Bayesian networks of BI-RADS/spl trade/ descriptors for breast lesion classification

We investigated Bayesian network structure learning and probability estimation from mammographic feature data in order to classify breast lesions into different pathological categories. We compared the learned networks to naive Bayes classifiers, which are similar to the expert systems previously investigated for breast lesion classification. The learned network structures reflect the difference in the classification of biopsy outcome and the invasiveness of malignant lesions for breast masses and microcalcifications. The difference between masses and microcalcifications should be taken into consideration when interpreting systems for automatic pathological classification of breast lesions. The difference may also affect use of these systems for tasks such as estimating the sampling error of biopsy.

[1]  Y H Chang,et al.  Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm. , 1999, Academic radiology.

[2]  Ross D. Shachter,et al.  A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience. , 2004, AJR. American journal of roentgenology.

[3]  C. Floyd,et al.  Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer. , 2002, AJR. American journal of roentgenology.

[4]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

[5]  Joseph Y. Lo,et al.  Self-organizing map for cluster analysis of a breast cancer database , 2003, Artif. Intell. Medicine.

[6]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[7]  C. Floyd,et al.  Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. , 1995, Radiology.

[8]  C. Floyd,et al.  Artificial neural network: improving the quality of breast biopsy recommendations. , 1996, Radiology.

[9]  C. Floyd,et al.  Differences between computer-aided diagnosis of breast masses and that of calcifications. , 2002, Radiology.

[10]  L. Liberman,et al.  The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. , 1998, AJR. American journal of roentgenology.

[11]  C. Floyd,et al.  Effect of patient histoy data on the prediction of breast cancer from mammographic findings with artificial neural networks , 1999 .

[12]  J. York,et al.  Bayesian Graphical Models for Discrete Data , 1995 .

[13]  Carol H Lee.  Screening mammography: proven benefit, continued controversy. , 2002, Radiologic clinics of North America.

[14]  Ross D. Shachter,et al.  A Bayesian network for mammography , 2000, AMIA.

[15]  C. Floyd,et al.  Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. , 2000, AJR. American journal of roentgenology.

[16]  C. Floyd,et al.  A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. , 2001, Medical physics.

[17]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[18]  P Haddawy,et al.  Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[19]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[20]  P Haddawy,et al.  Construction of a Bayesian network for mammographic diagnosis of breast cancer , 1997, Comput. Biol. Medicine.

[21]  C E Floyd,et al.  Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. , 1997, Radiology.

[22]  D. Kopans.  The positive predictive value of mammography. , 1992, AJR. American journal of roentgenology.

[23]  Paolo Giudici,et al.  Improving Markov Chain Monte Carlo Model Search for Data Mining , 2004, Machine Learning.

[24]  Georgia D. Tourassi,et al.  Perceptron error surface analysis: a case study in breast cancer diagnosis , 2002, Comput. Biol. Medicine.

[25]  George Hermann,et al.  Mammographic Appearance of Nonpalpable Breast Cancer Reflects Pathologic Characteristics , 2002, Annals of surgery.

[26]  C. Floyd,et al.  Prediction of breast cancer malignancy using an artificial neural network , 1994, Cancer.

[27]  J Y Lo,et al.  Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. , 1995, Academic radiology.