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

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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∈∈0.05), 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.

Original languageEnglish
Pages (from-to)91-100
Number of pages10
JournalJournal of Medical Systems
Issue number2
Publication statusPublished - Apr 2009
Externally publishedYes


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

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Health Information Management
  • Information Systems


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