Prediction of clinically significant prostate cancer by [68 Ga]Ga-PSMA-11 PET/CT: a potential tool for selecting patients for active surveillance

Which patients with negative PSMA-PET imaging can safely avoid biopsy for prostate cancer? a novel step towards PSMA-based biopsy-free strategy

PURPOSE

This study aimed to identify if a subset of men can safely avoid or delay prostate biopsy based on negative results of prostate-specific membrane antigen positron emission tomography (PSMA-PET).

MATERIALS AND METHODS

Among 341 consecutive cases in a prospective biopsy cohort (NCT05073653), 111 treatment-naïve men with negative PSMA-PET (PRIMARY-score 1/2) were included. All participants underwent PSMA-PET and histopathological examinations. Clinically significant prostate cancer (csPCa) was defined as Grade Group ≥ 2. Multivariate logistic regression was employed to identify predictors of non-csPCa. Receiver operating characteristic (ROC) analysis was performed to detect non-csPCa on prostate pathology. PSMA-postive patients were additionally reviewed to assess the imaging and pathological outcomes.

RESULTS

Younger age was identified as an independent predictor (P = 0.006) for the absence of csPCa. ROC analysis of csPCa revealed the largest areas under the curve of 0.77 (0.67-0.87) and 0.78 (0.68-0.88) for individual age in the entire PSMA-negative cohort and the MRI subset, respectively (both P < 0.001). The negative predictive value (NPV) of PSMA-PET for csPCa detection improved with a decreasing age, from 88% in all, to 98% in men aged under 65 yrs (98% vs. 88%, P = 0.021), and to 100% in men aged under 60 yrs (100% vs. 88%, P = 0.040). The NPV of PSMA-PET improved from 88 to 94% when combined with negative MRI, and to 100% in men with negative MRI and aged under 65 yrs. The prevalence was 57% for csPCa and 65% for PCa of any grade.

CONCLUSION

We preliminarily propose that omission or postponement of prostate biopsy should be considered for men under the age of 65 yrs with negative PSMA-PET scored as PRIMARY 1 or 2. Conversely, prostate biopsy might be considered in biopsy-naïve men aged 65 yrs or older with strong clinical suspicion of PCa, despite negative PSMA-PET. Further prospective and external evaluation is needed to prove the robustness of this novel strategy.

The impact of integrating PRIMARY score or SUVmax with MRI-based risk models for the detection of clinically significant prostate cancer

PURPOSE

An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [68Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies.

METHODS

A total of 683 consecutive biopsy-naïve men who underwent both [68Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies.

RESULTS

The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873-0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0-40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5-40% for csPCa in both the development and validation cohorts.

CONCLUSION

The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.

PRIMARY scoring in 68Ga-PSMA PET/CT: correlation with prostate cancer risk groups and its potential impact on active surveillance

OBJECTIVE

The PRIMARY scoring system is a scale designed to identify clinically significant intraprostatic malignancies on 68Ga-PSMA PET/CT images. Active surveillance is a management method for patients with low-risk prostate cancer. In this study, we aimed to assess the efficacy of PRIMARY scoring in identifying appropriate candidates for active surveillance based on the distribution within prostate cancer risk groups.

METHODS

The data of 134 patients diagnosed with PCa by biopsy who underwent 68Ga-PSMA PET/CT imaging for post-diagnostic staging purposes were retrospectively analyzed. Age, total PSA, ISUP grade, prostate lesion SUVmax values, PI-RADS scores, and PRIMARY scores were recorded. Patients were classified into low-risk and intermediate/high-risk groups.

RESULTS

In the intermediate/high-risk group, the PRIMARY score was 1-2 in 17.6% and 3-5 in 82.4% of patients. In the low-risk group, the PRIMARY score was 1-2 in 34.7% and 3-5 in 65.3% of patients. None of the patients in the low-risk group had a PRIMARY score of 5. The most frequent PRIMARY score in both groups was 4, and there was a significant difference between the average SUVmax values of the intermediate/high and low-risk groups with a PRIMARY score of 4 (p = 0.018). The sensitivity of PRIMARY scoring in detecting patients in the intermediate/high-risk group was 82.3%, the specificity was 34.6%, and the positive predictive value (PPV) was 68.6%. When a cut-off SUVmax value 5.0 was used for the PRIMARY score of 4, the sensitivity was 67.0%, the specificity was 65.3% and the PPV was 77.0%. In the ROC analysis, the area under the curve was 0.727 for PRIMARY scoring, 0.662 for PI-RADS, and 0.744 for their combined mean.

CONCLUSION

The PRIMARY scoring system can complement PI-RADS scoring in mpMRI for selecting patients suitable for active surveillance. Revising the PRIMARY score 4 with an SUVmax cut-off value may increase the specificity.

MRI and active surveillance: thoughts from across the pond

In the United States (US), urological guidelines recommend active surveillance (AS) for patients with low-risk prostate cancer (PCa) and endorse it as an option for those with favorable intermediate-risk PCa with a > 10-year life expectancy. Multiparametric magnetic resonance imaging (mpMRI) is being increasingly used in the screening, monitoring, and staging of PCa and involves the combination of T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted imaging. The American Urological Association (AUA) guidelines provide recommendations about the use of mpMRI in the confirmatory setting for AS patients but do not discuss the timing of follow-up mpMRI in AS. The National Comprehensive Cancer Network (NCCN) discourages using it more frequently than every 12 months. Finally, guidelines state that mpMRI can be used to augment risk stratification but should not replace periodic surveillance biopsy. In this review, we discuss the current literature regarding the use of mpMRI for patients with AS, with a particular focus on the approach in the US. Although AS shows a benefit to the addition of mpMRI to diagnostic, confirmatory, and follow-up biopsy, there is no strong evidence to suggest that mpMRI can safely replace biopsy for most patients and thus it must be incorporated into a multimodal approach. CLINICAL RELEVANCE STATEMENT: According to the US guidelines, regular follow-ups are important for men with prostate cancer on active surveillance, and prostate MRI is a valuable tool that should be utilized, in combination with PSA kinetics and biopsies, for monitoring prostate cancer. KEY POINTS: According to the US guidelines, the addition of MRI improves the detection of clinically significant prostate cancer. Timing interval imaging of patients on active surveillance remains unclear and has not been specifically addressed. MRI should trigger further work-ups, but not replace periodic follow-up biopsies, in men on active surveillance.

[68Ga]Ga‑PSMA‑617 PET-based radiomics model to identify candidates for active surveillance amongst patients with GGG 1-2 prostate cancer at biopsy

PURPOSE

To develop a radiomics-based model using [68Ga]Ga-PSMA PET/CT to predict postoperative adverse pathology (AP) in patients with biopsy Gleason Grade Group (GGG) 1-2 prostate cancer (PCa), assisting in the selection of patients for active surveillance (AS).

METHODS

A total of 75 men with biopsy GGG 1-2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [68Ga]Ga-PSMA PET scans and selected using the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression model. Logistic regression analyses were conducted to construct the prediction models. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the diagnostic value, clinical utility, and predictive accuracy of the models, respectively.

RESULTS

Among the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695-0.947) in the training set and 0.795 (0.603-0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720-0.941) in the training set and 0.829 (0.624-1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780-0.970) in the training set and 0.872 (0.678-1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model.

CONCLUSION

The combined model shows potential in stratifying men with biopsy GGG 1-2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.