Artificial neural networks for prediction of response to chemoradiation in HT29 xenografts.

Abstract

To evaluate the feasibility of using neural networks for predicting treatment response by using longitudinal measurements of apparent diffusion coefficient (ADC) obtained from diffusion-weighted magnetic resonance imaging (DWMRI). Mice bearing HT29 xenografts were allocated to six treatment groups receiving different combinations of daily chemotherapy and/or radiation therapy for 2 weeks. T(2)-weighted and DWMR images were acquired before treatment, twice during fractionated chemoradiation (at days 4 and 11), and four times after treatment ended (at days 18, 25, 32, and 46). A tumor doubling growth delay (T(delay)) value was found for individual xenografts. ADC values and treatment groups (1-6) were used as input to a back propagation neural network (BPNN) to predict T(delay). When treatment group and ADC values from days 0, 4, 11, 18, 25, 32, and 46 were used as inputs to the BPNN, a strong correlation between measured and predicted T(delay) values was found (R = 0.731, p < 0.01). When ADC values from days 0, 4, and 11, and the treatment group were used as inputs, the correlation between predicted and measured T(delay) was 0.693 (p < 0.01). BPNN was successfully used to predict T(delay) from tumor ADC values obtained from HT29 xenografts undergoing fractionated chemoradiation therapy.