Can 68Ga-PSMA-11 PET/CT predict pathological upgrading of prostate cancer from MRI-targeted biopsy to radical prostatectomy?

A PSMA PET/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer

PURPOSE

This study is to investigate the diagnostic value of 68Ga-PSMA-11 in improving the concordance between mpMRI-TB and combined biopsy (CB) in detecting PCa.

METHODS

115 consecutive men with 68Ga-PSMA-11 PET/CT prior to prostate biopsy were included for analysis. PSMA intensity, quantified as maximum standard uptake value (SUVmax), minimum apparent diffusion coefficient (ADCmin) and other clinical characteristics were evaluated relative to biopsy concordance using univariate and multivariate logistic regression analyses. A prediction model was developed based on the identified parameters, and a dynamic online diagnostic nomogram was constructed, with its discrimination evaluated through the area under the ROC curve (AUC) and consistency assessed using calibration plots. To assess its clinical applicability, a decision curve analysis (DCA) was performed, while internal validation was conducted using bootstrapping methods.

RESULTS

Concordance between mpMRI-TB and CB occurred in 76.5% (88/115) of the patients. Multivariate logistic regression analyses performed that SUVmax (OR= 0.952; 95% CI 0.917-0.988; P= 0.010) and ADCmin (OR= 1.006; 95% CI 1.003-1.010; P= 0.001) were independent risk factors for biopsy concordance. The developed model showed a sensitivity, specificity, accuracy and AUC of 0.67, 0.78, 0.81 and 0.78 in the full sample. The calibration curve demonstrated that the nomogram's predicted outcomes closely resembled the ideal curve, indicating consistency between predicted and actual outcomes. Furthermore, the decision curve analysis (DCA) highlighted the clinical net benefit achievable across various risk thresholds. These findings were reinforced by internal validation.

CONCLUSIONS

The developed prediction model based on SUVmax and ADCmin showed practical value in guiding the optimization of prostate biopsy pattern. Lower SUVmax and Higher ADCmin values are associated with greater confidence in implementing mono-TB and safely avoiding SB, effectively balancing benefits and risks.

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen's kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

Deep learning based on 68Ga-PSMA-11 PET/CT for predicting pathological upgrading in patients with prostate cancer

Objectives

To explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) in patients with prostate cancer (PCa).

Methods

In this retrospective study, all patients underwent 68Ga-PSMA-11 PET/CT, transrectal ultrasound (TRUS)-guided systematic biopsy, and RP for PCa sequentially between January 2017 and December 2022. Two DL models (three-dimensional [3D] ResNet-18 and 3D DenseNet-121) based on 68Ga-PSMA-11 PET and support vector machine (SVM) models integrating clinical data with DL signature were constructed. The model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Of 109 patients, 87 (44 upgrading, 43 non-upgrading) were included in the training set and 22 (11 upgrading, 11 non-upgrading) in the test set. The combined SVM model, incorporating clinical features and signature of 3D ResNet-18 model, demonstrated satisfactory prediction in the test set with an AUC value of 0.628 (95% confidence interval [CI]: 0.365, 0.891) and accuracy of 0.727 (95% CI: 0.498, 0.893).

Conclusion

A DL method based on 68Ga-PSMA-11 PET may have a role in predicting pathological upgrading from biopsy to RP in patients with PCa.

The role of PSMA PET/CT to predict upgrading in patients undergoing radical prostatectomy for ISUP grade group 1 prostate cancer

INTRODUCTION AND OBJECTIVES

To investigate the additive role of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) independent from multiparametric magnetic resonance imaging (mpMRI) and clinical-pathological parameters to predict pathological upgrading in patients with ISUP grade group (GG) 1 prostate cancer (PCa) at prostate biopsy.

