Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study.

BACKGROUND

Accurately identifying aggressive prostate tumors and studying them as a separate outcome are urgently needed. Nomogram is a predictive tool using an algorithm, it has been widely applied in clinical practice to predict prognosis. We aimed to develop and internally validate a nomogram predicting clinically significant prostate cancer (csPCa).

METHODS

Data were retrieved from the records of the two main hospitals in Riyadh, during the period 2019-2022. Significant variables associated with csPCa cases were used to develop and internally validate a novel nomogram, utilizing the C index, and calibration curves. Decision curve analysis (DCA) was used to assess its clinical utility.

RESULTS

Prostate imaging reporting and data system (PI-RADS), smaller prostate volume, and prostate-specific antigen (PSA) > 10 ng/mL were significantly associated with the risk csPCa, respectively. The model developed by the nomogram showed an excellent accuracy for csPCa discrimination, as indicated by area under the curve (0.83), and calibration curves. DCA showed that our model was superior and surpassed all other models with a larger net benefit for various threshold probabilities. Based on our model, at a probability threshold of 30%, biopsying patients is the equivalent of a strategy that led to an absolute 5% reduction in the number of biopsies without missing any csPCa.

CONCLUSION

The developed nomogram consisting of PI-RAD, total PSA, and prostate volume showed a robust predictive capacity for csPCa before prostate biopsy that may be valuable for clinical judgment to prevent needless biopsy. Yet, the small percentage (5%) of yielded unnecessary biopsies that could be saved by using such a model, cast an important question on its merit and clinical applicability.