Optimizing prostate biopsy decision-making for patients with Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions: novel magnetic resonance imaging (MRI)-based nomograms.

BACKGROUND

Current protocols endorse biopsies for men with Prostate Imaging-Reporting and Data System (PI-RADS v2.1) scores ≥3. However, the subjective nature of PI-RADS can lead to increased false positives and unnecessary biopsies. Synthetic magnetic resonance imaging (MRI), which quantifies multiple relaxation parameters, and apparent diffusion coefficient (ADC), which is the most commonly used quantitative metric, have not yet been combined with a predictive tool. This study aimed to develop and validate novel nomograms using multiparametric MRI, including synthetic MRI, to forecast the risk of prostate cancer (PCa) and clinically significant prostate cancer (csPCa), and to assess the potential of these nomograms to reduce unnecessary biopsies in PI-RADS ≥3 cases.

METHODS

Between August 2020 and August 2022, 323 patients suspected of PCa were enrolled from two centers (cohort 1: 243; cohort 2: 80). All participants underwent multiparametric MRI, including synthetic MRI, before targeted biopsy. Univariable and multivariable logistic regression identified risk factors for PCa and csPCa. Internal validation was conducted using bootstrap resampling, and nomogram performance was evaluated through receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). External validation was performed with cohort 2 data. The impact of the nomograms on biopsy decisions was measured by the avoidance rate and the risk of missed diagnoses.

RESULTS

The predictive nomogram for PCa incorporated four risk factors: age, quantitative transverse relaxation time (T2 value) from synthetic MRI, ADC value, and PI-RADS score. The csPCa nomogram included age, ADC value, and PI-RADS score. The nomograms showed high diagnostic accuracy with the area under the curves (AUCs) of 0.916 [95% confidence interval (CI): 0.901-0.974] and 0.947 (95% CI: 0.900-0.994) for PCa prediction in training and external datasets, and 0.884 (95% CI: 0.840-0.928) and 0.935 (95% CI: 0.871-0.998) for csPCa. Calibration curves confirmed the accuracy of predictions. DCA indicated that the nomograms possessed significant net benefit. For PCa detection, biopsy strategy combining our nomogram reduced biopsy procedures by 20.2% and 13.8% in the training and external cohorts, respectively, with a PCa miss rate of 4.5% for both cohorts. The csPCa-targeted biopsy strategy also provided clinical benefits, with biopsy avoidance rates of 20.2% and 10.0%, and csPCa miss rates of 4.8% and 1.7% for PI-RADS ≥3 patients in the two cohorts.

CONCLUSIONS

The nomograms integrating multiparametric MRI and synthetic MRI are highly effective in predicting PCa and csPCa, concurrently, reducing unnecessary biopsies for patients with PI-RADS ≥3 lesion.