Pre-optimization of radiotherapy treatment planning: an artificial neural network classification aided technique.
A method has been developed which, by using the geometric information from treatment sample cases, selects from a given data set an initial treatment plan as a step for treatment plan optimization. The method uses an artificial neural network (ANN) classification technique to select a best matching plan from the 'optimized' ANN database. Separate back-propagation ANN classifiers were trained using 50, 60 and 77 examples for three groups of treatment case classes (up to 21 examples from each class were used). The performance of the classifiers in selecting the correct treatment class was tested using the leave-one out method; the networks were optimized with respect their architecture. For the three groups used in this study, successful classification fractions of 0.83, 0.98 and 0.93 were achieved by the optimized ANN classifiers. The automated response of the ANN may be used to arrive at a pre-plan where many treatment parameters may be identified and therefore a significant reduction in the steps required to arrive at the optimum plan may be achieved. Treatment planning 'experience' and also results from lengthy calculations may be used for training the ANN.
Hosseini-Ashrafi
M E
ME
Department of Physics and Nuclear Science, Amir Kabir University, Tehran, Iran.
Bagherebadian
H
H
Yahaqi
E
E
D015233:Models, Statistical; D016571:Neural Networks, Computer; D011880:Radiotherapy Planning, Computer-Assisted