MATERIALS AND METHODS

A total of 41 patients who underwent robotic radical prostatectomy (RP) for GG1 disease at prostate biopsy with preoperative PSMA PET/CT and mpMRI images available for central review were included in the study. Univariate and multivariate logistic regression analyses were performed to determine the independent predictors of pathological upgrading (GG ≥ 2).

RESULTS

Final RP pathology revealed upgrading in 26 patients (65.9%); to GG 2 disease in 25 cases and GG 4 disease in one case. International Society of Urological Pathology (ISUP) upgrading rates for prostate imaging-reporting and data system (PIRADS)-5, PIRADS-4, and PIRADS ≤ 3 lesions were 78%, 74%, and 38%, respectively. Fourteen out of 15 (93.3%) patients with an SUVmax ≥ 5.6 and all patients with an SUVmax ≥ 6.5 (n = 10) had pathological upgrading. The upgrading rate in patients with SUV < 5.6 was 46.2% (12/26). Intraprostatic SUVmax ≥ 5.6 was found as the only independent predictor of pathological upgrading in multivariate analysis.

CONCLUSION

High prostatic PSMA uptake was found to be a very reliable predictor of pathological upgrading, but low PSMA uptake cannot exclude pathological upgrading. Intraprostatic PSMA uptake along with previously known mpMRI and biopsy-related parameters should be considered when making a treatment decision in patients with GG1 PCa at prostate biopsy.

The prognostic value of 18F-PSMA-1007 PET/CT in predicting pathological upgrading of newly diagnosed prostate cancer from systematic biopsy to radical prostatectomy

Objective

This study aimed to evaluate predictors for upgrading of newly diagnosed prostate cancer from systematic biopsy (SB) to radical prostatectomy (RP) using fluorine-18 prostate-specific membrane antigen 1007 (¹⁸F-PSMA-1007) positron emission tomography/computed tomography (PET/CT) and association with clinical parameters.

Materials and methods

We retrospectively collected data from biopsy-confirmed prostate cancer (PCa) patients who underwent ¹⁸F-PSMA-1007 PET/CT prior to RP from July 2019 and October 2022. Imaging characteristics derived from ¹⁸F-PSMA-1007 PET/CT and clinical parameters were compared in patients of pathological upgrading and concordance subgroups. Univariable and multivariable logistic regressions were performed to analyze factors predicting histopathological upgrading from SB to RP specimens. Discrimination ability of independent predictors was further evaluated by receiver operating characteristic (ROC) analysis with corresponding area under the curve (AUC).

Results

Pathological upgrading occurred in 26.97% (41/152) PCa patients, and 23.03% (35/152) of all patients experienced pathological downgrading. Concordance rate reached 50% (76/152). International Society of Urological Pathology grade group (ISUP GG) 1(77.78%) and ISUP GG 2 (65.22%) biopsies were related with the highest rate of upgrading. Multivariable logistic regression analyses showed that prostate volume (OR= 0.933; 95% CI, 0.887-0.982; p = 0.008), ISUP GG 1 vs. 4 (OR= 13.856; 95% CI: 2.467-77.831; p = 0.003), and total uptake of PSMA-avid lesions (PSMA-TL) (OR = 1.003; 95% CI, 1.000-1.006; p = 0.029) were found to be independent risk factors of pathological upgrading after RP. The AUCs and corresponding sensitivity and specificity of the independent predictors of synthesis for upgrading were 0.839, 78.00%, and 83.30% respectively, which showed good discrimination capacity.

Conclusion

¹⁸F-PSMA-1007 PET/CT may help to predict pathological upgrading between biopsy and RP specimens, particularly for ISUP GG 1 and ISUP GG 2 patients with higher PSMA-TL and smaller prostate volume.

