Prediction of clinical response to excimer laser treatment in vitiligo by using neural network models.
BACKGROUND
A predictive model may help to select likely responders and to anticipate treatment duration in vitiligo.


METHODS
We aimed to develop a predictive rule based on data from a randomized trial of excimer laser in vitiligo. Information on 325 treated patches was available. The degree of repigmentation was assessed by digital image analysis of UVB-reflected photographs. Since no strong relationship between any single predictive parameter and outcome was initially documented, we relied on artificial neural networks.


RESULTS
Using a time-response optimal threshold model, data were divided into 2 groups of responders and nonresponders. A discriminant network was trained in order to detect responders versus nonresponders. A regression network was subsequently used to compute repigmentation time in responders. The neural network discriminator achieved 66.46 +/- 5.37% (95% CI) overall accuracy. The mean absolute error of the neural network regressor was 19.5843 +/- 2.0930 with a root mean square error of 23.7156 +/- 2.2225.


CONCLUSIONS
Our study offers insight into the difficulty of clinical prediction in vitiligo and presents a way to develop an instrument with which to predict the clinical time response in patients treated by excimer laser.
Cazzaniga
Simone
S
Centro Studi GISED, Fondazione per la Ricerca Ospedale Maggiore (FROM), presidio ospedaliero Matteo Rota, Bergamo, Italy. cazzaniga_81@hotmail.it
Sassi
Fabrizia
F
Mercuri
Santo Raffaele
SR
Naldi
Luigi
L
D000328:Adult; D005260:Female; D006801:Humans; D054018:Lasers, Excimer / Q000627:therapeutic use*; D028022:Low-Level Light Therapy / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017060:Patient Satisfaction; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012720:Severity of Illness Index; D016896:Treatment Outcome; D014820:Vitiligo / Q000473:pathology / Q000532:radiotherapy*