Risk prediction models for biochemical recurrence ... - Semantic Scholar
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Risk prediction models for biochemical recurrence ... - Semantic Scholar
Jul 4, 2014 - patients will drop to zero within 2 months after LRP. ... Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical ..... All authors read and approved the final.
Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score Xin-Hai Hu1, Henning Cammann2, Hellmuth-A Meyer1, Klaus Jung1,3, Hong-Biao Lu1,4, Natalia Leva5, Ahmed Magheli1, Carsten Stephan1,3, Jonas Busch1 Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always
