Artificial Neural Network to Predict Skeletal Metastasis in Patients with Prostate Cancer

  • Jainn-Shiun Chiu
  • Yuh-Feng Wang
  • Yu-Cheih Su
  • Ling-Huei Wei
  • Jian-Guo Liao
  • Yu-Chuan Li
Original Paper


The application of an artificial neural network (ANN) in prediction of outcomes using clinical data is being increasingly used. The aim of this study was to assess whether an ANN model is a useful tool for predicting skeletal metastasis in patients with prostate cancer. Consecutive patients with prostate cancer who underwent the technetium-99m methylene diphosphate (Tc-99m MDP) whole body bone scintigraphies were retrospectively analyzed between 2001 and 2005. The predictors were the patient’s age and radioimmunometric serum PSA concentration. The outcome variable was dichotomous, either skeletal metastasis or non-skeletal metastasis, based on the results of Tc-99m MDP whole body bone scintigraphy. To assess the performance for classification model in clinical study, the discrimination and calibration of an ANN model was calculated. The enrolled subjects consisted of 111 consecutive male patients aged 72.41 ± 7.69 years with prostate cancer. Sixty-seven patients (60.4%) had skeletal metastasis based on the scintigraphic diagnosis. The final best architecture of neural network model was four-layered perceptrons. The area under the receiver-operating characteristics curve (0.88 ± 0.07) revealed excellent discriminatory power (p < 0.001) with the best simultaneous sensitivity (87.5%) and specificity (83.3%). The Hosmer–Lemeshow statistic was 6.74 (p = 0.08 > 0.05), which represented a good-fit calibration. These results suggest that an ANN, which is based on limited clinical parameters, appears to be a promising method in forecasting of the skeletal metastasis in patients with prostate cancer.


Artificial intelligence Computer assisted Image interpretation Radionuclide imaging Prostatic neoplasm Bone metastasis 



This paper is in memorial of Mr. A-Tsai Lee, Dr. Jainn-Shiun Chiu’s maternal grandfather, who died from prostate cancer with skeletal metastasis on 20 October 2006.


