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“Hippocrates-mst”: a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer

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Abstract

One of the most common cancer types among women is breast cancer. Regular mammographic examinations increase the possibility for early diagnosis and treatment and significantly improve the chance of survival for patients with breast cancer. Clustered microcalcifications have been considered as important indicators of the presence of breast cancer. We present “Hippocrates-mst”, a prototype system for computer-aided risk assessment of breast cancer. Our research has been focused in developing software to locate microcalcifications on X-ray mammography images, quantify their critical features and classify them according to their probability of being cancerous. A total of 260 cases (187 benign and 73 malignant) have been examined and the performance of the prototype is presented through receiver operating characteristic (ROC) analysis. The system is showing high levels of sensitivity identifying correctly 98.63% of malignant cases.

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References

  1. American College of Radiology, Appropriate imaging work-up of breast microcalcifications. URL: http://www.acr.org

  2. Bassett LW (1992) Mammographic analysis of calcifications. Radiol Clin North Am 30(1):93–105

    Google Scholar 

  3. Berg WA, D’Orsi CJ, Jackson VP, Bassett LW, Beam CA, Lewis RS, Crewson PE (2002) Does training in the breast imaging reporting and data system (BI-RADS) improve biopsy recommendations or feature analysis agreement with experienced breast imagers at mammography? Radiology 224(3):871–880

    Google Scholar 

  4. Bocchi L, Coppini G, Nori J, Valli G (2004) Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. Med Eng Phys 26(4):303–312

    Article  Google Scholar 

  5. Buchbinder SS, Leichter IS, Lederman RB, Novak B, Bamberger PN, Coopersmith H, Fields SI (2002) Can the size of microcalcifications predict malignancy of clusters at mammography? Acad Radiol 9(1):18–25

    Article  Google Scholar 

  6. Cancer Facts and Figures (2004) American Cancer Society. URL: http://www.cancer.org/downloads/STT/CAFF_finalPWSecured.pdf

  7. Chan HP, Lo S-C, Sahiner B, Lam KL, Helvie MA (1995) Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Med Phys 22:1555–1567

    Article  Google Scholar 

  8. Chang YH, Zheng B, Good WF, Gur D (1998) Identification of clustered microcalcifications on digitized mammograms using morphology and topography-based computer-aided detection schemes. A preliminary experiment. Invest Radiol 33(10):746–751

    Article  Google Scholar 

  9. Cooley T, Micheli-Tzanakou E (1998) Classification of mammograms using an intelligent computer system. J Intell Syst 8(1/2):1–54

    Google Scholar 

  10. Dengler J, Behrens J, Desaga JF (1993) Segmentation of microcalcifications in mammograms. IEEE Trans Med Imaging 12:634–642

    Article  Google Scholar 

  11. Doi K, Giger ML, Nishikawa RM, Schmidt RA (1997) Computer-aided diagnosis of breast cancer on mammograms. Breast Cancer 4(4):228–233

    Google Scholar 

  12. Elmore JG, Armstrong K, Lehman CD, Fletcher SW (2005) Screening for breast cancer. JAMA 293(10):1245–1256

    Article  Google Scholar 

  13. Elmore JG, Nakano CY, Koepsell TD, Desnick LM, D’Orsi CJ, Ransohoff DF (2003) International variation in screening mammography interpretations in community-based programs. J Natl Cancer Inst 95(18):1384–1393

    Google Scholar 

  14. Esserman L, Cowley H, Eberle C, Kirkpatrick A, Chang S, Berbaum K, Gale A (2002) Improving the accuracy of mammography: volume and outcome relationships. J Natl Cancer Inst 94(5):369–375

    Google Scholar 

  15. Fondrinier E, Lorimier G, Guerin-Boblet V, Bertrand AF, Mayras C, Dauver N (2002) Breast microcalcifications: multivariate analysis of radiologic and clinical factors for carcinoma. World J Surg 26(3):290–296

