Abstract
Ovarian cancer is a major cause of deaths worldwide. As a result, women are not diagnosed until the cancer has advanced to later stages. Accurate prognosis is required to determine the suitable therapeutic decision. Since abnormalities of hemostasis and increased risk of thrombosis are observed in cancer patient, assay involving hemostatic parameters can be potential prognosis tool. Thus a biological brain-inspired Complementary Learning Fuzzy Neural Network (CLFNN) is proposed, to complement the hemostasis in ovarian cancer prognosis. Experimental results that demonstrate the confluence of hemostasis and CLFNN offers a promising prognosis tool. Apart from superior performance, CLFNN provides interpretable rules to facilitate validation and justification of the system. Besides, CLFNN can be used as a concept validation tool for ovarian cancer prognosis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
American Cancer Society: Cancer Facts & Figures 2006. American Cancer Society, Inc. (2006)
Seow, A., Koh, W.P., Chia, K.S., Shi, L., Lee, H.P.: Trends in cancer incidence in Singapore 1968-2002. Singapore Cancer Registry (2004)
Koh, S.C.L., Khalil, R., Lim, F.K., Ilancheran, A., Choolani, M.: The association between fibrinogen, von Willebrand Factor, Antithrombin III, and D-Dimer levels and survival outcome by 36 months from ovarian cancer. Clin. Appl. Thrombosis/Hemostasis 11(3), T1–T6 (2005)
Gadducci, A., Baicchi, U., Marrai, R., Ferdeghini, M., Bianchi, R., Facchini, V.: Preoperative evaluation of D-Dimer and CA 125 levels in differentiating benign from malignant ovarian masses. Gynecologic Oncology 60, 197–202 (1996)
Koh, S.C.L., Tham, K.F., Khalil, R., Oei, P.L., Lim, F.K., Roy, A.C., Prasad, R.N.V.: Hemostatic and fibrinolytic status in patients with ovarian cancer and benign ovarian cysts: could D-Dimer and antithrombin III levels be included as prognostic markers for survival outcome? Clin. Appl. Thrombosis/Hemostasis 7(2), 141–148 (2001)
Gadducci, A., Marrai, R., Baicchi, U., Ferdeghini, M., Fanucchi, A., Weiss, C., Genazzani, A.R.: Preoperative D-Dimer plasma assay is not a predictor of clinical outcome for patients with advanced ovarian cancer. Gynecologic Oncology 66, 85–88 (1997)
Tan, T.Z., Quek, C., Ng, G.S.: Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures. Neural Networks 18(5-6), 818–825 (2005)
Griffee, K., Dougher, M.J.: Contextual control of stimulus generalization and stimulus equivalence in hierarchical categorization. Journal of the Experimental Analysis of Behavior 78(3), 433–447 (2002)
Koh, S.C.L., Khalil, R., Choolani, M., Ilancheran, A.: Systemic prognostic haemostatic marker levels for disease outcome within five years from ovarian cancer. Journal of Thrombosis and Haemostasis 3(suppl. 1), 1314 (2005)
Gadducci, A., Marrai, R., Baicchi, U., Gagetti, O., Facchini, V., Genazzni, A.R.: Prothrombin fragment F1+2 and thrombin-antithrombin III complex (TAT) plasma levels in patients with gynecological cancer. Gynecologic Oncology 61, 215–217 (1996)
Mackman, N.: Role of tissue factor in hemostasis and thrombosis. Blood Cells, Molecules, and Diseases (in press, 2006)
Bagozzi, R.P., Bergami, M., Leone, L.: Hierarchical representation of motives in goal setting. Journal of Applied Psychology 88(5), 915–943 (2003)
Hanm, J., Kamber, N.: Data mining: concepts and techniques. Morgan Kanfmann Publishers, San Francisco (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, T.Z., Ng, G.S., Quek, C., Koh, S.C.L. (2006). Ovarian Cancer Prognosis by Hemostasis and Complementary Learning. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_17
Download citation
DOI: https://doi.org/10.1007/11893295_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)