ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF RESPONSE TO CHEMORADIATION IN HT29 XENOGRAFTS

作者：Kakar Manish^*; Seierstad Therese; Roe Kathrine; Olsen Dag Rune

来源：International Journal of Radiation Oncology, Biology, Physics, 2009, 75(2): 506-511.

DOI：10.1016/j.ijrobp.2009.05.036

• BPNN
• DWMRI
• ADC
• Prediction
• Treatment response

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(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).
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 Tdelay 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).
Conclusions: BPNN was successfully used to predict T(delay) from tumor ADC values obtained from HT29 xenografts undergoing fractionated chemoradiation therapy.

• 出版日期2009-10-1