OBJECTIVE: To report a methodological feasibility study in a small series of patients with node-negative organ-confined prostatic cancer, using artificial neural networks to predict tumour progression after radical prostatectomy and thus help to identify high-risk patients who would benefit from adjuvant treatment. PATIENTS AND METHODS: A group of 20 patients with pT2N0 prostatic cancer and postoperative tumour progression was compared with a control group of 20 patients with no progression, matched for age, duration of follow-up and preoperative serum prostate-specific antigen level. Histopathological data were obtained from the radical prostatectomy specimens, i.e. the Gleason score, World Health Organisation (WHO) grade and maximum diameter of the tumour transects. The volume and surface area of the epithelial tumour component and of the lumina of the neoplastic glands per unit tissue volume were estimated by morphometric methods. To predict recurrence, multilayer feedforward networks with backpropagation (MLFF-BP), two implementations of learning vector quantization (LVQ), and linear discriminant analysis (LDA) were applied. The ability of these models to correctly classify new cases was tested using the 'leave-one-out' technique. RESULTS: Progression was predicted correctly in 85% of newly presented cases from the three routine histopathological variables alone. On the basis of the four morphometric variables alone progression was predicted correctly in 93% of cases. The use of all seven variables as input data only slightly improved the quality of prediction. The best results were obtained by the LVQ networks and LDA, followed by MLFF-BP networks. CONCLUSIONS: In this methodological feasibility study, the progression of pT2N0 prostatic cancer after radical prostatectomy could be predicted with good accuracy, sensitivity and specificity from routine variables or morphometric texture variables using artificial neural networks. These results suggest that this approach should be assessed in a prospective study with more cases.
OBJECTIVE: To report a methodological feasibility study in a small series of patients with node-negative organ-confined prostatic cancer, using artificial neural networks to predict tumour progression after radical prostatectomy and thus help to identify high-risk patients who would benefit from adjuvant treatment. PATIENTS AND METHODS: A group of 20 patients with pT2N0 prostatic cancer and postoperative tumour progression was compared with a control group of 20 patients with no progression, matched for age, duration of follow-up and preoperative serum prostate-specific antigen level. Histopathological data were obtained from the radical prostatectomy specimens, i.e. the Gleason score, World Health Organisation (WHO) grade and maximum diameter of the tumour transects. The volume and surface area of the epithelial tumour component and of the lumina of the neoplastic glands per unit tissue volume were estimated by morphometric methods. To predict recurrence, multilayer feedforward networks with backpropagation (MLFF-BP), two implementations of learning vector quantization (LVQ), and linear discriminant analysis (LDA) were applied. The ability of these models to correctly classify new cases was tested using the 'leave-one-out' technique. RESULTS: Progression was predicted correctly in 85% of newly presented cases from the three routine histopathological variables alone. On the basis of the four morphometric variables alone progression was predicted correctly in 93% of cases. The use of all seven variables as input data only slightly improved the quality of prediction. The best results were obtained by the LVQ networks and LDA, followed by MLFF-BP networks. CONCLUSIONS: In this methodological feasibility study, the progression of pT2N0 prostatic cancer after radical prostatectomy could be predicted with good accuracy, sensitivity and specificity from routine variables or morphometric texture variables using artificial neural networks. These results suggest that this approach should be assessed in a prospective study with more cases.