Grading breast cancer malignancy with neural networks

• Autorzy: Jeleń Ł. , Lipiński A. , Detyna J. , Jeleń M.
• Języki publikacji: EN

Abstrakty

EN

Breast cancer is one of the most often diagnosed cancers among middle-aged women. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for malignancy grading, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy based on the Bloom – Richardson grading scheme. This is a well known grading scheme among the pathologist used during the diagnosis process. To achieve such a classification we extracted 16 features that were then used to classify the malignancy into two classes. Each class represents the malignancy of the cancer according to Bloom – Richardson grading scheme. According to that scheme two types of features are considered, where each type is extracted from images recorded at two different magnifications. Three structural features were calculated from low magnification images and thirteen polymorphic features were derived from high magnification images. To classify the malignancy grades, the multilayer perceptron was used. The described system was able to classify the malignancy with the error rate of 13.5%. In this paper we also present first clinical trials that allow for the verification of the obtained classification rate. The clinical trial showed that the depicted system has a high performance achieving an accuracy of 93.08% .

Słowa kluczowe

EN

Bloom-Richardson; malignancy grading; malignancy classification; cancer classification

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2011
• Tom: Vol. 7, no. 2
• Strony: 47--53
• Opis fizyczny: Bibliogr. 16 poz., zdj., tab.

Twórcy

autor

Jeleń Ł.

• Institute of Materials Science and Applied Mechanics Wrocław University of Technology Smoluchowskiego 25, 50-370 Wrocław, Poland

autor

Lipiński A.

• Zakład Patomorfologii i Cytologii Klinicznej Akademia Medyczna we Wrocławiu Borowska 213, 50-556 Wrocław, Poland

autor

Detyna J.

• Institute of Materials Science and Applied Mechanics Wrocław University of Technology Smoluchowskiego 25, 50-370 Wrocław, Poland

autor

Jeleń M.

• Zakład Patomorfologii i Cytologii Klinicznej Akademia Medyczna we Wrocławiu Borowska 213, 50-556 Wrocław, Poland

Bibliografia

• 1. Bloom H.J.G., Richardson W.W:. Histological grading and prognosis in breast cancer. British Journal of Cancer 1957, 11: 359–377.
• 2. Le Doussal V., Tubiana-Hulin, M., Friedman S., et al..: Prognostic value of histologic grade nuclear components of Scarff–Bloom–Richardson (sbr). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas. Cancer 1989, 64(9) 1914–1921.
• 3. Street W.N.: Xcyt: a system for remote cytological diagnosis and prognosis of breast cancer. In L.C. Jain, editor, Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, World Scientific Publishing, Singapore, 2000, pp 297–322.
• 4. Street W.N., Wolberg W.H., Mangasarian O.L.: Nuclear feature extraction for breast tumor diagnosis. In Imaging Science and Technology/Society of Photographic Instrumentation Engineers 1993 International Symposium on Electronic Imaging: Science and Technology, IS&T/SPIE, Vol. 1905, San Jose, California, 1993, pp. 861–870.
• 5. Nezafat R., Tabesh A., Akhavan S., Lucas C., Zia M.A:. Feature selection and classification for diagnosing breast cancer. Proceedings of International Association of Science and Technology for Development International Conference, IASTED, Cancun, Mexico 1998, pp. 310–313.
• 6. Walker H.L., Jr., Albertelli L.E.: Breast cancer screening using evolved neural networks. IEEE International Conference on Systems, Man, and Cybernetics, San Diego 1998, USA, 2: 1619–1624.
• 7. Cheng H.D., Li X.Q., Riodan D., Scrimger J.N.: A Parallel Approach to Tubule Grading in Breast Cancer Lesions and its VLSI Implementation. Computer-Based Medical Systems: Fourth Annual IEEE Symposium 1991, pp. 322–329.
• 8. Schnorrenberg F., Tsapatsoulis N., Pattichis C.S., Schizas C.N., Kollias S., Vassiliou M., Adamou A., Kyriacou K.: A modular neural network system for the analysis of nuclei in histopathological sections. IEEE Engineering in Medicine and Biology Magazine 2000, 19: 48–63.
• 9. Jelen L., Fevens T., Krzyzak A.. Classification of Breast Cancer Malignancy using Cytological Images of Fine Needle Aspiration Biopsies. Int. J. Appl. Math. Comput. Sci., 2008, Vol. 18, No. 1: 75–83.
• 10. Ridler T., Calvard, S.: Picture thresholding using an iterative selection. IEEE Trans. System, Man and Cybernetics 1978, 8: 630–632.
• 11. Klir G.J., Yuan B.: Fuzzy sets and fuzzy logic: Theory and applications. Prentice Hall 1995, New Jersey.
• 12. Theera-Umpon N.: Patch–Based white blood cell nucleus segmentation using fuzzy clustering. ECTI Transactions on electrical eng., electronics and communications 2005 3(1): 15–19.
• 13. Bezdek J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York, 1981.
• 14. Jelen L.: Computerized cancer malignancy grading of fine needle aspirates. PhD thesis, Concordia University, 2009.
• 15. Rangayyan R.M.: Biomedical Image Analysis (Biomedical Engineering). CRC Press, Boca Raton, FL 2004.
• 16. Lapedes A., Farber R.: Nonlinear signal processing using neural networks: prediction, and system modelling. echnical Report, LA–UR–87–2662, 1987.