Belfatto Antonella, White Derek A, Mason Ralph P, Zhang Zhang, Stojadinovic Strahinja, Baroni Guido, Cerveri Pietro
Department of Electronics, Information and Bioengineering, Politecnico di Milano University, Milan 20133, Italy.
Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390.
Med Phys. 2016 Mar;43(3):1275-84. doi: 10.1118/1.4941746.
Radiation therapy is one of the most common treatments in the fight against prostate cancer, since it is used to control the tumor (early stages), to slow its progression, and even to control pain (metastasis). Although many factors (e.g., tumor oxygenation) are known to influence treatment efficacy, radiotherapy doses and fractionation schedules are often prescribed according to the principle "one-fits-all," with little personalization. Therefore, the authors aim at predicting the outcome of radiation therapy a priori starting from morphologic and functional information to move a step forward in the treatment customization.
The authors propose a two-step protocol to predict the effects of radiation therapy on individual basis. First, one macroscopic mathematical model of tumor evolution was trained on tumor volume progression, measured by caliper, of eighteen Dunning R3327-AT1 bearing rats. Nine rats inhaled 100% O2 during irradiation (oxy), while the others were allowed to breathe air. Second, a supervised learning of the weight and biases of two feedforward neural networks was performed to predict the radio-sensitivity (target) from the initial volume and oxygenation-related information (inputs) for each rat group (air and oxygen breathing). To this purpose, four MRI-based indices related to blood and tissue oxygenation were computed, namely, the variation of signal intensity ΔSI in interleaved blood oxygen level dependent and tissue oxygen level dependent (IBT) sequences as well as changes in longitudinal ΔR1 and transverse ΔR2(*) relaxation rates.
An inverse correlation of the radio-sensitivity parameter, assessed by the model, was found with respect the ΔR2() (-0.65) for the oxy group. A further subdivision according to positive and negative values of ΔR2() showed a larger average radio-sensitivity for the oxy rats with ΔR2(*)<0 and a significant difference in the two distributions (p < 0.05). Finally, a leave-one-out procedure yielded a radio-sensitivity error lower than 20% in both neural networks.
While preliminary, these specific results suggest that subjects affected by the same pathology can benefit differently from the same irradiation modalities and support the usefulness of IBT in discriminating between different responses.
放射治疗是对抗前列腺癌最常用的治疗方法之一，因为它可用于控制肿瘤（早期阶段）、减缓其进展，甚至控制疼痛（转移）。尽管已知许多因素（如肿瘤氧合）会影响治疗效果，但放射治疗剂量和分割方案通常按照“一刀切”的原则开具，几乎没有个性化。因此，作者旨在从形态学和功能信息出发，先验地预测放射治疗的结果，以便在治疗定制方面更进一步。
作者提出了一个两步方案来逐个预测放射治疗的效果。首先，基于18只荷Dunning R3327 - AT1肿瘤大鼠的肿瘤体积进展（通过卡尺测量），训练了一个肿瘤演化的宏观数学模型。9只大鼠在照射期间吸入100%氧气（富氧组），而其他大鼠呼吸空气。其次，对两个前馈神经网络的权重和偏差进行监督学习，以根据每组大鼠（呼吸空气和呼吸氧气）的初始体积和与氧合相关的信息（输入）预测放射敏感性（目标）。为此，计算了四个基于MRI的与血液和组织氧合相关的指标，即交错的血氧水平依赖和组织氧水平依赖（IBT）序列中的信号强度变化ΔSI，以及纵向ΔR1和横向ΔR2（*）弛豫率的变化。
模型评估的放射敏感性参数与富氧组的ΔR2（）呈负相关（-0.65）。根据ΔR2（）的正值和负值进一步细分显示，ΔR2（*）<0的富氧大鼠平均放射敏感性更高，且两种分布存在显著差异（p < 0.05）。最后，留一法程序在两个神经网络中产生的放射敏感性误差均低于20%。
虽然这些结果是初步的，但这些特定结果表明，患有相同疾病的个体可能从相同的照射方式中获得不同的益处，并支持IBT在区分不同反应方面的有用性。
