Prostate Magnetic Resonance Imaging Provides Limited Incremental Value Over the Memorial Sloan Kettering Cancer Center Preradical Prostatectomy Nomogram

John K. Weaver, Eric H. Kim, Joel M. Vetter, Anup Shetty, Robert L. Grubb, Seth A. Strope, Gerald L. Andriole

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Objective: To examine the incremental value of prostate magnetic resonance imaging (MRI) when used in combination with the currently available preoperative risk stratification tool, the Memorial Sloan Kettering Cancer Center (MSKCC) preradical prostatectomy nomogram. Materials and Methods: We reviewed our institutional database of prostate MRI performed before radical prostatectomy between December 2014 and March 2016 (n = 236). We generated a logistic regression model based on observed final pathology results and the MSKCC nomogram predictions for organ-confined disease, extracapsular extension (ECE), seminal vesicle invasion, and lymph node involvement (LNI) (“MSKCC only”). We then generated a combined regression model incorporating both the MSKCC nomogram prediction with the degree of prostate MRI suspicion (“MSKCC + MRI”). Receiver operating characteristic curves were generated, and the area under the curves (AUCs) were compared. Results: When independently examining the MSKCC nomogram predicted risk and the degree of prostate MRI suspicion, MRI was a predictor for ECE (odds ratio 2.8, P <.01) and LNI (odds ratio 5.6, P =.01). When examining the “MSKCC + MRI” and “MSKCC only” models, the incremental benefit in risk discrimination from the combined model (“MSKCC + MRI”) was not significant for organ-confined disease, ECE, seminal vesicle invasion, or LNI (ΔAUC +0.03, P =.10; ΔAUC +0.03, P =.08; ΔAUC 0.63, P =.63; ΔAUC +0.04, P =.42; respectively). Conclusion: A combined model with prostate MRI and the MSKCC nomogram provides no additional risk discrimination over the MSKCC nomogram-based model alone. Evaluation of prostate MRI as a predictive tool should be performed in combination with, not independent of, these clinical risk stratification models.

Original languageEnglish
Pages (from-to)119-128
Number of pages10
JournalUrology
Volume113
DOIs
StatePublished - Mar 2018

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