黄金华;刘烁;苏福再;丁雪梅;杨洪钦;

• 1:漳州职业技术学院计算机工程系
• 2:福建师范大学数学与信息学院
• 3:福建师范大学医学光电科学与技术教育部重点实验室

摘要(Abstract)：

利用贝叶斯网络建模方法,定量分析了1 500例良性肿瘤和500例恶性肿瘤临床乳腺肿瘤超声检查相关参量的诊断参考价值以及各参量之间的关联程度.研究结果表明,在超声检查中,形态检查参量的诊断价值最高(40.3%),其次分别为阻力指数(25.0%)、钙化灶(18.4%)和血流信号(16.3%)等检查参量.此外,阻力指数与血流信号之间的关联性比较强,约为0.432.贝叶斯概率模型在乳腺肿瘤超声智能诊断的前期应用研究,有助于帮助医生根据各检查参量诊断参考价值和各参量之间关联程度分析,实现乳腺肿瘤超声的智能诊断,提高诊断准确率.

关键词(KeyWords)： 贝叶斯网络;乳腺肿瘤;超声检查;智能诊断

Abstract:

Keywords:

基金项目(Foundation): 国家重点基础研究发展计划(973计划,2015CB352006);; 国家自然科学基金重点资助项目(61335011)

作者(Authors): 黄金华;刘烁;苏福再;丁雪梅;杨洪钦;

参考文献(References)：

• [1]陈万青,郑荣寿.中国女性乳腺癌发病死亡和生存状况 [J].中国肿瘤临床,2015,42(13):668-674.
• [2]FAN L,STRASSER-WEIPPL K,LI J J,et al.Breast cancer in China [J].Lancet Oncology,2014,15(7):279-289.
• [3]CHEN W Q,ZHENG R S,ZHANG S W,et al.Cancer incidence and mortality in China in 2013:an analysis based on urbanization level [J].Chinese Journal of Cancer Research,2017,29(1):1-10.
• [4]朱铖,郑笑娟.乳腺肿瘤超声诊断 [J].医学研究杂志,2016,45(4):11-13.
• [5]SHAN J,ALAM S K,GARRA B,et al.Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods [J].Ultrasound in Medicine and Biology,2016,42(4):980-988.
• [6]MOON W K,LO C M,CHO N,et al.Computer-aided diagnosis of breast masses using quantified BI-RADS findings [J].Computer Methods and Programs in Biomedicine,2013,111(1):84-92.
• [7]ELTOUKHY M M,FAYE I,SAMIR B B.A statistical based feature extraction methods for breast cancer diagnosis in digital mammogram using multiresolution representation [J].Computers in Biology and Medicine,2012,42(1):123-128.
• [8]MOHAMMED E A,NAUGLER C T,FAR B H.Breast tumor classification using a new OWA operator [J].Expert Systems with Applications,2016,61(C):302-313.
• [9]KALET A M,GENNARI J H,FORD E C,et al.Bayesian network models for error detection in radiotherapy plans [J].Physics in Medicine and Biology,2015,60(7):2735-2749.
• [10]LIU S,ZENG J S,GONG H Z,et al.Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach[J].Computers in Biology and Medicine,2018,12(6):168-175.
• [11]COMSTANTINOU A C,FENTON N,MARSH W,et al.From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support [J].Artificial Intelligence in Medicine,2016,67(2):75-93.
• [12]DALY R,SHEN Q,AITKEN S.Learning Bayesian networks:approaches and issues [J].The Knowledge Engineering Review,2011,26(2):99-157.
• [13]BOUCHAALA L,MASMOUDI A,GARGOURI F,et al.Improving algorithms for structure learning in Bayesian Networks using a new implicit score [J].Expert Systems with Applications,2010,37(7):5470-5475.
• [14]COOPER G F,HERSOVITS E,A Bayesian method for the induction of probabilistic networks from data [J].Machine Learning,1992,9(4):309-347.
• [15]HECKERMAN D,GEIGER D,CHICKERING D M.Learning Bayesian networks:the combination of knowledge and statistical data [J].Machine Learning,1995,20(3):197-243.
• [16]KOITER J R.Visualizing inference in Bayesian networks,master thesis [M].Delft,Netherlands:Delft University of Technology,2006.
• [17]WITTEN I H,FRANK E.Data Mining:practical machine learning tools and techniques [M].Morgan Kaufmann:San Francisco,2005.
• [18]KRAAIJEVELD P.GeNIeRate:an interactive generator of diagnostic Bayesian network models,master thesis [M].Pennsylvania,USA:University of Pittsburgh,2005.
• (1)http://www.breastcancer.org/symptoms/understand_bc/statistics.