Method to increase sensitivity and specificity of computer-aided detection system for mammographic images in dense breast parenchyma

Cover Page

Cite item


Aim. To search ways to increase the diagnostic value of computer-aided detection of pathological lesions for mammography based on the principle of comparing the images of contralateral breasts.

Methods. Analysis of the diagnostic value of computer-aided detection of pathological lesions for mammography MammCheck 1.15 of our own design, which included asymmetric regions and the brightness transformation search algorithms, was performed. To test this system standard digital mammograms in craniocaudal and mediolateral oblique views of 117 patients with morphologically verified breast cancer (visualized as focal lesions with or without microcalcifications) and 114 patients who did not have malignant tumors, which was confirmed by the results of a 3-year follow-up, were used. All mammograms had a density 3-4 (C-D) according to the ACR classification. In 23 of the 117 patients, visualized changes corresponded to breast cancer were blur or generally not visible with unaided eye on standard mammograms.

Results.Method overall sensitivity was 80.3%, false positive rate - 13.2%. Sensitivity in identifying lesions with microcalcifications was higher (100%) compared to the lesions without microcalcifications (78.1%, p

Conclusion. The breast parenchyma density remains a problem for the computer-aided detection of pathological lesions in cancer diagnosis, especially when not accompanied by the microcalcifications, however, these systems can detect malignant lesions, which are invisible or barely visible in the standard study, and therefore their use is advisable as an option for a second or third mammograms reading.

About the authors

D V Pasynkov

Republican Oncology Center of the Republic of Mari El

Author for correspondence.

I V Kliouchkine

Kazan State Medical University


O V Busygina

Republican Oncology Center of the Republic of Mari El



  1. Клюшкин И.В., Пасынков Д.В., Бусыгина О.В., Пасынкова О.О. К вопросу о возможном повышении риска рака молочной железы у пациенток, перенёсших оперативные вмешательства на ней по поводу доброкачественной патологии. Казанский мед. ж. 2015; 96 (3): 316-321.
  2. Клюшкин И.В., Пасынков Д.В., Насруллаев М.Н., Пасынкова О.В. Эффективность ультразвукового скрининга рака молочной железы у больных фиброзно-кистозной болезнью. Казанский мед. ж. 2009; 90 (2): 213-217.
  3. Пасынков Д.В., Клюшкин И.В. Автоматическая компьютерная расшифровка рентгеномаммограмм. Казанский мед. ж. 2009; 90 (2): 223-227.
  4. Чувашаев И.Р., Акберов Р.Ф. Диагностическая эффективность комплексного лучевого исследования молочных желёз при заболеваниях, сопровождающихся увеличением подмышечных лимфоузлов. Казанский мед. ж. 2009; 90 (2): 212-214.
  5. Adepoju T.M., Ojo J.A., Omidiora E.O. et al. Detection of tumour based on breast tissue categorization. Brit. J. Applied Sci. Technol. 2015; 11 (5): 1-12.
  6. Baker J.A., Lo J.Y., Delong D.M. et al. Computer-aided detection in screening mammography: variability in cues. Radiology. 2004; 233: 411-417.
  7. Bigenwald R.Z., Warner E., Gunasekara A. et al. Is Mammography adequate for screening women with inherited BRCA mutations and low breast density? Cancer Epidemiol. Biomarkers Prev. 2008; 17: 706.
  8. Boyd N.F., Guo H., Martin L.J. et al. Mammographic density and the risk and detection of breast cancer. N. Engl. J. Med. 2007; 356: 227-236.
  9. Boyd N.F., Martin L.J., Sun L. et al. Body size, mammographic density and breast cancer risk. Cancer Epidemiol. Biomarkers. Prev. 2006; 15: 2086-2092.
  10. Brem R.F., Baum J., Lechner M. et al. Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial. AJR Am. J. Roentgenol. 2003; 181: 687-693.
  11. Dheeba J., Albert Singh N., Tamil Selvi S. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 2014; 49: 45-52.
  12. Elmore J.G., Barton M.B., Moceri V.M. et al. Ten-year risk of false positive screening mammograms and clinical breast exams. NEJM. 1999; 338: 1089-1096.
  13. Freer T.W., Ulissey M.J. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology. 2001; 220: 781-786.
  14. Karssemeijer N., Otten J.D.M., Verbeek A.L.M. et al. Computer-aided detection versus independent double reading of masses on mammograms. Radiology. 2003; 227: 192-200.
  15. McCormack V.A., dos Santos Silva S.I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomarkers Prev. 2006; 15: 1159-1169.
  16. Melton A.R., Worrell S.W., Knapp J. et al. Computer-aided detection with full-field digital mammography and screen-film mammography. Am. J. Roentgenol. 2007; 188: A36-A39.
  17. Morton M.J., Whaley D.H., Brandt K.R. et al. Screening mammograms: interpretation with computer-aided detection - prospective evaluation. Radiology. 2006; 239: 375-383.
  18. Park C.S., Jung N.Y., Kim K. et al. Detection of breast cancer in asymptomatic and symptomatic groups using computer-aided detection with full-field digital mammography. J. Breast Cancer. 2013; 16 (3): 322-328.
  19. Romero C., Almenar A., Pinto J.M. et al. Impact on breast cancer diagnosis in a multidisciplinary unit after the incorporation of mammography digitalization and computer-aided detection systems. Am. J. Roentgenol. 2011; 197: 1492-1497.
  20. Sohns C., Angic B., Sossalla S. et al. Computer-assisted diagnosis in full-field digital mammography - results in dependence of readers experiences. Breast J. 2010; 16: 490-497.
  21. Wei J., Sahiner B., Hadjiiski L.M. et al. Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 2005; 32: 2827-2838.
  22. Yaghjyan L., Colditz G.A., Collins L.C. et al. Mammographic breast density and subsequent risk of breast cancer in 7 postmenopausal women according to tumor characteristics. J. Natl. Cancer Inst. 2011; 103 (15): 1179-1189.
  23. Zhao Y., de Bock G.H., Vliegenthart R. et al. Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume. Eur. Radiol. 2012; 22 (10): 2076-2084.

© 2016 Pasynkov D.V., Kliouchkine I.V., Busygina O.V.

Creative Commons License

This work is licensed
under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies