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

PURPOSE To assess if PSMA PET quantitative parameters are associated with pathologic ISUP grade group (GG) and upgrading/downgrading. METHODS PCa patients undergoing radical prostatectomy with or without pelvic lymph node dissection staged with preoperative PSMA PET at seven referral centres worldwide were evaluated. PSMA PET parameters which included SUVmax, PSMAvolume, and total PSMA accumulation (PSMAtotal) were collected. Multivariable logistic regression evaluated the association between PSMA PET quantified parameters and surgical ISUP GG. Decision-tree analysis was performed to identify discriminative thresholds for all three parameters related to the five ISUP GGs The ROC-derived AUC was used to determine whether the inclusion of PSMA quantified parameters improved the ability of multivariable models to predict ISUP GG ≥ 4. RESULTS A total of 605 patients were included. Overall, 2%, 37%, 37%, 10% and 13% patients had pathologic ISUP GG1, 2, 3, 4, and 5, respectively. At multivariable analyses, all three parameters SUVmax, PSMAvolume and PSMAtotal were associated with GG ≥ 4 at surgical pathology after accounting for PSA and clinical T stage based on DRE, hospital and radioligand (all p  28, PSMAvol 0-2, 2-9, 9-20 and > 20 and PSMAtotal 0-12, 12-98 and > 98). PSMAvolume was significantly associated with GG upgrading (OR 1.03 95%CI 1.01 - 1.05). In patients with biopsy GG1-3, PSMAvolume ≥ 2 was significantly associated with higher odds for upgrading to ISUP GG ≥ 4, compared to PSMAvolume < 2 (OR 6.36, 95%CI 1.47 - 27.6). CONCLUSION Quantitative PSMA PET parameters are associated with surgical ISUP GG and upgrading. We propose clinically relevant thresholds of these parameters which can improve in PCa risk stratification in daily clinical practice

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

An updated model for predicting side-specific extraprostatic extension in the era of MRI-targeted biopsy

Purpose: Accurate prediction of extraprostatic extension (EPE) is pivotal for surgical planning. Herein, we aimed to provide an updated model for predicting EPE among patients diagnosed with MRI-targeted biopsy.// Materials and methods: We analyzed a multi-institutional dataset of men with clinically localized prostate cancer diagnosed by MRI-targeted biopsy and subsequently underwent prostatectomy. To develop a side-specific predictive model, we considered the prostatic lobes separately. A multivariable logistic regression analysis was fitted to predict side-specific EPE. The decision curve analysis was used to evaluate the net clinical benefit. Finally, a regression tree was employed to identify three risk categories to assist urologists in selecting candidates for nerve-sparing, incremental nerve sparing and non-nerve-sparing surgery.// Results: Overall, data from 3169 hemi-prostates were considered, after the exclusion of prostatic lobes with no biopsy-documented tumor. EPE was present on final pathology in 1,094 (34%) cases. Among these, MRI was able to predict EPE correctly in 568 (52%) cases. A model including PSA, maximum diameter of the index lesion, presence of EPE on MRI, highest ISUP grade in the ipsilateral hemi-prostate, and percentage of positive cores in the ipsilateral hemi-prostate achieved an AUC of 81% after internal validation. Overall, 566, 577, and 2,026 observations fell in the low-, intermediate- and high-risk groups for EPE, as identified by the regression tree. The EPE rate across the groups was: 5.1%, 14.9%, and 48% for the low-, intermediate- and high-risk group, respectively.// Conclusion: In this study we present an update of the first side-specific MRI-based nomogram for the prediction of extraprostatic extension together with updated risk categories to help clinicians in deciding on the best approach to nerve-preservation

Unilateral Pelvic Lymph Node Dissection in Prostate Cancer Patients Diagnosed in the Era of Magnetic Resonance Imaging-targeted Biopsy: A Study That Challenges the Dogma

