COMPARISON OF MULTIPLE LINEAR REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORK APPROACHES IN THE ESTIMATION OF MONTE CARLO MEAN GLANDULAR DOSE CALCULATIONS OF MAMMOGRAPHY

Digest Journal of Nanomaterials and Biostructures

T. T. Erguzel¹ 

关键词: Mammography;    Mean glandular dose prediction;    Monte Carlo;    Artificial;    neural network;    Multiple linear regression analysis;   

DOI  :  

学科分类：生物技术

来源: Institute of Materials Physics

【 摘 要 】

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.

【 授权许可】

Unknown