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首页> 外文期刊>Digest Journal of Nanomaterials and Biostructures >COMPARISON OF MULTIPLE LINEAR REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORK APPROACHES IN THE ESTIMATION OF MONTE CARLO MEAN GLANDULAR DOSE CALCULATIONS OF MAMMOGRAPHY
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COMPARISON OF MULTIPLE LINEAR REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORK APPROACHES IN THE ESTIMATION OF MONTE CARLO MEAN GLANDULAR DOSE CALCULATIONS OF MAMMOGRAPHY

机译：乳腺摄影中蒙特卡洛平均腺体剂量估算的多元线性回归分析与人工神经网络方法的比较

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Mammography is an x-ray based breast imaging process which uses radiological method as a non-invasive way for the diagnosis of breast diseases common among woman subjects. A breast screening operation employs mammography in the early recognition of abnormalities in breast construction such as micro-calcifications, which could develop a breast carcinoma. On the other hand, breast dosimetry is an indispensable issue on behalf of patient radiation safety and evaluation of potential risks from medical radiation. In this study, we first aimed to investigate capabilities of Monte Carlo N-Particle eXtended (MCNPX) code for calculations of Mean Glandular Dose (MGD) in a mathematical breast phantom during mammography screening. MGD values were investigated by using MCNPX (version 2.4.0) Monte Carlo code. A mathematical breast phantom has been modeled in an average shape by defining the dimensions x, y and z. The breast model has been considered as semi-elliptical cylindrical geometry in different thicknesses as cranio-caudal projection. Afterwards, x-ray spectra from W/Rh target-filter combination has been obtained and defined as a spectrum into source definition in MCNPX input file. Following the Monte Carlo calculations process, a linear, multiple linear regression analysis (MLRA), and a nonlinear, artificial neural network (ANN), approach was employed in order to put forward an alternative predictive model. Finally, the performance comparison of the aforementioned models were expressed in terms of five accuracy indices, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE) and R2 coefficient of determination. The results underlined that both of the models perform quite satisfactorily and MGD values are strongly correlated with three independent variables which are breast thickness, X-ray spectra and glandular-adipose rate.
机译：乳腺摄影术是一种基于X射线的乳腺成像过程，它使用放射学方法作为非侵入性方法来诊断女性受试者中常见的乳腺疾病。乳房筛查手术在早期发现乳房结构异常（例如微钙化）时会使用乳房X线照相术，这可能会导致乳腺癌。另一方面，代表患者辐射安全和评估医疗辐射的潜在风险，乳房剂量测定是必不可少的问题。在这项研究中，我们首先旨在研究在乳房X光检查中数学乳腺模型中平均腺体剂量（MGD）的蒙特卡罗N粒子扩展（MCNPX）代码的功能。通过使用MCNPX（版本2.4.0）蒙特卡洛代码研究了MGD值。通过定义尺寸x，y和z，以平均形状对数学乳房模型进行建模。乳房模型已被视为具有不同厚度的半椭圆形圆柱体形状，如颅尾投影。之后，获得了来自W / Rh目标滤波器组合的X射线光谱，并将其定义为MCNPX输入文件中源定义中的光谱。在蒙特卡洛计算过程之后，采用了线性，多元线性回归分析（MLRA）和非线性人工神经网络（ANN）的方法，以提出另一种预测模型。最后，上述模型的性能比较用五个准确度指标表示：平均绝对误差（MAE），平均绝对百分比误差（MAPE），均方根误差（RMSE），归一化均方根误差（NRMSE）和R2的确定系数。结果表明，两个模型的性能都令人满意，MGD值与三个独立变量（乳房厚度，X射线光谱和腺体脂肪率）密切相关。

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