Impact of PSA density of transition zone as a potential parameter in reducing the number of unnecessary prostate biopsies in patients with psa levels between 2.6 and 10.0 ng/mL

Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies

OBJECTIVES

To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS).

METHODS

This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics.

RESULTS

A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001).

CONCLUSION

Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers.

CLINICAL RELEVANCE STATEMENT

Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers.

KEY POINTS

• Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer

PURPOSE

To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD.

METHODS

1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test.

RESULTS

Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set).

CONCLUSION

DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.

Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL

Purpose

To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL.

Methods

A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram.

Conclusion

The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.

Detection rate and variables associated with incidental prostate cancer by holmium laser enucleation of the prostate

OBJECTIVES

Holmium laser enucleation of the prostate is well-established and effective for bladder outlet obstruction due to benign prostatic hyperplasia. The objective of this study was to examine the detection rate of incidental prostate cancer by holmium laser enucleation of the prostate and variables associated with them.

METHODS

A total of 612 patients were enrolled. We retrospectively examined the detection rate of incidental prostate cancer and perioperative variables associated with them.

RESULTS

Forty-nine of 612 patients were diagnosed with incidental prostate cancer. Univariate logistic regression analysis showed that higher prostate-specific antigen density (odds ratio 3.34, 95% confidence interval 1.02-10.94, P = 0.05), higher prostate-specific antigen density of the transition zone (odds ratio 2.28, 95% confidence interval 1.02-5.09, P = 0.04), and findings of the prostate cancer on magnetic resonance imaging (peripheral zone: odds ratio 4.71, 95% confidence interval 1.70-13.1, P = 0.003; transition zone: odds ratio 3.46, 95% confidence interval 1.74-6.86, P < 0.001; peripheral and transition zones: odds ratio 6.00, 95% confidence interval 1.51-23.8, P = 0.01) were significantly associated with incidental prostate cancer. Multivariate logistic regression analysis showed that findings of the prostate cancer on magnetic resonance imaging (peripheral zone: odds ratio 4.36, 95% confidence interval 1.49-12.8, P = 0.001; transition zone: odds ratio 3.54, 95% confidence interval 1.75-7.16, P < 0.001; peripheral and transition zones: odds ratio 6.14, 95% confidence interval 1.53-24.5, P = 0.01) was an independent risk factor for incidental prostate cancer.

CONCLUSION

The detection rate of incidental prostate cancer was 8.0%, and findings of the prostate cancer on magnetic resonance imaging were an independent predictive factor for incidental prostate cancer.

Diagnostic accuracy of prostate-specific antigen below 4 ng/mL as a cutoff for diagnosing prostate cancer in a hospital setting: A systematic review and meta-analysis

PURPOSE

A prostate-specific antigen (PSA) cutoff of 4 ng/mL has been widely used for prostate cancer screening in population-based settings. However, the accuracy of PSA below 4 ng/mL as a cutoff for diagnosing prostate cancer in a hospital setting is inconclusive. We systematically reviewed the accuracy of PSA below 4 ng/mL cutoff in a hospital setting.

MATERIALS AND METHODS

We systematically reviewed the literature by searching major databases until March 2020, and a meta-analysis and quality assessment were performed.

RESULTS

A total of 11 studies were included at the completion of the screening process. The meta-analysis showed a sensitivity of 0.92 and a specificity of 0.16 for a PSA cutoff below 4 ng/mL. The area under the hierarchical summary receiver operating characteristic curve was 0.87, the positive likelihood ratio was 1.23, the negative likelihood ratio was 0.46, and the diagnostic odds ratio was 2.64. PSA sensitivities and specificities varied according to the cutoff range: 0.94 and 0.17 for 2 to 2.99 ng/mL, and 0.92 and 0.16 for 3 to 3.99 ng/mL, respectively. No significant differences in the sensitivity and specificity of PSA cutoffs in the range of 2 to 2.99 ng/mL and 3 to 3.99 ng/mL were found.

CONCLUSIONS

Although a PSA cutoff <3 ng/mL is relatively more sensitive and specific than PSA ≥3 ng/mL, no significant differences in sensitivity and specificity were found in the diagnosis of prostate cancer. Therefore, clinicians should choose an appropriate PSA cutoff on the basis of clinical circumstances and patients' characteristics.

Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4-10 ng ml-1)

Our goal was to establish two new predictive models of prostate cancer to determine whether to require a prostate biopsy when the prostate-specific antigen level is in the diagnostic gray zone. A retrospective analysis of 197 patients undergoing prostate biopsy with prostate-specific antigens between 4 and 10 ng ml⁻¹ was conducted. Of these, 47 patients were confirmed to have cancer, while the remaining 150 patients were diagnosed with benign prostate disease after examining biopsy pathology. Two multivariate logistic regression models were established including age, prostate volumes, free/total prostate-specific antigen ratio, and prostate-specific antigen density using SPSS 19.0 to obtain the predicted probability and Logit P, and then, two receiver operating characteristic (ROC) curves were drawn to obtain the best cutoff value for prostate biopsy: one for the group of all the prostate cancers and one for the group of clinically significant prostate cancers. The best cutoff value for prostate biopsy was 0.25 from the multivariate logistic regression ROC curve model of all the prostate cancers, which gave a sensitivity of 75.4% and a specificity of 75.8%. The best cutoff value for prostate biopsy was 0.20 from the multivariate logistic regression model of clinically significant prostate cancers, which gave a sensitivity of 76.7% and a specificity of 80.1%. We identified the best cutoff values for prostate biopsy (0.25 for all prostate cancers and 0.20 for clinically significant prostate cancers) to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone.

Adaptation of the prostate biopsy collaborative group risk calculator in patients with PSA less than 10 ng/ml improves its performance

PURPOSES

The prostate biopsy collaborative group risk calculator (PBCGRC) is a newly developed risk estimator for predicting prostate biopsy outcomes. However, its clinical usefulness is still unknown within the so-called gray area of PSA values. This study aimed to determine whether updating the PBCGRC improves its predictive performance for predicting any-grade and high-grade (HG), defined as biopsy Gleason score ≥ 7, prostate cancer (PCa) in patients with prostate-specific antigen (PSA) less than 10 ng/ml.

METHODS

The risk of any-grade and HGPCa was calculated using the PBCG risk calculation formulas updated by recalibration in the large, logistic recalibration and model revision. Predictive performances of the PBCGRC and the updated models were compared using discrimination, calibration, and clinical utility.

RESULTS

Within the study sample of 526 patients, PCa was detected in 193 (36.7%), and 78 (14.8%) of them had HGPCa. According to the calibration curves, the PBCGRC overestimated the risk of PCa. Predictive accuracy of the revised model was higher [the area under the receiver-operating characteristic curve (AUCs), 65.4% and 70.2%] than that of the PBCGRC (AUCs, 60.4% and 64.3%) for any-grade and HGPCa. The net benefit was greater for model revision in comparison with the original model.

CONCLUSION

The performance accuracy of PBCGRC for the prediction of any and HGPC in men undergoing prostate biopsy with PSA levels below 10 ng/ml is suboptimal. The model revision resulted with significant improvement in model performance. However, external validation of the revised model is necessary before its routine use in clinical practice.

A Comparative Study: Has MRI-guided Fusion Prostate Biopsy Changed the Prostate-specific Antigen Gray-zone Range?

Objective

The gray-zone prostate-specific antigen (PSA) range is accepted to be 4-10 ng/ml and is considered to vary according to age. We aimed to investigate whether fusion prostate biopsy (FPB), which has been reported to have relatively higher cancer detection rates, has an effect on gray-zone PSA cut-off value.

Material and methods

This retrospective study included patients that underwent standard prostate biopsy (SPB) or multiparametric magnetic resonance imaging (MpMRI)-guided FPB (SPB+ targeted biopsy). All the patients included in the study were detected with a Prostate Imaging Reporting and Data System (PI-RADS) ≥3 lesion on MpMRI (the FPB group only). The demographics, clinical characteristics, and histopathological diagnoses were recorded for each patient.

Results

A total of 1,628 patients comprising 1,208 patients in the SPB group and 420 patients in the FPB group were included in the study. The mean PSA level was 9.75±6.68 ng/ml in the FBP group and 10.46±6.46 ng/ml in the SPB group (p=0.053). Prostate cancer (PCa) detection rate was significantly higher in the FPB group as compared to the SPB group (42.4% vs. 36.4%). The PSA cut-off value for PCa was 9.75 ng/ml (sensitivity and specificity, 81%) in the SPB group and was 7.55 ng/ml (sensitivity and specificity, 81% and 84%, respectively) in the FPB group. In the FPB group, the cancer detection rate among the patients with a PSA level of 7.55-10.00 ng/ml was 56.1%.

Conclusion

The results indicated that the introduction of FPB into clinical practice, which has relatively higher cancer detection rates, has further lowered the upper limit for gray-zone PSA.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.