  1. 1.
    Coleman, R. E., Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat. Rev. 27(3):165–176, 2001.CrossRefGoogle Scholar
  2. 2.
    Carlin, B. I., and Andriole, G. L., The natural history, skeletal complications, and management of bone metastases in patients with prostate carcinoma. Cancer. 88(12 Suppl):2989–2994, 2000.CrossRefGoogle Scholar
  3. 3.
    Rigaud, J., Tiguert, R., Le Normand, L., Karam, G., Glemain, P., Buzelin, J. M. et al., Prognostic value of bone scan in patients with metastatic prostate cancer treated initially with androgen deprivation therapy. J. Urol. 168(4 Pt 1):1423–1426, 2002.Google Scholar
  4. 4.
    Rodvold, D. M., McLeod, D. G., Brandt, J. M., Snow, P. B., and Murphy, G. P., Introduction to artificial neural networks for physicians: taking the lid off the black box. Prostate. 46(1):39–44, 2001.CrossRefGoogle Scholar
  5. 5.
    Forsstrom, J. J., and Dalton, K. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27(5):509–517, 1995.CrossRefGoogle Scholar
  6. 6.
    Wei, J. T., Zhang, Z., Barnhill, S. D., Madyastha, K. R., Zhang, H., and Oesterling, J. E., Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology. 52(2):161–172, 1998.CrossRefGoogle Scholar
  7. 7.
    Lisboa, P. J., A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 15(1):11–39, 2002.CrossRefGoogle Scholar
  8. 8.
    Anagnostou, T., Remzi, M., Lykourinas, M., and Djavan, B., Artificial neural networks for decision-making in urologic oncology. Eur. Urol. 43(6):596–603, 2003.Google Scholar
  9. 9.
    Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30(5):449–464, 1992.CrossRefGoogle Scholar
  10. 10.
    Penny, W., and Frost, D., Neural networks in clinical medicine. Med. Decis. Mak. 16(4):386–398, 1996.CrossRefGoogle Scholar
  11. 11.
    Henderson, A. R., The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data. Clin. Chim. Acta. 359(1–2):1–26, 2005.CrossRefGoogle Scholar
  12. 12.
    Das, A., Ben-Menachem, T., Cooper, G. S., Chak, A., Sivak, M. V. Jr., Gonet, J. A. et al., Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet. 362(9392):1261–1266, 2003.CrossRefGoogle Scholar
  13. 13.
    Banerjee, R., Das, A., Ghoshal, U. C., and Sinha, M., Predicting mortality in patients with cirrhosis of liver with application of neural network technology. J. Gastroenterol. Hepatol. 18(9):1054–1060, 2003.CrossRefGoogle Scholar
  14. 14.
    Wang, Y. F., Hu, T. M., Wu, C. C., Yu, F. C., Fu, C. M., Lin, S. H. et al., Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network. Comput. Methods Programs Biomed. 83(2):111–119, 2006.CrossRefGoogle Scholar
  15. 15.
    Guan, P., Huang, D. S., and Zhou, B. S., Forecasting model for the incidence of hepatitis A based on artificial neural network. World J. Gastroenterol. 10(24):3579–3582, 2004.Google Scholar
  16. 16.
    Dreiseitl, S., and Ohno-Machado, L., Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform. 35(5–6):352–359, 2002.CrossRefGoogle Scholar
  17. 17.
    Linden, A., Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J. Eval. Clin. Pract. 12(2):132–139, 2006.CrossRefMathSciNetGoogle Scholar
  18. 18.
    Hanley, J. A., and McNeil, B. J., A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 148(3):839–843, 1983.Google Scholar
  19. 19.
    Chatzicostas, C., Roussomoustakaki, M., Notas, G., Vlachonikolis, I. G., Samonakis, D., Romanos, J. et al., A comparison of Child-Pugh, APACHE II and APACHE III scoring systems in predicting hospital mortality of patients with liver cirrhosis. BMC Gastroenterol. 3:7, 2003.CrossRefGoogle Scholar
  20. 20.
    Lemeshow, S., and Hosmer, D. W. Jr., A review of goodness of fit statistics for use in the development of logistic regression models. Am. J. Epidemiol. 115(1):92–106, 1982.Google Scholar
  21. 21.
    Hosmer, D. W., Hosmer, T., Le Cessie, S., and Lemeshow, S., A comparison of goodness-of-fit tests for the logistic regression model. Stat. Med. 16(9):965–980, 1997.CrossRefGoogle Scholar
  22. 22.
    Chen, C. A., Lin, S. H., Hsu, Y. J., Li, Y. C., Wang, Y. F., and Chiu, J. S., Neural network modeling to stratify peritoneal membrane transporter in predialytic patients. Intern. Med. 45(9):663–664, 2006.CrossRefGoogle Scholar
  23. 23.
    Partin, A. W., Kattan, M. W., Subong, E. N., Walsh, P. C., Wojno, K. J., Oesterling, J. E. et al., Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. JAMA. 277(18):1445–1451, 1997.CrossRefGoogle Scholar
  24. 24.
    Kattan, M. W., Stapleton, A. M., Wheeler, T. M., and Scardino, P. T., Evaluation of a nomogram used to predict the pathologic stage of clinically localized prostate carcinoma. Cancer. 79(3):528–537, 1997.CrossRefGoogle Scholar
  25. 25.
    Murphy, G. P., Snow, P. B., Brandt, J., Elgamal, A., and Brawer, M. K., Evaluation of prostate cancer patients receiving multiple staging tests, including ProstaScint scintiscans. Prostate. 42(2):145–149, 2000.CrossRefGoogle Scholar
  26. 26.
    Batuello, J. T., Gamito, E. J., Crawford, E. D., Han, M., Partin, A. W., McLeod, D. G. et al., Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer. Urology. 57(3):481–485, 2001.CrossRefGoogle Scholar
  27. 27.
