title: Artificial neural network to predict skeletal metastasis in patients with prostate cancer.
abstract: 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.
authors.affiliation: Department of Nuclear Medicine, Buddhist Dalin Tzu Chi General Hospital, Chiayi, Taiwan.
authors.forename: Jainn-Shiun
authors.initials: JS
authors.lastname: Chiu
authors.forename: Yuh-Feng
authors.initials: YF
authors.lastname: Wang
authors.forename: Yu-Cheih
authors.initials: YC
authors.lastname: Su
authors.forename: Ling-Huei
authors.initials: LH
authors.lastname: Wei
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Liao
authors.forename: Yu-Chuan
authors.initials: YC
authors.lastname: Li
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001185:Artificial Intelligence; D001859:Bone Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017430:Prostate-Specific Antigen / Q000032:analysis; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D012372:ROC Curve; D011877:Radionuclide Imaging; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D051598:Whole Body Imaging / Q000379:methods
chemical_list: D017430:Prostate-Specific Antigen