Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and Gleason score to predict positive surgical margins

Urology
Vassilis PoulakisEduard Becht

Abstract

To assess whether artificial neural network analysis (ANNA) predicts for positive surgical margins (PSMs) more effectively than logistic regression analysis (LRA) according to the combined use of the findings of pelvic coil magnetic resonance imaging (pMRI) and other preoperatively available tumor variables in patients with clinically organ-confined prostate cancer after radical prostatectomy. A total of 205 patients with clinically localized prostate cancer, who underwent retropubic radical prostatectomy were evaluated. The predictive variables included clinical TNM stage, prostate-specific antigen (PSA) level, PSA density, biopsy Gleason score, percentage of cancer in biopsy specimens, and pMRI findings. The predicted outcome was PSMs. The patient data were randomly split into four cross-validation sets and used to develop and validate the ANNA and LRA models. For comparison, the area under the receiver operating characteristic curve was used. The overall PSM rate was 22% (n = 45). Using all input parameters, the accuracy of the ANNA and LRA was 84% and 75% for the prediction of PSMs, respectively. The area under the receiver operating characteristic curve of the ANNA (0.872 +/- 0.014) was significantly greater statistically ...Continue Reading

References

Jan 1, 1994·Annual Review of Medicine·G L Andriole, W J Catalona
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Nov 1, 1996·The Urologic Clinics of North America·J I Epstein
Aug 2, 2000·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·L ChengD G Bostwick

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Citations

Aug 2, 2005·European Urology·Wolfgang DillenburgEduard Becht
Feb 13, 2013·Nature Reviews. Urology·Xinhai HuCarsten Stephan
Jul 24, 2021·BMC Medical Informatics and Decision Making·Hesham SalemAmir Awwad

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