Determination of breast cancer response to bevacizumab therapy using contrast-enhanced ultrasound and artificial neural networks.
Determination of breast cancer response to bevacizumab therapy using contrast-enhanced ultrasound and artificial neural networks.
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DOI：
10.7863/jum.2010.29.4.577
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发表时间：
2010-04
期刊：
影响因子：
--
通讯作者：
Zinn KR
中科院分区：
文献类型：
--
作者：
Hoyt K;Warram JM;Umphrey H;Belt L;Lockhart ME;Robbin ML;Zinn KR
本研究的目的是评估超声造影和神经网络数据分类，以确定小鼠模型中乳腺癌对贝伐珠单抗治疗的反应。采用谐波模式超声扫描仪测量了体内超声造影剂（UCA）的时间-强度曲线。25只患有原位乳腺癌的裸无胸腺小鼠接受了30 μL全氟醚微球UCA的尾静脉推注，并使用微泡破坏-补充技术进行基线肿瘤成像。随后，15只动物接受0.2 mg贝伐珠单抗注射，而10只对照动物接受同等剂量的生理盐水。在安乐死前第1、2、3和6天对动物进行再成像。对切除的肿瘤切片进行组织学评估。使用定制软件对给定的感兴趣区域进行时间-强度曲线分析。使用神经网络数据分类方案（60%学习和40%测试）对第1、2、3和6天的肿瘤灌注度量进行建模，以预测乳腺癌对治疗的反应。在小鼠模型中，贝伐珠单抗单次给药后的乳腺癌应答是立即和短暂的。神经网络数据分类方案的输入排列显示，贝伐珠单抗给药3天内的肿瘤灌注数据足以将预测误差降至10%，而单独测量物理肿瘤大小似乎不足以评估治疗反应。对比增强超声可能是一个有用的工具，用于确定对贝伐单抗治疗的反应和监测随后恢复血流到乳腺癌。
The purpose of this study was to evaluate contrast-enhanced ultrasound and neural network data classification for determining the breast cancer response to bevacizumab therapy in a murine model. An ultrasound scanner operating in the harmonic mode was used to measure ultrasound contrast agent (UCA) time-intensity curves in vivo. Twenty-five nude athymic mice with orthotopic breast cancers received a 30-µL tail vein bolus of a perflutren microsphere UCA, and baseline tumor imaging was performed using microbubble destruction-replenishment techniques. Subsequently, 15 animals received a 0.2-mg injection of bevacizumab, whereas 10 control animals received an equivalent dose of saline. Animals were reimaged on days 1, 2, 3, and 6 before euthanasia. Histologic assessment of excised tumor sections was performed. Time-intensity curve analysis for a given region of interest was conducted using customized software. Tumor perfusion metrics on days 1, 2, 3, and 6 were modeled using neural network data classification schemes (60% learning and 40% testing) to predict the breast cancer response to therapy. The breast cancer response to a single dose of bevacizumab in a murine model was immediate and transient. Permutations of input to the neural network data classification scheme revealed that tumor perfusion data within 3 days of bevacizumab dosing was sufficient to minimize the prediction error to 10%, whereas measurements of physical tumor size alone did not appear adequate to assess the therapeutic response. Contrast-enhanced ultrasound may be a useful tool for determining the response to bevacizumab therapy and monitoring the subsequent restoration of blood flow to breast cancer.
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影响因子：
1.6
作者：
Wang, D.;Stockard, C. R.;Siegal, G. P.
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作者：
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