A Systematic Review of the Variability in Performing and Reporting Intraprostatic Prostate-specific Membrane Antigen Positron Emission Tomography in Primary Staging Studies

Context

Prostate cancer (PCa) remains one of the leading causes of cancer-related deaths in men worldwide. Men at risk are typically offered multiparametric magnetic resonance imaging and, if suspicious, a targeted biopsy. However, false-negative rates of magnetic resonance imaging are consistently 18%; therefore, there is growing interest in improving the diagnostic performance of imaging through novel technologies. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is being utilised for PCa staging and, more recently, for intraprostatic tumour localisation. However, significant variability has been observed in how PSMA PET is performed and reported.

Objective

In this review, we aim to evaluate how pervasive this variability is in trials investigating the performance of PSMA PET in primary PCa workup.

Evidence acquisition

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, we performed an optimal search in five different databases. After removing duplicates, 65 studies were included in our review.

Evidence synthesis

Studies dated back as early as 2016, with numerous different source countries. There was variation in the reference standard for PSMA PET, with some using biopsy specimens or surgical specimens, and in some cases, a combination of the two. Similar inconsistencies were noted when studies selected histological definitions of clinically significant PCa, while some omitted their definition altogether. The most significant variations in performing PSMA PET were the radiotracer type, dose, acquisition time after injection, and the PET camera being utilised. Substantial variation in the reporting of PSMA PET was noted, with no consistency in defining what constitutes a positive intraprostatic lesion. Across 65 studies, four different definitions were used.

Conclusions

This systematic review has highlighted considerable variation in obtaining and performing a PSMA PET study in the context of primary PCa diagnosis. Given the discrepancy in how PSMA PET was performed and reported, it questions the homogony of studies from centre to centre. Standardisation of PSMA PET is required for this to become a consistently useful and reproducible modality in the diagnosis of PCa.

Patient summary

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is being utilised for staging and localisation of prostate cancer (PCa); however, there is significant variability in performing and reporting PSMA PET. Standardisation of PSMA PET is required for results to be consistently useful and reproducible for the diagnosis of PCa.

Prostate specific membrane antigen positron emission tomography in primary prostate cancer diagnosis: First-line imaging is afoot

Prostate specific membrane antigen positron emission tomography (PSMA PET) is an excellent molecular imaging technique for prostate cancer. Currently, PSMA PET for patients with primary prostate cancer is supplementary to conventional imaging techniques, according to guidelines. This supplementary function of PSMA PET is due to a lack of systematic review of its strengths, limitations, and potential development direction. Thus, we review PSMA ligands, detection, T, N, and M staging, treatment management, and false results of PSMA PET in clinical studies. We also discuss the strengths and challenges of PSMA PET. PSMA PET can greatly increase the detection rate of prostate cancer and accuracy of T/N/M staging, which facilitates more appropriate treatment for primary prostate cancer. Lastly, we propose that PSMA PET could become the first-line imaging modality for primary prostate cancer, and we describe its potential expanded application.

A nomogram for accurately predicting the pathological upgrading of prostate cancer, based on 68 Ga-PSMA PET/CT

PURPOSE

To develop and validate a nomogram for preoperative predicting the pathological upgrading of prostate cancer (PCa).

METHODS

The prediction model was developed in a primary cohort that consisted of 208 PCa patients. All patients included in the study possessed both biopsy pathology specimens and radical prostatectomy pathology specimens, and completed the (⁶⁸ Ga-prostate-specific membrane antigen [PSMA]) positron emission tomography/computed tomography (PET/CT) detection. The R function "createDataPartition" was used in a 7:3 ratio to randomly divide the patients into training and validation cohorts. In the training cohort, the independent predictors of pathological upgrading of PCa were determined by univariate analysis, univariate regression analysis and multivariate regression analysis. Based on these independent predictors, a nomogram was developed, and its performance was evaluated by receiver operating characteristic (ROC) curve, area under the curve (AUC) and calibration curve of training cohort and validation cohort.

