Kohonen Clasification Applying 'Missing Variables' Criterion to Evaluate the p-boronophenylalanine Human-body-concentration Decreasing Profile of Boron-Neutron-Capture-Therapy Patients

MAGALLANES, JORGE F.; GARCÍA REIRIZ, ALEJANDRO G.; LIBERMAN, SARA; ZUPAN, JURE

JOURNAL OF CHEMOMETRICS

JOHN WILEY & SONS LTD

Año: 2011 vol. 25 p. 340 - 347

0886-9383

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 experimentaly 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 in the human body during BNCT treatment, a neural network method Kohonen based 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 was tested by the leave-one-out procedure. The results show that the method is very robust and mostly independent on small variations. It can yield predictions, Root Mean Squared Prediction Error, 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 advantage of this method is the automatic improving of the prediction ability and robustness of the model by feeding it with an increasing number of data.