BACKGROUND: Quantitative biopsy pathology with prostate specific antigen significantly improves the prediction of pathologic stage in patients with clinically localized prostate carcinoma (PCa). The authors recently reported a computational model for predicting patient specific likelihood of organ confinement of PCa using biopsy pathology and clinical data. The current study validates the initial models and presents an new, improved tool for clinical decision making. METHODS: The authors assessed 10 biopsy pathologic parameters and 2 clinical parameters using data from two institutions. Of 1287 patients, 798 men had pathologically organ confined (OC) PCa, 282 men had nonorgan-confined disease with capsular penetration (NOC-CP) only, and 207 men showed seminal vesicle or lymph node invasion (NOC-AD) after undergoing pelvic lymphadenectomy and radical prostatectomy. Patient input data were evaluated by ordinal logistic (OLOGIT) and neural network (NN) models; and the likelihood of developing OC, NOC-CP, or NOC-AD disease was calculated for the combined and separate data sets and was compared with the results from original presentation. In addition, a new two-output model was constructed (OC/NOC-CP vs. NOC-AD). RESULTS: The three-output OLOGIT and NN models predicted OC disease with 95.0% and 98.6% accuracy, respectively, for the combined data set and with 93.0% and 98.6% accuracy, respectively, on subset analysis. The combined accuracy for predicting OC, NOC-CP, and NOC-AD disease in the entire validation set was 66.7% for OLOGIT model and 66.0% for the NN model. The two-output OLOGIT and NN models correctly predicted 94.9% and 100.0% of all OC/NOC-CP disease, respectively. CONCLUSIONS: Both computation models predicted OC PCa with an accuracy of 93.0-98.6% when they were validated with two different data sets. The OLOGIT and NN-based, two-output model permitted an appropriate treatment decision for 85.2-90.2% of patients. These data support the use of quantitative pathology and clinical data-based decision modeling to manage patients with clinically localized PCa.
BACKGROUND: Quantitative biopsy pathology with prostate specific antigen significantly improves the prediction of pathologic stage in patients with clinically localized prostate carcinoma (PCa). The authors recently reported a computational model for predicting patient specific likelihood of organ confinement of PCa using biopsy pathology and clinical data. The current study validates the initial models and presents an new, improved tool for clinical decision making. METHODS: The authors assessed 10 biopsy pathologic parameters and 2 clinical parameters using data from two institutions. Of 1287 patients, 798 men had pathologically organ confined (OC) PCa, 282 men had nonorgan-confined disease with capsular penetration (NOC-CP) only, and 207 men showed seminal vesicle or lymph node invasion (NOC-AD) after undergoing pelvic lymphadenectomy and radical prostatectomy. Patient input data were evaluated by ordinal logistic (OLOGIT) and neural network (NN) models; and the likelihood of developing OC, NOC-CP, or NOC-AD disease was calculated for the combined and separate data sets and was compared with the results from original presentation. In addition, a new two-output model was constructed (OC/NOC-CP vs. NOC-AD). RESULTS: The three-output OLOGIT and NN models predicted OC disease with 95.0% and 98.6% accuracy, respectively, for the combined data set and with 93.0% and 98.6% accuracy, respectively, on subset analysis. The combined accuracy for predicting OC, NOC-CP, and NOC-AD disease in the entire validation set was 66.7% for OLOGIT model and 66.0% for the NN model. The two-output OLOGIT and NN models correctly predicted 94.9% and 100.0% of all OC/NOC-CP disease, respectively. CONCLUSIONS: Both computation models predicted OC PCa with an accuracy of 93.0-98.6% when they were validated with two different data sets. The OLOGIT and NN-based, two-output model permitted an appropriate treatment decision for 85.2-90.2% of patients. These data support the use of quantitative pathology and clinical data-based decision modeling to manage patients with clinically localized PCa.