Extended pelvic lymph node dissection in robot-assisted radical prostatectomy is an independent risk factor for major complications.

The aim of this study is to evaluate the major postoperative complication rate after robot-assisted radical prostatectomy (RARP) and to identify related risk factors. A consecutive series of patients who underwent RARP between September 2016 and May 2021, with or without extended pelvic lymph node dissection (ePLND) were analyzed for postoperative complications that occurred within 30 days following surgery. Potential risk factors related to complications were identified by means of a multivariate logistic analysis. Electronic medical records were retrospectively reviewed for the occurrence of major complications (Clavien-Dindo grade III or higher) on a per patient level. A multivariate logistic regression with risk factors was performed to identify contributors to complications. In total, 1280 patients were included, of whom 79 (6.2%) experienced at least 1 major complication. Concomitant ePLND was performed in 609 (48%) of patients. The majority of all complications were likely related to the surgical procedure, with anastomotic leakage and lymphoceles being the most common. Upon multivariate analysis, performing ePLND remained the only significant risk factor for the occurrence of major complications (OR 2.26, p = 0.001). In contrast to robot-assisted radical prostatectomy alone, the combination with extended pelvic lymph node dissection (ePLND) has a substantial risk of serious complications. Since the ePLND is performed mainly for staging purpose, the clinical contribution of the ePLND has to be reconsidered with the present use of the PSMA-PET/CT.

Validation of Claims Data for Absorbing Pads as a Measure for Urinary Incontinence after Radical Prostatectomy, a National Cross-Sectional Analysis.

The use of healthcare insurance claims data for urinary incontinence (UI) pads has the potential to serve as an objective measure for assessing post-radical prostatectomy UI rates, but its validity for this purpose has not been established. The aim of this study is to correlate claims data with Patient Reported Outcome Measures (PROMs) for UI pad use. Patients who underwent RP in the Netherlands between September 2019 and February 2020 were included. Incontinence was defined as the daily use of ≥1 pad(s). Claims data for UI pads at 12-15 months after RP were extracted from a nationwide healthcare insurance database in the Netherlands. Participating hospitals provided PROMS data. In total, 1624 patients underwent RP. Corresponding data of 845 patients was provided by nine participating hospitals, of which 416 patients were matched with complete PROMs data. Claims data and PROMs showed 31% and 45% post-RP UI (≥1 pads). UI according to claims data compared with PROMs had a sensitivity of 62%, specificity of 96%, PPV of 92%, NPV of 75% and accuracy of 81%. The agreement between both methods was moderate (κ = 0.60). Claims data for pads moderately align with PROMs in assessing post-prostatectomy urinary incontinence and could be considered as a conservative quality indicator.

The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer.

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.

Head-to-Head Comparison of 18F-PSMA-1007 Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging with Whole-mount Histopathology as Reference in Localisation and Staging of Primary Prostate Cancer.

BACKGROUND: Accurate local staging is critical for treatment planning and prognosis in prostate cancer (PCa). Although multiparametric magnetic resonance imaging (mpMRI) has high specificity for detection of extraprostatic extension (EPE) and seminal vesicle invasion (SVI), its sensitivity remains limited. 18F-PSMA-1007 positron emission tomography/computed tomography (PET/CT) may be more accurate in determining T stage. OBJECTIVE: To assess the diagnostic performance of 18F-PSMA-1007 PET/CT in comparison to mpMRI for intraprostatic tumour localisation and detection of EPE and SVI in men with primary PCa undergoing robot-assisted radical prostatectomy (RARP). DESIGN, SETTING, AND PARTICIPANTS: Between February 2019 and October 2020, 105 treatment-naïve patients with biopsy-proven intermediate- or high-risk PCa undergoing mpMRI and 18F-PSMA-1007 PET/CT before RARP were prospectively enrolled. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The diagnostic accuracy of 18F-PSMA-1007 PET/CT and mpMRI for intraprostatic tumour localisation and detection of EPE and SVI was assessed via histopathological examination of whole-mount RP specimens. The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy were calculated. The McNemar test was used to compare outcomes between imaging modalities. RESULTS AND LIMITATIONS: In 80 RP specimens, 129 PCa lesions were found, of which 96 were clinically significant PCa (csPCa). Per-lesion sensitivity for localisation of overall PCa was 85% (95% confidence interval [CI] 77-90%) with PSMA PET/CT and 62% (95% CI 53-70%) with mpMRI (p < 0.001). Per-lesion sensitivity for csPCa was 95% (95% CI 88-98%) with PSMA PET/CT and 73% (95% CI 63-81%) with mpMRI (p < 0.001). The diagnostic accuracy of PSMA PET/CT and mpMRI for detection of EPE per lesion did not significantly differ (sensitivity 45%, 95% CI 31-60% vs 55%, 95% CI 40-69%; p = 0.3; specificity 85%, 95% CI 75-92% vs 90%, 95% CI 81-86%; p = 0.5). The sensitivity and specificity of PSMA PET/CT and mpMRI for detection of SVI did not significantly differ (sensitivity 47%, 95% CI 21-73% vs 33%, 95% CI 12-62; p = 0.6; specificity 94%, 95% CI 88-98% vs 96%, 95% CI 90-99%; p = 0.8). CONCLUSIONS: 18F-PSMA-1007 is a promising imaging modality for localising intraprostatic csPCa but did not show additional value in assessing EPE and SVI in comparison to mpMRI. PATIENT SUMMARY: A new imaging technique called PET/CT (positron emission tomography/computed tomography) with the radioactive tracer 18F-PSMA-1007 shows promise in identifying the location of clinically significant prostate cancer. However, it does not seem to be of additional value over magnetic resonance imaging (MRI) for determining the local tumour stage.

Confocal laser microscopy for assessment of surgical margins during radical prostatectomy.

OBJECTIVE: To evaluate the feasibility of confocal laser microscopy (CLM) for intraoperative margin assessment as faster alternative to neurovascular structure-adjacent frozen-section examination (NeuroSAFE) during robot-assisted radical prostatectomy (RARP). PATIENTS AND METHODS: Surgical margins were assessed during 50 RARP procedures in patients scheduled for NeuroSAFE. Posterolateral sections were cut and imaged with CLM and further processed to conform with the NeuroSAFE protocol. Secondary resection (SR) was performed in case a positive surgical margin (PSM) was observed with NeuroSAFE. Afterwards, the CLM images were non-blinded assessed for the presence of PSMs. The accuracy of both NeuroSAFE and CLM was compared with conventional histopathology. Agreement for detection of PSMs between NeuroSAFE and CLM was evaluated with Cohen's kappa coefficient. Procedure times were compared with a Wilcoxon signed-ranks test. RESULTS: In total, 96 posterolateral sections of RP specimens were evaluated for the presence of PSMs. CLM identified 15 (16%) PSMs and NeuroSAFE identified 14 (15%) PSMs. CLM had a calculated sensitivity, specificity, positive predictive value and negative predictive value of 86%, 96%, 80% and 98% respectively for the detection of PSMs compared to definite pathology. After SR, residual tumour was found in six of 13 cases (46%), which were all identified by both techniques. There was a substantial level of agreement between CLM and NeuroSAFE (κ = 0.80). The median procedure time for CLM was significantly shorter compared to NeuroSAFE (8 vs 50 min, P < 0.001). The main limitation of this study was the non-blinded assessment of the CLM images. CONCLUSIONS: Compared to NeuroSAFE, CLM is a promising technique for intraoperative margin assessment and is able to reduce the time of intraoperative margin assessment.