PURPOSE: Bilateral extended pelvic lymph node dissection at the time of radical prostatectomy is the current standard of care if pelvic lymph node dissection is indicated; often, however, pelvic lymph node dissection is performed in pN0 disease. With the more accurate staging achieved with magnetic resonance imaging-targeted biopsies for prostate cancer diagnosis, the indication for bilateral extended pelvic lymph node dissection may be revised. We aimed to assess the feasibility of unilateral extended pelvic lymph node dissection in the era of modern prostate cancer imaging. MATERIALS AND METHODS: We analyzed a multi-institutional data set of men with cN0 disease diagnosed by magnetic resonance imaging-targeted biopsy who underwent prostatectomy and bilateral extended pelvic lymph node dissection. The outcome of the study was lymph node invasion contralateral to the prostatic lobe with worse disease features, ie, dominant lobe. Logistic regression to predict lymph node invasion contralateral to the dominant lobe was generated and internally validated. RESULTS: Overall, data from 2,253 patients were considered. Lymph node invasion was documented in 302 (13%) patients; 83 (4%) patients had lymph node invasion contralateral to the dominant prostatic lobe. A model including prostate-specific antigen, maximum diameter of the index lesion, seminal vesicle invasion on magnetic resonance imaging, International Society of Urological Pathology grade in the nondominant side, and percentage of positive cores in the nondominant side achieved an area under the curve of 84% after internal validation. With a cutoff of contralateral lymph node invasion of 1%, 602 (27%) contralateral pelvic lymph node dissections would be omitted with only 1 (1.2%) lymph node invasion missed. CONCLUSIONS: Pelvic lymph node dissection could be omitted contralateral to the prostate lobe with worse disease features in selected patients. We propose a model that can help avoid contralateral pelvic lymph node dissection in almost one-third of cases

Follow-up in Active Surveillance for Prostate Cancer: Strict Protocol Adherence Remains Important for PRIAS-ineligible Patients

Contains fulltext : 207889.pdf (publisher's version ) (Open Access

Multiparametric Magnetic Resonance Imaging Should Be Preferred Over Digital Rectal Examination for Prostate Cancer Local Staging and Disease Risk Classification

Contains fulltext : 231174.pdf (Publisher’s version ) (Closed access

External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy

Contains fulltext : 220081.pdf (Publisher’s version ) (Closed access

Improved depth perception with three-dimensional auxiliary display and computer generated three-dimensional panoramic overviews in robot-assisted laparoscopy

In comparison to open surgery, endoscopic surgery offers impaired depth perception and narrower field-of-view. To improve depth perception, the Da Vinci robot offers three-dimensional (3-D) video on the console for the surgeon but not for assistants, although both must collaborate. We improved the shared perception of the whole surgical team by connecting live 3-D monitors to all three available Da Vinci generations, probed user experience after two years by questionnaire, and compared time measurements of a predefined complex interaction task performed with a 3-D monitor versus two-dimensional. Additionally, we investigated whether the complex mental task of reconstructing a 3-D overview from an endoscopic video can be performed by a computer and shared among users. During the study, 925 robot-assisted laparoscopic procedures were performed in three hospitals, including prostatectomies, cystectomies, and nephrectomies. Thirty-one users participated in our questionnaire. Eighty-four percent preferred 3-D monitors and 100% reported spatial-perception improvement. All participating urologists indicated quicker performance of tasks requiring delicate collaboration (e.g., clip placement) when assistants used 3-D monitors. Eighteen users participated in a timing experiment during a delicate cooperation task in vitro. Teamwork was significantly (40%) faster with the 3-D monitor. Computer-generated 3-D reconstructions from recordings offered very wide interactive panoramas with educational value, although the present embodiment is vulnerable to movement artifacts

External Validation of Models Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients

Background: Multiple statistical models predicting lymph node involvement (LNI) in prostate cancer (PCa) exist to support clinical decision-making regarding extended pelvic lymph node dissection (ePLND). Objective: To validate models predicting LNI in Dutch PCa patients. Design, setting, and participants: Sixteen prediction models were validated using a patient cohort of 1001 men who underwent ePLND. Patient characteristics included serum prostate specific antigen (PSA), cT stage, primary and secondary Gleason scores, number of biopsy cores taken, and number of positive biopsy cores. Outcome measurements and statistical analysis: Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Calibration plots were used to visualize over- or underestimation by the models. Results and limitations: LNI was identified in 276 patients (28%). Patients with LNI had higher PSA, higher primary Gleason pattern, higher Gleason score, higher number of nodes harvested, higher number of positive biopsy cores, and higher cT stage compared to patients without LNI. Predictions generated by the 2012 Briganti nomogram (AUC 0.76) and the Memorial Sloan Kettering Cancer Center (MSKCC) web calculator (AUC 0.75) were the most accurate. Calibration had a decisive role in selecting the most accurate models because of overlapping confidence intervals for the AUCs. Underestimation of LNI probability in patients had a predicted probability of <20%. The omission of model updating was a limitation of the study. Conclusions: Models predicting LNI in PCa patients were externally validated in a Dutch patient cohort. The 2012 Briganti and MSKCC nomograms were identified as the most accurate prediction models available. Patient summary: In this report we looked at how well models were able to predict the risk of prostate cancer spreading to the pelvic lymph nodes. We found that two models performed similarly in predicting the most accurate probabilities. Nomograms developed by Briganti et al and the Memorial Sloan Kettering Cancer Center were best at predicting lymph node involvement in prostate cancer patients. These models support clinical decision-making on whether to perform pelvic lymph node dissection