    Article  Google Scholar 

  16. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886

    Google Scholar 

  17. Gail MH, Costantino JP (2001) Validating and improving models for projecting the absolute risk of breast cancer. J Natl Cancer Inst 93(5):334–335

    Article  Google Scholar 

  18. Gavrielides MA, Lo JY, Floyd CE Jr (2002) Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. Med Phys 29(4):475–483

    Article  Google Scholar 

  19. Gulsun M, Demirkazik FB, Ariyurek M (2003) Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and Le Gal’s classification. Eur J Radiol 47(3):227–231

    Article  Google Scholar 

  20. Gurcan MN, Sahiner B, Chan HP, Hadjiiski L, Petrick N (2001) Selection of an optimal neural network architecture for computer-aided detection of microcalcifications–comparison of automated optimization techniques. Med Phys 28(9):1937–1948

    Article  Google Scholar 

  21. Gurcan MN, Chan HP, Sahiner B, Hadjiiski L, Petrick N, Helvie MA (2002) Optimal neural network architecture selection: improvement in computerized detection of microcalcifications, Acad Radiol 9(4):420–429

    Article  Google Scholar 

  22. Ho WT, Lam PWT (2003) Clinical performance of computer assisted detection (CAD) system in detecting carcinoma in breasts of different densities. Clin Radiol 58:133–136

    Article  Google Scholar 

  23. Karssemeijer N (1993) Adaptive noise equalization and recognition of microcalcification clusters in mammograms. Int J Patt Rec Im Anal 7:1357–1375

    Article  Google Scholar 

  24. Lado M, Tahoces PG, Mendez AJ, Souto M, Vidal JJ (2001) Evaluation of an automated wavelet-based system dedicated to the detection of clustered microcalcifications in digital mammograms. Med Inform Internet Med 26(3):149–163

    Article  Google Scholar 

  25. Lanyi M (1977) Differential diagnosis of microcalcifications, X-ray film analysis of 60 intraductal carcinoma, the triangle principle. Radiology 17(5):213–216

    Google Scholar 

  26. Lanyi M (1985) Microcalcifications in the breast—a blessing or a curse? A critical review. Diagn Imaging Clin Med 54(3–4):126–145

    Google Scholar 

  27. Le Gal M, Chavanne G, Pellier D (1984) Diagnostic value of clustered microcalcifications discovered by mammography (apropos of 227 cases with histological verification and without a palpable breast tumor). Bull Cancer 71(1):57–64

    Google Scholar 

  28. Le Gal M, Durand JC, Laurent M, Pellier D (1976) Management following mammography revealing grouped microcalcifications without palpable tumor. Nouv Presse Med 5(26):1623–1627

    Google Scholar 

  29. Li H, Liu KJ, Lo SC (1997) Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans Med Imaging 16(6):785–798

    Article  Google Scholar 

  30. Lorenz H, Richter GM, Capaccioli M, Longo G (1993) Adaptive filtering in astronomical image processing. I. Basic considerations and examples. Astron Astrophys 277:321

    Google Scholar 

  31. Mary SS, Eric LR, Jessie QX et al (2005) Computer aided detection of amorphous calcifications. AJR 184:887–892

    Google Scholar 

  32. Mata Campos R, Vidal EM, Nava E, Martinez-Morillo M, Sendra F (2000) Detection of microcalcifications by means of multiscale methods and statistical techniques. J Digit Imaging 13(2 Suppl 1):221–225

    Article  Google Scholar 

  33. National Cancer Institute. Available at http://www.nci.nih.gov

  34. Roque AC, Andre TC (2002) Mammography and computerized decision systems: a review. Ann N Y Acad Sci 980:83–94

    Article  Google Scholar 

  35. Shah AJ, Wang J, Yamada T, Fajardo LL (2003) Digital mammography: a review of technical development and clinical applications. Clin Breast Cancer 4(1):63–70

    Article  Google Scholar 

  36. Shen L, Rangayyan RM, Desautels JEL (1994) Application of shape analysis to mammographic calcifications. IEEE Trans Med Imaging 13:263–274