    Han, M., Snow, P. B., Brandt, J. M., and Partin, A. W., Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma. Cancer. 91(8 Suppl):1661–1666, 2001.CrossRefGoogle Scholar
  28. 28.
    Tewari, A., and Narayan, P., Novel staging tool for localized prostate cancer: a pilot study using genetic adaptive neural networks. J. Urol. 160(2):430–436, 1998.CrossRefGoogle Scholar
  29. 29.
    Crawford, E. D., Batuello, J. T., Snow, P., Gamito, E. J., McLeod, D. G., Partin, A. W. et al., The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma. Cancer. 88(9):2105–2109, 2000.CrossRefGoogle Scholar
  30. 30.
    Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L. et al., Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J. Am. Med. Inform. Assoc. 10(6):523–530, 2003.CrossRefGoogle Scholar
  31. 31.
    Carter, H. B., Epstein, J. I., and Partin, A. W., Influence of age and prostate-specific antigen on the chance of curable prostate cancer among men with nonpalpable disease. Urology. 53(1):126–130, 1999.CrossRefGoogle Scholar
  32. 32.
    Jung, K., Lein, M., Stephan, C., Von Hosslin, K., Semjonow, A., Sinha, P. et al., Comparison of 10 serum bone turnover markers in prostate carcinoma patients with bone metastatic spread: diagnostic and prognostic implications. Int. J. Cancer. 111(5):783–791, 2004.CrossRefGoogle Scholar
  33. 33.
    Stephan, C., Xu, C., Brown, D. A., Breit, S. N., Michael, A., Nakamura, T. et al., Three new serum markers for prostate cancer detection within a percent free PSA-based artificial neural network. Prostate. 66(6):651–659, 2006.CrossRefGoogle Scholar
  34. 34.
    Oates, J. C., Varghese, S., Bland, A. M., Taylor, T. P., Self, S. E., Stanislaus, R. et al., Prediction of urinary protein markers in lupus nephritis. Kidney Int. 68(6):2588–2592, 2005.CrossRefGoogle Scholar
  35. 35.
    Martich, G. D., Waldmann, C. S., and Imhoff, M., Clinical informatics in critical care. J. Intensive Care Med. 19(3):154–163, 2004.CrossRefGoogle Scholar
  36. 36.
    Yamamura, S., Takehira, R., Kawada, K., Nishizawa, K., Katayama, S., Hirano, M. et al., Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations. J. Clin. Pharm. Ther. 28(5):425–432, 2003.CrossRefGoogle Scholar
  37. 37.
    Boone, J. M., Gross, G. W., and Greco-Hunt, V., Neural networks in radiologic diagnosis. I. Introduction and illustration. Invest. Radiol. 25(9):1012–1016, 1990.CrossRefGoogle Scholar
  38. 38.
    O’Dowd, G. J., Veltri, R. W., Orozco, R., Miller, M. C., and Oesterling, J. E., Update on the appropriate staging evaluation for newly diagnosed prostate cancer. J. Urol. 158(3 Pt 1):687–698, 1997.Google Scholar
  39. 39.
    Hurwitz, G. A., Weingert, M. E., Silver, D. L., MacDonald, A. C., Finnie, K. J., Powe, J. E. et al., The usefulness of stress tests performed in the nuclear medicine department: mathematical methods to assess efficacy at various angiographic endpoints. Nucl. Med. Commun. 17(6):463–474, 1996.CrossRefGoogle Scholar
  40. 40.
    Hunter, A., Kennedy, L., Henry, J., and Ferguson, I., Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput. Methods Programs Biomed. 62(1):11–19, 2000.CrossRefGoogle Scholar
  41. 41.
    Heckerling, P. S., Gerber, B. S., Tape, T. G., and Wigton, R. S., Entering the black box of neural networks. Methods Inf. Med. 42(3):287–296, 2003.Google Scholar
  42. 42.
    Arana, E., Marti-Bonmati, L., Bautista, D., and Paredes, R., Qualitative diagnosis of calvarial metastasis by neural network and logistic regression. Acad. Radiol. 11(1):45–52, 2004.CrossRefGoogle Scholar
  43. 43.
    Fu, W. J., Carroll, R. J., and Wang, S., Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics. 21(9):1979–1986, 2005.CrossRefGoogle Scholar
  44. 44.
    Fujimoto, R., Higashi, T., Nakamoto, Y., Hara, T., Lyshchik, A., Ishizu, K. et al., Diagnostic accuracy of bone metastases detection in cancer patients: comparison between bone scintigraphy and whole-body FDG-PET. Ann. Nucl. Med. 20(6):399–408, 2006.CrossRefGoogle Scholar
  45. 45.
    Even-Sapir, E., Metser, U., Mishani, E., Lievshitz, G., Lerman, H., and Leibovitch, I., The detection of bone metastases in patients with high-risk prostate cancer: 99mTc-MDP Planar bone scintigraphy, single- and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET/CT. J. Nucl. Med. 47(2):287–297, 2006.Google Scholar
  46. 46.
    Cross, S. S., Harrison, R. F., and Kennedy, R. L., Introduction to neural networks. Lancet. 346(8982):1075–1079, 1995.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jainn-Shiun Chiu
    • 1
    • 4
  • Yuh-Feng Wang
    • 1
    • 4
  • Yu-Cheih Su
    • 2
    • 4
  • Ling-Huei Wei
    • 1
  • Jian-Guo Liao
    • 1
  • Yu-Chuan Li
    • 3
  1. 1.Department of Nuclear MedicineBuddhist Dalin Tzu Chi General HospitalChiayiTaiwan
  2. 2.Division of Hematology and Oncology, Department of Internal MedicineBuddhist Dalin Tzu Chi General HospitalChiayiTaiwan
  3. 3.Institute of Biomedical InformaticsNational Yang Ming UniversityTaipei CityTaiwan
  4. 4.Department of Medicine, College of MedicineTzu Chi UniversityHualienTaiwan

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