Kakar Manish, Seierstad Therese, Røe Kathrine, Olsen Dag Rune
Department of Radiation Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
Int J Radiat Oncol Biol Phys. 2009 Oct 1;75(2):506-11. doi: 10.1016/j.ijrobp.2009.05.036.
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.
通过使用从扩散加权磁共振成像（DWMRI）获得的表观扩散系数（ADC）的纵向测量值，评估使用神经网络预测治疗反应的可行性。
将携带HT29异种移植瘤的小鼠分配到六个治疗组，接受不同组合的每日化疗和/或放射治疗，为期2周。在治疗前、分次放化疗期间（第4天和第11天）两次以及治疗结束后四次（第18天、第25天、第32天和第46天）采集T2加权和DWMR图像。为单个异种移植瘤确定肿瘤倍增生长延迟（T(delay)）值。将ADC值和治疗组（1-6）用作反向传播神经网络（BPNN）的输入，以预测T(delay)。
当将治疗组以及第0天、第4天、第11天、第18天、第25天、第32天和第46天的ADC值用作BPNN的输入时，发现测量的和预测的T(delay)值之间存在强相关性（R = 0.731，p < 0.01）。当将第0天、第4天和第11天的ADC值以及治疗组用作输入时，预测的和测量的T(delay)之间的相关性为0.693（p < 0.01）。
BPNN成功用于从接受分次放化疗的HT29异种移植瘤的肿瘤ADC值预测T(delay)。
