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Assessment of Breast Cancer Risk in an Iranian Female Population Using Bayesian Networks with Varying Node Number

  • Rezaianzadeh, Abbas (Colorectal Research Center, Shiraz University of Medical Sciences) ;
  • Sepandi, Mojtaba (Faculty of Health, Baqiyatallah University of Medical Sciences) ;
  • Rahimikazerooni, Salar (Colorectal Research Center, Shiraz University of Medical Sciences)
  • Published : 2016.11.01

Abstract

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

Keywords

Breast cancer;Bayesian networks;risk assessment

References

  1. American Cancer Society (2015). Breast Cancer Facts and Figures. Atlanta: American Cancer Society, Inc. 2015.
  2. Chan HP, Sahiner B, Helvie MA, et al (1999). Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: An ROC study. Radiology, 212, 817-27. https://doi.org/10.1148/radiology.212.3.r99au47817
  3. Chhatwal J, Alagoz O, Lindstrom M J, et al (2009). A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol, 192, 1117-27. https://doi.org/10.2214/AJR.07.3345
  4. Erbil N, Dundar N, Inan C, Bolukbas N (2014). Breast cancer risk assessment using the Gail model: a Turkish study. Asian Pac J Cancer prev, 16, 303-6.
  5. Freer TW, Ulissey M J (2001). Screening mammography with computer-aided detection: Prospective study of 12,860 patients in a community breast center 1. Radiology, 220, 781-86. https://doi.org/10.1148/radiol.2203001282
  6. Gotzsche PC, Nielsen M (2011). Screening for breast cancer with mammography. The cochrane library.
  7. Hadjiiski L, Chan HP, Sahiner B, et al (2004). Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: An ROC study 1. Radiology, 233, 255-65. https://doi.org/10.1148/radiol.2331030432
  8. Jacobi CE, de Bock GH, Siegerink B, et al (2009). Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose? Breast Cancer Res Treat, 115, 381-90. https://doi.org/10.1007/s10549-008-0070-x
  9. Jesneck JL, Lo JY, Baker JA (2007). Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors 1. Radiology, 244, 390-98. https://doi.org/10.1148/radiol.2442060712
  10. Liberman L, Abramson AF, Squires FB, et al (1998). The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol, 171, 35-40. https://doi.org/10.2214/ajr.171.1.9648759
  11. Lo JY, Baker JA, Kornguth PJ, Floyd CE (1999). Effect of patient histoy data on the prediction of breast cancer from mammographic findings with artificial neural networks. Academic Radiol, 6, 10-15. https://doi.org/10.1016/S1076-6332(99)80056-7
  12. Lo JY, Markey MK, Baker JA, Floyd Jr, Carey E (2002). evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer. AJR Am J Roentgenol, 178, 457-63. https://doi.org/10.2214/ajr.178.2.1780457
  13. Miglioretti DL, Smith-Bindman R, Abraham L , et al (2007). Radiologist characteristics associated with interpretive performance of diagnostic mammography. J Natl Cancer Inst, 99, 1854-63. https://doi.org/10.1093/jnci/djm238
  14. Sharifian A , Pourhoseingholi MA, Emadedin M, et al (2015). burden of breast cancer in Iranian women is increasing. Asian Pac J Cancer Prev, 16, 5049-52. https://doi.org/10.7314/APJCP.2015.16.12.5049
  15. Sickles E A, Wolverton DE, Dee KE (2002). Performance parameters for screening and diagnostic mammography: specialist and general radiologists 1. Radiology, 224, 861-69. https://doi.org/10.1148/radiol.2243011482
  16. Taplin S, Abraham L, Barlow WE, et al (2008). Mammography facility characteristics associated with interpretive accuracy of screening mammography. J Natl Cancer Inst, 100, 876-887. https://doi.org/10.1093/jnci/djn172
  17. Warren Burhenne LJ, Wood SA, D'Orsi CJ, et al (2000). Potential contribution of computer-aided detection to the sensitivity of screening mammography 1. Radiology, 215, 554-62. https://doi.org/10.1148/radiology.215.2.r00ma15554