Prebiopsy bpMRI and hematological parameter-based risk scoring model for predicting outcomes in biopsy-naive men with PSA 4-20 ng/mL

Abstract

Excessive prostate biopsy is a common problem for clinicians. Although some hematological and bi-parametric magnetic resonance imaging (bpMRI) parameters might help increase the rate of positive prostate biopsies, there is a lack of studies on whether their combination can further improve clinical detection efficiency. We retrospectively enrolled 394 patients with PSA levels of 4-20 ng/mL who underwent prebiopsy bpMRI during 2010-2021. Based on bpMRI and hematological indicators, six models and a nomogram were constructed to predict the outcomes of biopsy. Furthermore, we constructed and evaluated a risk scoring model based on the nomogram. Age, prostate-specific antigen (PSA) density (PSAD), systemic immune-inflammation index, cystatin C level, and the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 score were significant predictors of prostate cancer (PCa) on multivariable logistic regression analyses (P < 0.05) and the five parameters were used to construct the XYFY nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was 0.916. Based on the nomogram, a risk scoring model (XYFY risk model) was constructed and then we divided the patients into low-(XYFY score: < 95), medium-(XYFY score: 95-150), and, high-risk (XYFY score: > 150) groups. The predictive values for diagnosis of PCa and clinically-significant PCa among the three risk groups were 3.0%(6/201), 41.8%(51/122), 91.5%(65/71); 0.5%(1/201), 19.7%(24/122), 60.6%(43/71), respectively. In conclusion, in this study, we used hematological and bpMRI parameters to establish and internally validate a XYFY risk scoring model for predicting the biopsy outcomes for patients with PSA levels of 4-20 ng/mL and this risk model would support clinical decision-making and reduce excessive biopsies.

Figure 1

ROC curves comparing PI-RADS with PI-RADS + PSAD model, baseline model (baseline model was constructed on the basis of age, SII, and CysC level), baseline + PSAD model, PI-RADS + PSAD + age model, and XYFY model (XYFY model was constructed on the basis of age, SII, CysC level, PI-RADS v2.1 score, and PSAD) in predicting the outcomes of biopsy. ROC receiver operating characteristic; PI-RADS Prostate Imaging Reporting and Data System; PSAD prostate-specific antigen density.

Figure 2

The nomogram for predicting the outcomes of biopsy in biopsy-naive men with PSA 4–20 ng/mL. PI-RADS Prostate Imaging Reporting and Data System; PSAD prostate-specific antigen density; SII systemic immune-inflammation index; CysC cystatin C.

Figure 3

ROC curves for comparing predictive performance of XYFY risk model and PI-RADS. ROC receiver operating characteristic; PI-RADS Prostate Imaging Reporting and Data System.

Figure 4

An 80-year-old patient with a PI-RADS v2.1 score of 3 + 3. Transition zone (TZ): (a) Axial T2WI shows a heterogeneous signal intensity with obscured margins nodule (white arrow). (b) DWI map shows a focal lesion with a markedly hyperintense signal (white arrow) corresponding to the lesion seen in (a). T2WI PI-RADS = 3, DWI PI-RADS = 4, PI-RADS assessment category = 4. Peripheral zone (PZ): (c) DWI map shows a focal lesion with a markedly hyperintense signal (black arrow) in the right PZ. (d) Apparent diffusion coefficient (ADC) map does not show a markedly hypointense signal corresponding to the lesion seen in (c). DWI PI-RADS = 3, ADC PI-RADS = 1, PI-RADS assessment category = 3.