Skip to main content
SpringerLink
Log in

An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

This article describes the fundamentals of an intelligent decision support system for the diagnosis of ovarian tumors. The system is designed to support diagnosis by less experienced gynecologists, and to gather data for continuous improvement of the quality of diagnosis. The theoretical basis for the construction of the system is the IF-sets framework, used to aggregate multiple decision-making methods, and simultaneously providing information about positive and negative diagnosis of a given tumor type.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ferlay, J., Steliarova-Foucher, E., Lortet-Tieulent, J., Rosso, S., Coebergh, J., Comber, H., Forman, D., Bray, F.: Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. European Journal of Cancer 49(6), 1374–1403 (2013)

    Article  Google Scholar 

  2. Szpurek, D., Moszyński, R., Szubert, S., Sajdak, S.: Urban and rural differences in characteristics of ovarian cancer patients. Annals of Agricultural and Environmental Medicine 20(2), 390–394 (2013)

    Google Scholar 

  3. du Bois, A., Rochon, J., Pfisterer, J., Hoskins, W.J.: Variations in institutional infrastructure, physician specialization and experience, and outcome in ovarian cancer: A systematic review. Gynecologic Oncology 112(2), 422–436 (2009)

    Article  Google Scholar 

  4. Timmerman, D., Schwärzler, P., Collins, W., Claerhout, F., Coenen, M., Amant, F., Vergote, I., Bourne, T.: Subjective assessment of adnexal masses with the use of ultrasonography: an analysis of interobserver variability and experience. Ultrasound in Obstetrics & Gynecology 13(1), 11–16 (1999)

    Article  Google Scholar 

  5. DePriest, P., Shenson, D., Fried, A., Hunter, J., Andrews, S., Gallion, H., Pavlik, E., Kryscio, R., et al.: A morphology index based on sonographic findings in ovarian cancer. Gynecologic Oncology 51(1), 7–11 (1993)

    Article  Google Scholar 

  6. Alcázar, J.L., Mercé, L.T., Laparte, C., Jurado, M., López-Garcia, G.: A new scoring system to differentiate benign from malignant adnexal masses. Obstetrical & Gynecological Survey 58(7), 462–463 (2003)

    Article  Google Scholar 

  7. Szpurek, D., Moszyński, R., Ziętkowiak, W., Spaczyński, M., Sajdak, S.: An ultrasonographic morphological index for prediction of ovarian tumor malignancy. European Journal of Gynaecological Oncology 26(1), 51–54 (2005)

    Google Scholar 

  8. Moszyński, R.: Evaluation of usefulness of rough set theory in diagnostics of ovarian tumors. Doctoral thesis at the Poznan University of Medical Sciences (2003)

    Google Scholar 

  9. Timmerman, D., Testa, A.C., Bourne, T., Ameye, L., Jurkovic, D., Van Holsbeke, C., Paladini, D., Van Calster, B., Vergote, I., Van Huffel, S., et al.: Simple ultrasound-based rules for the diagnosis of ovarian cancer. Ultrasound in Obstetrics & Gynecology 31(6), 681–690 (2008)

    Article  Google Scholar 

  10. Timmerman, D., Testa, A.C., Bourne, T., Ferrazzi, E., Ameye, L., Konstantinovic, M.L., Van Calster, B., Collins, W.P., Vergote, I., Van Huffel, S., et al.: Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. Journal of Clinical Oncology 23(34), 8794–8801 (2005)

    Article  Google Scholar 

  11. Moore, R.G., McMeekin, D.S., Brown, A.K., DiSilvestro, P., Miller, M.C., Allard, W.J., Gajewski, W., Kurman, R., Bast Jr., R.C., Skates, S.J.: A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecologic Oncology 112(1), 40–46 (2009)

    Article  Google Scholar 

  12. Tailor, A., Jurkovic, D., Bourne, T.H., Collins, W.P., Campbell, S.: Sonographic prediction of malignancy in adnexal masses using an artificial neural network. BJOG: An International Journal of Obstetrics & Gynaecology 106(1), 21–30 (1999)

    Article  Google Scholar 

  13. Smoleń, A., Czekierdowski, A., Stachowicz, N., Kotarski, J.: Use of multilayer perception artificial neutral networks for the prediction of the probability of malignancy in adnexal tumors. Ginekologia Polska 74(9), 855–862 (2003)

    Google Scholar 

  14. Van Calster, B., Timmerman, D., Lu, C., Suykens, J.A., Valentin, L., Van Holsbeke, C., Amant, F., Vergote, I., Van Huffel, S.: Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods. Ultrasound in Obstetrics & Gynecology 29(5), 496–504 (2007)

