Designing a Clinical Decision Support System Based on Artificial Neural Network for Early Detection of Prostate Cancer and Differentiation from Benign Prostatic Hyperplasia *

Document Type : Original Article

Authors
Mustafa Ghaderzadeh 1
Farahnaz Sadoughi 2
Arvin Ketabat 1
1 Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran
2 Associate Professor, Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
Introduction: In recent years, the concepts of artificial neural networks (ANN) have extensively
undergone remarkable development in early detection and classification of diseases such as
benign prostatic hyperplasia (BPH). The usage of ANN has become widely accepted in medical
applications owing to its potential capabilities for detecting the complex interactions among
variables, diagnosis and diseases’ modeling. The present study aimed to design and implement a
decision support system (DSS) based on ANN for early detection of prostate cancer.
Methods: This survey design was conducted through data collection among 360 males with
prostate abnormalities in Urology Department of Imam Khomeini Hospital, Tehran, Iran, from
January 2008 to March 2011. In order to assess the performance and accuracy of the designed
system, sensitivity, specificity and receiver-operating characteristics (ROC) curve were used as
the indicators of distinguishing prostate cancers from BPH. In order to implement DSS in this
study, scaled conjugate gradient (SCG) algorithm was used as the main algorithm for early
detection of prostate cancer from benign prostate.
Results: The proposed intelligent ANN-based system can be used as a strong diagnostic tool with
97.0% specificity and 92.1% sensitivity for detecting the prostate cancer and to differentiate it
from BPH. The results indicated a high potential of artificial neural network as a strong tool in
classification of prostatic neoplasia diseases.
Conclusion: A medical decision support system was used aiming to help medical experts in their
classification and early detection of prostatic neoplasia disorders in the present study. Such
artificial intelligent-based medical intelligent systems, particularly for neural networks, can help
physicians in accurate decision-making concerning prostate cancer and BPH. Using such
systems, specialists would be able to eliminate or minimize unnecessary biopsy and reduce
diagnostic costs. In addition, such systems can accelerate the diagnostic detection time.

