A magnetic resonance imaging-based nomogram for predicting clinically significant prostate cancer at radical prostatectomy

Daniele Castellani; Sara Cecchini; Roberta Mazzucchelli; Luca Soraci; Mirko Di Rosa; Paolo Fabbietti; Erika Palagonia; Francesca Puccio; Francesca Carnevali; Enrico Paci; Rodolfo Montironi; Andrea Benedetto Galosi

doi:10.1016/j.urolonc.2022.04.011

A magnetic resonance imaging-based nomogram for predicting clinically significant prostate cancer at radical prostatectomy

Urol Oncol. 2022 Aug;40(8):379.e1-379.e8. doi: 10.1016/j.urolonc.2022.04.011. Epub 2022 May 31.

Affiliations

¹ Unit of Urology, Polytechnic University of the Marche Region, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Ancona, Italy. Electronic address: castellanidaniele@gmail.com.
² Unit of Radiology, IRCSS INRCA, Ancona, Italy.
³ Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Ancona, Italy.
⁴ Unit of Geriatric Medicine, IRCSS INRCA, Cosenza, Italy.
⁵ Unit of Geriatric Pharmacoepidemiology and Biostatistics, IRCCS INRCA, Ancona, Italy.
⁶ Unit of Urology, Polytechnic University of the Marche Region, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Ancona, Italy.
⁷ Unit of Pathology and Cytopathology, Azienda Ospedaliero-Universitaria Federico II, Napoli, Italy.

PMID: 35662497
DOI: 10.1016/j.urolonc.2022.04.011

Abstract

Purpose: To develop a nomogram incorporating clinical and multiparametric magnetic resonance imaging (mpMRI) parameters for the detection of clinically significant prostate cancer (csCaP) at radical prostatectomy (RP).

Materials and methods: We retrospectively analyzed all consecutive patients who underwent robotic RP between 2016 and 2020. All patients underwent a 1.5-T mp-MRI according to the PI-RADS-v2 scoring system. RP specimens were examined with the whole-mount technique. csCaP definition: any tumor with a volume larger than 0.5 cm³ or with a Gleason score ≥7. Univariable logistic regression models explored the association between clinical and imaging data and the risk of csCaP. Significant variables (P < 0.05) were selected into multivariable regression models to identify independent predictors. A nomogram was designed to select the significant relevant predictors. The nomogram was internally validated in terms of discrimination and calibration. Receiver operating characteristics of the area under the curve was used to assess the discrimination ability of the nomogram. To assess the predictive performance of mpMRI, the accuracy of the mpMRI-based nomogram was compared with that excluding either PI-RADS score or mpMRI IL size.

Results: The analysis involved 393 patients. The median age was 65(9) years. The median prostate specific antigen was 5.81(3.76) ng/ml. 363 had csCaP. PI-RADS v2 score of 4-5, prostate specific antigen density of 0.15 or more, and mpMRI index lesion (IL) size were significantly associated with csCaP in the multivariable regression analyses. Based on these variables, a diagnostic model was developed. The full model yielded an area under the curve of 0.77 (95%CI:0.75-0.80) which was significantly better than those excluding mpMRI findings (P = 0.02) Decision curve analysis showed a slight but significant net benefit associated with the use of the mp-MRI based nomograms compared with those excluding either PI-RADS score (Delta net benefit 0.0278) or mpMRI maximum IL size (Delta net benefit 0.0111).

Conclusions: The nomogram constructed in this study can assist urologists in assessing an individual's risk of csCaP at RP.

Keywords: Multiparametric magnetic resonance imaging; Nomograms; Prostatic neoplasms.

MeSH terms

Aged
Humans
Magnetic Resonance Imaging / methods
Male
Nomograms
Prostate-Specific Antigen*
Prostatectomy
Prostatic Neoplasms* / diagnostic imaging
Prostatic Neoplasms* / pathology
Prostatic Neoplasms* / surgery
Retrospective Studies

Substances

Prostate-Specific Antigen