    Article  Google Scholar 

  37. Spyrou G, Nikolaou M, Koufopoulos K, Ligomenides P (2002) A computer based model to assist in improving early diagnosis of breast cancer. In: Proceedings of the 7th world congress on advances in oncology and 5th international symposium on molecular medicine, 10–12 October 2002, Creta Maris Hotel, Hersonissos, Greece

  38. Spyrou G, Nikolaou M, Koussaris M, Tsibanis A, Vassilaros S, Ligomenides P (2002) A system for computer aided early diagnosis of breast cancer based on microcalcifications analysis. In: 5th European conference on systems science, 16–19 October 2002, Creta Maris Hotel, Greece

  39. Spyrou G, Pavlou P, Harissis A, Bellas I, Ligomenides P (1999) Detection of microcalcifications for early diagnosis of breast cancer. In: Proceedings of the 7th Hellenic conference on informatics, University of Ioannina Press, p V104, 26–28 August 1999, Greece

  40. Taylor CG, Champness J, Reddy M, Taylor P, Potts HWW, Given Wilson R (2003) Reproducibility of prompts in computer aided detection of breast cancer. Clin Radiol 58:733–738

    Article  Google Scholar 

  41. Timins JK (2005) Controversies in mammography. N J Med 102(1–2):45–49

    Google Scholar 

  42. Wright T, McGechan A (2003) Breast cancer: new technologies for risk assessment and diagnosis. Mol Diagn 7(1):49–55

    Article  Google Scholar 

  43. Wu Y, Doi K, Giger ML, Nishikawa RM (1992) Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. Med Phys 19:555–560

    Article  Google Scholar 

  44. Wu Y, Giger ML, Doi K, Schmidt RA, Metz CE (1993) Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 187:81–87

    Google Scholar 

  45. Yankaskas BC, Schell MJ, Bird RE, Desrochers DA (2001) Reassessment of breast cancers missed during routine screening mammography: a community-based study. AJR Am J Roentgenol 177(3):535–541

    Google Scholar 

  46. Zhang W, Yoshida H, Nishikawa RM, Doi K (1998) Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Med Phys 25(6):949–956

    Article  Google Scholar 

  47. Zhang W, Doi K, Giger ML, Wu Y, Nishikawa RM, Schmidt RA (1996) An improved shift-invariant artificial neural networks for computerized detection of clustered microcalcifications in digital mammograms. Med Phys 23:595–601

    Article  Google Scholar 

  48. Zhang W, Doi K, Giger ML, Wu Y, Nishikawa RM, Schmidt RA (1994) Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural networks. Med Phys 21:517–524

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Informatics, Academy of Athens, 4 Soranou Efesiou, 115 27, Athens, Greece

    George Spyrou, Smaragda Kapsimalakou, Antonis Frigas & Panos Ligomenides

  2. Foundation for Biomedical Research, Academy of Athens, 4 Soranou Efesiou, 115 27, Athens, Greece

    George Spyrou

  3. “Gynaikologiki Apeikonisi” Diagnostic Center, 106 Kifissias, 115 26, Athens, Greece

    Konstantinos Koufopoulos

  4. “Prolipsis” Diagnostic Breast Center, 3 Sevastias, 115 28, Athens, Greece

    Stamatios Vassilaros

Authors
  1. George Spyrou
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  2. Smaragda Kapsimalakou
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  3. Antonis Frigas
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  4. Konstantinos Koufopoulos
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  5. Stamatios Vassilaros
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  6. Panos Ligomenides
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Correspondence to George Spyrou.

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Spyrou, G., Kapsimalakou, S., Frigas, A. et al. “Hippocrates-mst”: a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer. Med Bio Eng Comput 44, 1007–1015 (2006). https://doi.org/10.1007/s11517-006-0117-2

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  • DOI: https://doi.org/10.1007/s11517-006-0117-2

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