The association of quantitative PSMA PET parameters with pathologic ISUP grade: an international multicenter analysis

The Added Value of Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography to Magnetic Resonance Imaging for Local Staging of Prostate Cancer in Patients Undergoing Radical Prostatectomy

BACKGROUND AND OBJECTIVE

The role of prostate-specific membrane antigen (PSMA)-based positron emission tomography (PET)/computed tomography (CT) in addition to magnetic resonance imaging (MRI) for local staging of prostate cancer (PC) has been poorly addressed so far. Our aim was to assess the diagnostic accuracy of PSMA PET/CT and MRI, alone and combined, for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) in PC.

METHODS

We conducted a multicenter retrospective study evaluating patients undergoing PSMA PET/CT and MRI before radical prostatectomy. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) for detection of EPE and SVI were calculated for MRI and PSMA PET/CT alone and combined.

KEY FINDINGS AND LIMITATIONS

We included 550 patients, of whom 2%, had low-risk, 43% had intermediate-risk, and 55% had high-risk PC. Overall, 52% of patients had EPE and 21% had SVI at histopathology. Patient-based comparison of MRI versus PSMA PET/CT for detection of EPE revealed sensitivity of 60% versus 41% (p < 0.001), specificity of 77% versus 83% (p = 0.075), PPV of 75% versus 73% (p = 0.6), NPV of 64% versus 56% (p < 0.001), and AUC of 69% versus 62% (p = 0.01). Combining the modalities increased the sensitivity (73%; p < 0.001) and NPV (69%; p < 0.001) and decreased the specificity (67%; p < 0.001) and PPV (71%; p = 0.01) over MRI alone. Patient-based comparison of MRI versus PSMA PET/CT for detection of SVI revealed sensitivity of 36% versus 44% (p = 0.2), specificity of 96% versus 96% (p > 0.99), PPV of 71% versus 75% (p = 0.6), NPV of 85% versus 87% (p = 0.2), and AUC of 66% versus 70% (p = 0.2). Combining the modalities increased the sensitivity (60%; p < 0.001), NPV (90%; p < 0.001), and AUC (76%; p < 0.001) and decreased the specificity (92%; p < 0.001) over MRI alone. Limitations include the retrospective nature of the study, selection of higher-risk cases for PSMA PET/CT, and lack of central review.

CONCLUSIONS AND CLINICAL IMPLICATIONS

PSMA PET/CT has lower sensitivity for EPE detection in comparison to MRI. However, addition of PSMA PET information to MRI improved the sensitivity for EPE and SVI detection. Thus, the two modalities should be combined to guide treatment selection.

PATIENT SUMMARY

Combining MRI (magnetic resonance imaging) scans with another type of imaging called PSMA PET/CT (prostate-specific membrane antigen positron emission tomography/computed tomography) for patients with prostate cancer leads to better identification of cancer growth outside the prostate in comparison to MRI alone. This could potentially improve the choice of prostate cancer treatment.

The predictive value of multiparametric MRI combined with [18F]PSMA-1007 PET/CT for the pathological upgrade in prostate cancer: a multicenter study

PURPOSE

This study aimed to develop a predictive model that integrates parameters derived from preoperative multiparametric magnetic resonance imaging (mpMRI) and [18F]PSMA-1007 PET/CT for reliably predicting pathological upgrading from systematic biopsy (SB) to radical prostatectomy (RP) specimens.

METHODS

We ultimately retrospectively analyzed 163 patients with biopsy-confirmed localized prostate cancer (PCa) who underwent preoperative mpMRI and [18F]PSMA-1007 PET/CT scans between January 2019 and June 2022. Clinical and imaging characteristics were compared between patients with and without pathological upgrading. Predictive factors for pathological upgrading were evaluated through univariate and multivariable analyses. Predictive models were constructed based on the identified parameters. Receiver operating characteristic (ROC) curves were utilized to determine optimal cutoff values and to evaluate model performance. Additionally, patients from two external centers were selected as a validation cohort.

RESULTS

A total of 55 (33.7%) cases experienced pathological upgrading. Multivariate analysis revealed that ADCmean - ADCmin (P = 0.035); SUVmax (P = 0.003); highest tumor grade at SB, ISUP grade group (ISUP GG) 1 vs. 2 (P = 0.001), ISUP GG 1 vs. 3 (P < 0.001), ISUP GG 1 vs. 4 (P < 0.001); and multifocality on [18F]PSMA-1007 PET/CT (P = 0.007) were independent predictors for pathological upgrading. The combined model achieved an area under the curve (AUC) of 0.803 (95% CI: 0.734 to 0.861), indicating robust discriminative power. External validation confirmed the model's reliability and predictive ability.

CONCLUSION

Our predictive model, integrating mpMRI and [18F]PSMA-1007 PET/CT parameters, effectively forecasts pathological upgrading in PCa, allowing for more precise treatment risk stratification.

External validation of nomograms including PSMA PET information for the prediction of lymph node involvement of prostate cancer

BACKGROUND

Novel nomograms predicting lymph node involvement (LNI) of prostate cancer (PCa) including PSMA PET information have been developed. However, their predictive accuracy in external populations is still unclear.

PURPOSE

To externally validate four LNI nomograms including PSMA PET parameters (three Muehlematter models and the Amsterdam-Brisbane-Sydney model) as well as the Briganti 2012 and MSKCC nomograms.

METHODS

Patients with histologically confirmed PCa undergoing preoperative MRI and PSMA PET/CT before radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) were included. Model discrimination (AUC), calibration and net benefit using decision curve analysis were determined for each nomogram.

RESULTS

A total of 437 patients were included, comprising 0.7% with low-risk disease, 39.8% with intermediate-risk disease, and 59.5% with high-risk disease. Among them, 86 out of 437 (19.7%) had pN1 disease. The sensitivity and specificity of PSMA PET/CT for the detection of LNI were 47.7% (95% CI: 36.8-58.7) and 95.4% (95% CI: 92.7-97.4), respectively. Among predictive models, the Amsterdam-Brisbane-Sydney model achieved the highest discrimination (AUC: 0.81, 95% CI: 0.76-0.86), followed by Muehlematter Model 1 (AUC: 0.79, 95% CI: 0.74-0.85), both with good calibration but slight systematic overestimation of risks across all thresholds. The MSKCC and Briganti 2012 models had AUCs of 0.68 (95% CI: 0.61-0.74) and 0.67 (95% CI: 0.61-0.73), respectively, and both had moderate calibration. Decision curve analysis indicated that the Amsterdam-Brisbane-Sydney model provided superior net benefit across thresholds of 5-20%, followed by the Muehlematter Model 1 nomogram showing benefit in the 14-20% range. Using thresholds of 8% for the Amsterdam-Brisbane-Sydney nomogram and 15% for Muehlematter Model 1, ePLND could be spared in 15% and 16% of patients, respectively, without missing any LNI cases.

CONCLUSION

External validation of the Muehlematter Model 1 and Amsterdam-Brisbane-Sydney nomograms for predicting LNI confirmed their strong model discrimination, moderate calibration, and good clinical utility, supporting their reliability as tools to guide clinical decision-making.