Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts
- Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo (Norway)
Purpose: 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). Methods and Materials: Mice bearing HT29 xenografts were allocated to six treatment groups receiving different combinations of daily chemotherapy and/or radiation therapy for 2 weeks. T{sub 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{sub 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{sub delay}. Results: 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{sub 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{sub delay} was 0.693 (p < 0.01). Conclusions: BPNN was successfully used to predict T{sub delay} from tumor ADC values obtained from HT29 xenografts undergoing fractionated chemoradiation therapy.
- OSTI ID:
- 21282054
- Journal Information:
- International Journal of Radiation Oncology, Biology and Physics, Vol. 75, Issue 2; Other Information: DOI: 10.1016/j.ijrobp.2009.05.036; PII: S0360-3016(09)00820-7; Copyright (c) 2009 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0360-3016
- Country of Publication:
- United States
- Language:
- English
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