Prediction of Breast Cancer Using Artificial Neural Networks

In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.

[1]  E. Hwang,et al.  Ductal carcinoma in situ (DCIS): posttreatment follow-up care among Latina and non-Latina White women , 2013, Journal of Cancer Survivorship.

[2]  Carey E Floyd,et al.  Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon. , 2002, Medical physics.

[3]  R. Rakel,et al.  Conn's current therapy , 2006 .

[4]  A. Ronco,et al.  Improving ultrasonographic diagnosis of prostate cancer with neural networks. , 1999, Ultrasound in medicine & biology.

[5]  J. Connolly,et al.  Clinically occult ductal carcinoma in situ detected with mammography: analysis of 100 cases with radiologic-pathologic correlation. , 1989, Radiology.

[6]  Anna O. Bilska-Wolak,et al.  Computer aid for decision to biopsy breast masses on mammography: validation on new cases. , 2005, Academic radiology.

[7]  L. Liberman,et al.  Breast imaging reporting and data system (BI-RADS). , 2002, Radiologic clinics of North America.

[8]  Mark A Helvie,et al.  Mammographic breast density: effect on imaging and breast cancer risk. , 2010, Journal of the National Comprehensive Cancer Network : JNCCN.

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

[10]  Ilker Ali Ozkan,et al.  Prognosis of prostate cancer by artificial neural networks , 2010, Expert Syst. Appl..

[11]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

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

[13]  Carey E. Floyd,et al.  Investigating different similarity measures for a case-based reasoning classifier to predict breast cancer , 2001, SPIE Medical Imaging.

[14]  Thomas Tolxdorff,et al.  Classification Models for Early Detection of Prostate Cancer , 2008, Journal of biomedicine & biotechnology.

[15]  M.K. Markey,et al.  Bayesian networks of BI-RADS/spl trade/ descriptors for breast lesion classification , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  M. Elter,et al.  The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. , 2007, Medical physics.

[17]  David Page,et al.  Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming , 2010, IHI.

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