    Article  Google Scholar 

  15. Van Calster, B., Timmerman, D., Nabney, I.T., Valentin, L., Testa, A.C., Van Holsbeke, C., Vergote, I., Van Huffel, S.: Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery. Neural Computing and Applications 17(5-6), 489–500 (2008)

    Article  Google Scholar 

  16. Madu, E., Stalbovskaya, V., Hamadicharef, B., Ifeachor, E., Van Huffel, S., Timmerman, D.: Preoperative ovarian cancer diagnosis using neuro-fuzzy approach. In: European Conference on Emergent Aspects on Clinical Data Analysis (2005)

    Google Scholar 

  17. Tingulstad, S., Hagen, B., Skjeldestad, F.E., Onsrud, M., Kiserud, T., Halvorsen, T., Nustad, K.: Evaluation of a risk of malignancy index based on serum CA125, ultrasound findings and menopausal status in the pre-operative diagnosis of pelvic masses. BJOG: An International Journal of Obstetrics & Gynaecology 103(8), 826–831 (1996)

    Article  Google Scholar 

  18. Amor, F., Vaccaro, H., Alcázar, J.L., León, M., Craig, J.M., Martinez, J.: Gynecologic imaging reporting and data system a new proposal for classifying adnexal masses on the basis of sonographic findings. Journal of Ultrasound in Medicine 28(3), 285–291 (2009)

    Google Scholar 

  19. Timmerman, D., Bourne, T.H., Tailor, A., Collins, W.P., Verrelst, H., Vandenberghe, K., Vergote, I.: A comparison of methods for preoperative discrimination between malignant and benign adnexal masses: the development of a new logistic regression model. American Journal of Obstetrics and Gynecology 181(1), 57–65 (1999)

    Article  Google Scholar 

  20. Valentin, L., Hagen, B., Tingulstad, S., Eik-Nes, S.: Comparison of ’pattern recognition’ and logistic regression models for discrimination between benign and malignant pelvic masses: a prospective cross validation. Ultrasound in Obstetrics & Gynecology 18(4), 357–365 (2001)

    Article  Google Scholar 

  21. Van Holsbeke, C., Van Calster, B., Valentin, L., Testa, A.C., Ferrazzi, E., Dimou, I., Lu, C., Moerman, P., Van Huffel, S., Vergote, I., et al.: External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor Analysis Group. Clinical Cancer Research 13(15), 4440–4447 (2007)

    Article  Google Scholar 

  22. Wójtowicz, A., Żywica, P., Szarzyński, K., Moszyński, R., Szubert, S., Dyczkowski, K., Stachowiak, A., Szpurek, D., Wygralak, M.: Dealing with Uncertainty in Ovarian Tumor Diagnosis (to appear, 2014)

    Google Scholar 

  23. Stachowiak, A., Żywica, P., Dyczkowski, K., Wójtowicz, A.: An Interval-Valued Fuzzy Classifier Based on an Uncertainty-Aware Similarity Measure. In: Advances in Intelligent Systems and Computing (to appear, 2014)

    Google Scholar 

  24. Zadeh, L.A.: Fuzzy logic and approximate reasoning. Synthese 30(3-4), 407–428 (1975)

    Article  MATH  Google Scholar 

  25. Wygralak, M.: Intelligent Counting Under Information Imprecision. STUDFUZZ, vol. 292. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  26. Atanassov, K.T.: Intuitionistic fuzzy sets. STUDFUZZ, vol. 35. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  27. De, S.K., Biswas, R., Roy, A.R.: An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets and Systems 117(2), 209–213 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  28. Szmidt, E., Kacprzyk, J.: An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis. In: Abraham, A., Jain, L.C., Kacprzyk, J. (eds.) Recent Advances in Intelligent Paradigms and Applications. STUDFUZZ, vol. 113, pp. 57–70. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  29. Stachowiak, A., Dyczkowski, K.: A similarity measure with uncertainty for incompletely known fuzzy sets. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 390–394. IEEE (2013)

    Google Scholar 

  30. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational Intelligence: A Methodological Introduction. Springer (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614, Poznań, Poland

    Krzysztof Dyczkowski, Andrzej Wójtowicz, Patryk Żywica & Anna Stachowiak

  2. Division of Gynecological Surgery, Poznan University of Medical Sciences, Polna 33, 60-535, Poznań, Poland

    Rafał Moszyński & Sebastian Szubert

Authors
  1. Krzysztof Dyczkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Wójtowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patryk Żywica
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Stachowiak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rafał Moszyński
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sebastian Szubert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Dyczkowski .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Dyczkowski, K., Wójtowicz, A., Żywica, P., Stachowiak, A., Moszyński, R., Szubert, S. (2015). An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11310-4_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation