluate the p-boronophenylalanine human-body-concentration decreasing profile of boron neutron capture therapy patients. J. Chemometrics, 25: 340–348. doi: 10.1002/cem.1383
- Issue published online: 28 JUN 2011
- Article first published online: 17 MAR 2011
- Manuscript Accepted: 11 JAN 2011
- Manuscript Revised: 11 NOV 2010
- Manuscript Received: 27 SEP 2010
- artificial neural networks;
- boron neutron capture therapy (BNCT);
- tumor irradiation
The irradiation dose in tumor and healthy tissue of a boron neutron capture therapy (BNCT) patient depends on the boron concentration in blood. In most treatments, this concentration is experimentally determined before and after irradiation but not while irradiation is being carried out because it is troublesome to take the blood samples when the patient remains isolated in the irradiation room. A few models are used to predict the boron profile during that period, which until now involves a biexponential decay. For the prediction of decay concentration profiles of the p-boronophenylalanine (BPA) in the human body during BNCT treatment, a Kohonen-based neural network method is suggested. The results of various (20 × 20 × 40 Kohonen network) models based on different trainings on the data set of 67 concentration sets (profiles) are described and discussed. The prediction ability and robustness of the modeling method were tested by the leave-one-out procedure. The results show that the method is very robust and mostly independent of small variations. It can yield predictions, root mean squared prediction error (RMSPE), with a maximum of 3.30 µg g−1 for the present cases. In order to show the abilities and limitations of the method, the best and the few worst results are discussed in detail. It should be emphasized that one of the main advantages of this method is the automatic improvement in the prediction ability and robustness of the model by feeding it with an increasing number of data. Copyright © 2011 John Wiley & Sons, Ltd.