Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach.
The incidence of late-toxicities after radiotherapy can be modelled based on the dose delivered to the organ under consideration. Most predictive models reduce the dose distribution to a set of dose-volume parameters and do not take the spatial distribution of the dose into account. The aim of this study was to develop a classifier predicting radiation-induced rectal bleeding using all available information on the dose to the rectal wall. The dose was projected on a two-dimensional dose-surface map (DSM) by virtual rectum-unfolding. These DSMs were used as inputs for a classification method based on locally connected neural networks. In contrast to fully connected conventional neural nets, locally connected nets take the topology of the input into account. In order to train the nets, data from 329 patients from the RT01 trial (ISRCTN 47772397) were split into ten roughly equal parts. By using nine of these parts as a training set and the remaining part as an independent test set, a ten-fold cross-validation was performed. Ensemble learning was used and 250 nets were built from randomly selected patients from the training set. Out of these 250 nets, an ensemble of expert nets was chosen. The performances of the full ensemble and of the expert ensemble were quantified by using receiver-operator-characteristic (ROC) curves. In order to quantify the predictive power of the shape, ensembles of fully connected conventional neural nets based on dose-surface histograms (DSHs) were generated and their performances were quantified. The expert ensembles performed better than or equally as well as the full ensembles. The area under the ROC curve for the DSM-based expert ensemble was 0.64. The area under the ROC curve for the DSH-based expert ensemble equalled 0.59. This difference in performance indicates that not only volumetric, but also morphological aspects of the dose distribution are correlated to rectal bleeding after radiotherapy. Thus, the shape of the dose distribution should be taken into account when a predictive model for radiation-induced rectal bleeding is developed.
Buettner
Florian
F
Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT, UK. florian.buttner@icr.ac.uk
Gulliford
Sarah L
SL
Webb
Steve
S
Partridge
Mike
M
D006470:Hemorrhage / Q000209:etiology*; D006801:Humans; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D012372:ROC Curve; D011829:Radiation Dosage; D011832:Radiation Injuries / Q000209:etiology*; D011879:Radiotherapy Dosage; D012007:Rectum / Q000528:radiation effects*; D015203:Reproducibility of Results