RESULTS

The nomogram incorporated five independent predictors including prostate volume (PV), SUV_max of the ⁶⁸ Ga-PSMA PET/CT examination on prostate lesions (SUV_max ), body mass index (BMI); percentage of cancer positive biopsy cores (PPC) and biopsy International Society of Urological Pathology (ISUP) grade. The nomogram showed good diagnostic accuracy for the pathological upgrading of both the training cohort and the validation cohort (AUC = 0.818 and 0.806, respectively). The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation.

CONCLUSIONS

We developed and validated a nomogram to accurately predict the risk of pathological upgrading after radical PCa surgery, which can provide accurate basis for therapeutic schedule and prognostic data of PCa patients.

Establishment and prospective validation of an SUVmax cutoff value to discriminate clinically significant prostate cancer from benign prostate diseases in patients with suspected prostate cancer by 68Ga-PSMA PET/CT: a real-world study

Background and Aims: The aims of this study were to establish a maximum standardized uptake value (SUV_max) cutoff to discriminate clinically significant prostate cancer (csPCa) from benign prostate disease (BPD) by ⁶⁸Ga-labeled prostate-specific membrane antigen (⁶⁸Ga-PSMA-11) positron emission tomography/computed tomography (PET/CT) in patients with suspected prostate cancer (PCa), and to perform a prospective real-world validation of this cutoff value.

Methods: The study included a training cohort to identify an SUV_max cutoff value and a prospective real-world cohort to validate it. A retrospective analysis assessed 135 patients with suspected PCa in a large tertiary care hospital in China who underwent ⁶⁸Ga-PSMA-11 PET/CT. All patients were suspected of having PCa based on symptoms, digital rectal examination (DRE), total prostate-specific antigen (tPSA) level, and multiparameter magnetic resonance imaging (mpMRI). The ⁶⁸Ga-PSMA PET/CT results were evaluated using histopathological results from transrectal ultrasound-guided 12-core biopsy with necessary targeted biopsy as references. Patients with Gleason scores (GS) ≥7 from the biopsy results were diagnosed with csPCa, and patients with negative biopsy and follow-up results were diagnosed with BPD. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal SUV_max cutoff value. The cutoff value was prospectively validated in 58 patients with suspected PCa. The diagnostic benefits of the cutoff value for clinical decision making were also evaluated.

Results: According to ROC curve analysis, the most appropriate SUV_max cutoff value for discriminating csPCa from BPD was 5.30 (sensitivity, 85.85%; specificity, 86.21%; area under the curve [AUC], 0.893). The cutoff achieved a sensitivity of 83.33%, a specificity of 81.25%, a positive predictive value (PPV) of 92.11%, a negative predictive value (NPV) of 65.00%, and an accuracy of 82.76% in the prospective validation cohort. Metastases were used as an indicator to reduce false negative results in patients with SUV_max ≤ 5.30. In patients without metastases, an SUV_max value of 5.30 was also the best cutoff to diagnose localized csPCa (sensitivity, 80.43%; specificity, 86.21%; AUC, 0.852). The cutoff discriminated localized csPCa from BPD with a sensitivity of 76.19%, a specificity of 81.25%, a PPV of 84.21%, an NPV of 72.22%, and an accuracy of 78.38% in the prospective validation cohort. The cutoff, combined with metastases, achieved an accuracy of 89.12% in all patients, increasing accuracy by 8.29% and reducing equivocal results compared with manual reading. There was a strong correlation between SUV_max and PSMA expression (r_s = 0.831, P < 0.001) and a moderate correlation between SUV_max and GS (r_s = 0.509, P < 0.001). The PSMA expression and SUV_max values of patients with csPCa were significantly higher than those of patients with BPD (P < 0.001).

Conclusion: We established and prospectively validated the best SUV_max cutoff value (5.30) for discriminating csPCa from BPD with high accuracy in patients with suspected PCa. 5.30 is an effective cutoff to discriminate csPCa patients with or without metastases. The cutoff may provide a potential tool for the precise identification of csPCa by ⁶⁸Ga-PSMA PET/CT, ensuring high accuracy and reducing equivocal results.