MRI-based nomogram predicting the probability of diagnosing a clinically significant Prostate Cancer with MRI-US fusion biopsy

Giuseppe Simone1, Rocco Papalia2, Emanuela Altobelli2, Alessandro Giacobbe3, Luigi Benecchi4, Gabriele Tuderti1, Leonardo Misuraca1, Francesco Minisola1, Salvatore Guaglianone1, Devis Collura3, Giovanni Muto2, Michele Gallucci1, Mariaconsiglia Ferriero1
  • 1 Istituto Nazionale Tumori "Regina Elena", Unità di Urologia (Roma)
  • 2 Università Campus Biomedico, Dipartimento di Urologia (Roma)
  • 3 Ospedale "San Giovanni Bosco”, Unità di Urologia (Torino)
  • 4 Ospedale di Cremona, Unità di Urologia (Cremona)


Identifying clinically significant prostate cancers is the main objective of prostate cancer diagnosis. The aim of this study was to develop, to internally validate and to calibrate a nomogram to predict the probability of detecting a clinically significant prostate cancer.

Materials and Methods

Prospectively collected data from 3 tertiary referral center series of 478 consecutive patients who underwent MRI-US fusion biopsy using the UroStation™ (Koelis, France) were used to build the nomogram. A logistic regression model is created to identify predictors of PCa diagnosis with MRI-US fusion biopsy. Predictive accuracy was quantified using the concordance index (CI). Internal validation with 200 bootstrap resampling and calibration plot were performed.


Mean age was 66.3 yrs (± 7.98) and mean PSA levels were 9.8 ng/mL (± 7.98). The overall PCa detection rate was 57.4%.
Age, PSA serum levels, PI-RADS score at MRI report, number of targeted and number of systematic cores taken were included in the model (Figure 1).Predictive accuracy was 0.81. On internal validation the CI was 0.81 and predicted probability was well calibrated (Figure 2).
Limitations include the lack of external validation and the absence of patients with races different by Caucasian in the development cohort.


Predicting the risk of a clinically significant PCa is the goal of physicians. This nomogram provides a high accuracy in predicting the probability of diagnosing a clinically significant PCa with MRI-US fusion biopsy. The ease to use makes this nomogram a clinical tool for both patients and physicians.


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