Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: A comparison with logistic regression analysis

Pierfrancesco Bassi, Emilio Sacco, Vincenzo De Marco, Maurizio Aragona, Andrea Volpe

Risultato della ricerca: Contributo in rivistaArticolo in rivista

31 Citazioni (Scopus)

Abstract

OBJECTIVE: To compare the prognostic performance of an artificial neural network (ANN) with that of standard logistic regression (LR), in patients undergoing radical cystectomy for bladder cancer. PATIENTS AND METHODS: From February 1982 to February 1994, 369 evaluable patients with non-metastatic bladder cancer had pelvic lymph node dissection and radical cystectomy for either stage Ta-T1 (any grade) tumour not responding to intravesical therapy, with or with no carcinoma in situ, or stage T2-T4 tumour. LR analysis based on 12 variables was used to identify predictors of overall 5-year survival, and the ANN model was developed to predict the same outcome. The LR analysis, based on statistically significant predictors, and the ANN model were the compared for their accuracy in predicting survival. RESULTS: The median age of the patients was 63 years, and overall 201 of them died. The tumour stage and nodal involvement (both P < 0.001) were the only statistically independent predictors of overall 5-year survival on LR analysis. Based on these variables, LR had a sensitivity and specificity for predicting survival of 68.4% and 82.8%, respectively; corresponding values for the ANN were 62.7% and 86.1%. For LR and ANN, the positive predictive values were 78.6% and 76.2%, and the negative predictive values were 73.9% and 76.5%, respectively. The index of diagnostic accuracy was 75.9% for LR and 76.4% for ANN. CONCLUSIONS: The ANN accurately predicted the survival of patients undergoing radical cystectomy for bladder cancer and had a prognostic performance comparable with that of LR. As ANNs are based on easy-to-use software that can identify nonlinear interactions between variables, they might become the preferred tool for predicting outcome. © 2007 The Authors.
Lingua originaleEnglish
pagine (da-a)1007-1012
Numero di pagine6
RivistaBJU International
Volume2007
DOI
Stato di pubblicazionePubblicato - 2007

Keywords

  • Bladder cancer
  • Prognosis
  • Neural network
  • Logistic regression

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