Keywords

  1. Yan H, Jiang Y, Zheng J, Peng C, Li Q. A multilayer perceptron-based medical decision support system for heart
    disease diagnosis. Expert Systems with Applications 2006; 30(2): 272-81.
    2. Shortliffe EH, Cimino JJ. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. New
    York, NY: Springer; 2006.
    3. Lisboa PJ, Taktak AF. The use of artificial neural networks in decision support in cancer: a systematic review.
    Neural Netw 2006; 19(4): 408-15.
    4. Smith AE, Nugent CD, McClean SI. Evaluation of inherent performance of intelligent medical decision support
    systems: utilising neural networks as an example. Artif Intell Med 2003; 27(1): 1-27.
    5. Monadjemi SA, Moallem P. Automatic Diagnosis of. Particular Diseases Using a Fuzzy-Neural Approach.
    International Review on Computers and Software (IRECOS) 2008; 3(4): 406-11.
    6. Ghumbre SU, Ghatol AA. An intelligent system for hepatitis b disease diagnosis. International Journal of
    Computers and Applications 2010; 32(4).
    7. Pandey B, Mishra RB. Knowledge and intelligent computing system in medicine. Computers in Biology and
    Medicine 2009; 39(3): 215-30.
    8. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. Neural Networks, IEEE
    Transactions onDate of Publication 1994; 5(6): 989-93.
    9. Han M, Snow PB, Brandt JM, Partin AW. Evaluation of artificial neural networks for the prediction of pathologic
    stage in prostate carcinoma. Cancer 2001; 91(8 Suppl): 1661-6.
    10. Haykin S. Neural Networks: A Comprehensive Foundation. New York, NY: Prentice-Hall; 1994.
    11. Baxt WG. Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute
    coronary occlusion. Neural Computation archive 1990; 2(4): 480-9.  12. Al-Shayea Q. Artificial Neural Networks in Medical Diagnosis. International Journal of Computer Science Issues
    2011; 8(2): 150-4.
    13. Sordo M. Introduction to neural networks in healthcare. Open Clinical: Knowledge Management for Medical
    Care, Harvard University [Online]. 2002; Available from: URL:
    www.openclinical.org/docs/int/neuralnetworks011.pdf/
    14. Ohlsson M. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.
    Artif Intell Med 2004; 30(1): 49-60.
    15. Stafford RG, Mickewich DJ, Beutel J. Application of neural networks as an aid in medical diagnosis and general
    anomaly detection. Expert Systems with Applications 1995; 9(2): VII-VII.
    16. Ottowitz WE, Dougherty DD, Savage CR. The neural network basis for abnormalities of attention and executive
    function in major depressive disorder: implications for application of the medical disease model to psychiatric
    disorders. Harv Rev Psychiatry. 2002; 10(2): 86-99.
    17. Glotsos D, Kalatzis I, Theocharakis P, Georgiadis P, Daskalakis A, Ninos K, et al. A multi-classifier system for
    the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound
    images. Comput Methods Programs Biomed 2010; 97(1): 53-61.
    18. Anagnostou T, Remzi M, Lykourinas M, Djavan B. Artificial neural networks for decision-making in urologic
    oncology. Eur Urol 2003; 43(6): 596-603.
    19. Botoca C, Bardan R, Botoca M, Alexa F. Prostate cancer prognosis evaluation assisted by neural networks.
    WSEAS Transactions on Computers archive 2010; 9(2), 164-73.
    20. Cookson MS, Aus G, Burnett AL, Canby-Hagino ED, D'Amico AV, Dmochowski RR, et al. Variation in the
    definition of biochemical recurrence in patients treated for localized prostate cancer: the American Urological
    Association Prostate Guidelines for Localized Prostate Cancer Update Panel report and recommendations for a
    standard in the reporting of surgical outcomes. J Urol 2007; 177(2): 540-5.
    21. Parkin DM, Muir CS. Cancer Incidence in Five Continents. Comparability and quality of data. IARC Sci Publ
    1992; (120): 45-173.
    22. Tanagho E, McAninch J. Smith's General Urology. New York, NY: McGraw-Hill Companies, Incorporated; 2007.
    23. Cinar M, Engin M, Engin EZ, Atesci YZ. Early prostate cancer diagnosis by using artificial neural networks and
    support vector machines. Expert Systems with Applications 2009; 36(3): 6357-61.
    24. Saritas I, Ozkan IA, Sert IU. Prognosis of prostate cancer by artificial neural networks. Expert Systems with
    Applications 2010; 37(9): 6646-50.
    25. Zhu Y, Williams S, Zwiggelaar R. Computer technology in detection and staging of prostate carcinoma: a review.
    Med Image Anal 2006; 10(2): 178-99.
    26. Kuriyama M. Prostate-specific antigen as a tumor marker in prostate cancer. International Journal of Urology
    1994; 1(2): 99-113.
    27. Remzi M, Djavan B. Artificial neural networks in Urology 2004. Eur Urol Supplements 2004; 3: 33-8.
    28. Djavan B, Remzi M, Zlotta A, Seitz C, Snow P, Marberger M. Novel artificial neural network for early detection
    of prostate cancer. J Clin Oncol. 2002; 20(4): 921-9.
    29. Llobet R, Perez-Cortes JC, Toselli AH, Juan A. Computer-aided detection of prostate cancer. Int J Med Inform.
    2007; 76(7): 547-56.    30. Snow PB, Smith DS, Catalona WJ. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a
    pilot study. J Urol. 1994; 152(5 Pt 2): 1923-6.
    31. Bishop CM. Neural Networks for Pattern Recognition. London, UK: Oxford University Press; 1995
    32. Demuth HB, Beale MH. MATLAB: Neural Network Toolbox. Natick, MA: Math Works, Incorporated; 1994.
    33.Mooller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993; 6(4):
    525-33.
    34. Bose NK, Liang P. Neural network fundamentals with graphs, algorithms, and applications. New York, NY:
    McGraw-Hill; 1996.
    35. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic
    analysis for diagnostic tests. Prev Vet Med. 2000; 45(1-2): 23-41.
Health Information Management
Volume 9, Issue 4
September and October 2012
Pages 457-464