Preoperative neural network using combined magnetic resonance imaging variables, prostate specific antigen, and Gleason score to predict prostate cancer recurrence after radical prostatectomy.
OBJECTIVE
An artificial neural network analysis (ANNA) was developed to predict the biochemical recurrence more effectively than regression models based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate specific antigen (PSA) and biopsy Gleason score in patients with clinically organ-confined prostate cancer after radical prostatectomy (RP).


METHODS
Two-hundred-and-ten patients undergoing retropubic RP with pelvic lymphadenectomy were evaluated. Predictive study variables included clinical TNM classification, preoperative serum PSA, biopsy Gleason score, transrectal ultrasound (TRUS) findings, and pMRI findings. The predicted result was a biochemical failure (PSA >or=0.1 ng/ml). Using a five-way cross-validation method, the predicted ability of ANNA for a validation set of 200 randomly selected patients was compared with those of Cox regression analysis and "Kattan nomogram" by area under the receiver operating characteristic curve (AUC) analysis.


RESULTS
Seventy-three patients (35%) failed at median follow-up of 61 (mean: 60, range: 2-94) months. Using similar input variables, the AUC of ANNA (0.765, 95% Confidence Interval [CI]: 0.704-0.825) was comparable (p > 0.05) to those for Cox regression (0.738, 95%CI: 0.691-0.819) and Kattan nomogram (0.728, 95%CI: 0.644-0.819). Contrarily, adding the pMRI findings, the ANNA is significantly (p < 0.05) superior to any other predictive model (0.897, 95%CI: 0.841-0.977). The Gleason score represented the most influential predictor (relative weight: 2.4) of PSA recurrence, followed by pMRI (2.2), and PSA (2.0).


CONCLUSIONS
ANNA is superior to regression models to predict accurately biochemical recurrence. The relative importance of pMRI and the utility of ANNA to predict the PSA failure in patients referred for RP must be confirmed in further trials.
Poulakis
Vassilis
V
Department of Urology and Pediatric Urology, Krankenhaus Nordwest, Teaching Hospital of the Johann-Wolfgang-Goethe University Frankfurt, Steinbacher Hohl 2-26, D-60488 Frankfurt am Main, Germany. Poulakis@em.uni-frankfurt.de
Witzsch
Ulrich
U
de Vries
Rachelle
R
Emmerlich
Volker
V
Meves
Michael
M
Altmannsberger
Hans-Michael
HM
Becht
Eduard
E
D014408:Biomarkers, Tumor / Q000097:blood*; D001706:Biopsy; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008197:Lymph Node Excision; D008207:Lymphatic Metastasis / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008297:Male; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011467:Prostate / Q000473:pathology*; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery
D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen