Thermal Ablation for Small Renal Masses: Identifying Anthropometric Factors for Predicting Perioperative and Oncological Outcomes.

OBJECTIVES: To test for specific anthropometric parameters to predict perioperative outcomes after thermal ablation (TA) for renal cell carcinoma (RCC). MATERIALS AND METHODS: Retrospective single center (2008-2022) analysis of 538 T1a-b RCC patients treated with TA. We tested for specific anthropometric parameters, namely skin to tumor distance (STTD), perirenal fat thickness (PFT), median psoas muscle axial area (PMAA) and median paravertebral muscle axial area (PVMAA), to predict TRIFECTA achievement: (1) absence of CLAVIEN-DINDO≥ 3 complications; (2) complete ablation; (3) absence of ≥ 30% decrease in eGFR. Univariable (ULRM) and multivariable logistic regression models (MLRM) were used for testing TRIFECTA achievement. RESULTS: Overall, 103 patients (19%) did not achieve TRIFECTA. Of all anthropometric factors, only lower PMAA was associated with no TRIFECTA achievement (10 vs. 11 cm2, P = .02). However, ULRMs and MLRMs did not confirmed the aforementioned association. We than tested for the 3 specific TRIFECTA items. In separate ULRM and MLRM predicting incomplete ablation, both continuously coded STTD (Odds Ratio [OR]: 1.02; CI: 1.01-1.03; P = .02) and STTD strata (STTD > 10 cm; OR: 2.1; CI: 1.1-4.1; P = .03) achieved independent predictor status. Conversely, in separate ULRM and MLRM predicting CLAVIEN-DINDO ≥3 complications, both continuously coded PFT (OR: 1.04; CI: 1.01-1.07; P = .01) and PFT strata (PFT ≥ 14 mm; OR: 3.3; CI: 1.6-10.2; P = .003) achieved independent predictor status. Last, none of the anthropometric parameters were associated with eGFR decrease ≥ 30%. CONCLUSION: None of the tested anthropometric parameters predicted TRIFECTA achievement. However, when the 3 specific TRIFECTA items were tested, STTD and PFT were associated with, respectively, incomplete ablation and CLAVIEN-DINDO ≥ 3 complications.

Telemedicine and social media: A contemporary analysis of the most shared content by internet users.

OBJECTIVE: To evaluate the telemedicine information published on the most popular social media platforms, during the second year of the COVID-19 pandemic. METHODS: We queried the BuzzSumo tool to identify related telemedicine article links that were shared most on social media, from February 2021 to February 2022. The PEMAT-P was used for the quality assessment of the most shared links. RESULTS: 125 links were eligible for the analysis. Facebook was the most used social media platform for sharing articles (median engagement: 1000). Most of the articles were published by magazines (n = 82, 65.6%) and the main topic addressed was general information (n = 49, 39.2%). In the subgroup analyses of the 34 most shared articles, Facebook was the most used social media platform (median engagement:1950), most of the articles were published by magazines (n = 24, 70.6%), whereas the main topic addressed was the prescription of the abortion pill (n = 9, 26.5%). According to the PEMAT-P tool, the median understandability and actionability score was 63.8 and 20%, respectively. CONCLUSIONS: The interest in telemedicine has increased all over the world, as evidenced by the high engagement in social media articles, recorded during the last year. However, the access to digital health services is still limited, the information provided is often not verified by an official entity and unable to fill the digital divide exacerbated by COVID 19 pandemic crisis. Hence, health policy should be developed or modified to ensure a more egalitarian Internet access for all citizens. Official medical institutions should standardize telemedicine regulation and online content to reduce the widespread of misleading information.

Extreme-hypofractionated RT with concomitant boost to the DIL in PCa: a 5-year update on oncological and patient-reported outcomes for the phase II trial "GIVE ME FIVE".

AIM: The present work reports updated oncological results and patients-reported outcomes at 5 years of phase II trial "Short-term high precision RT for early prostate cancer with SIB to the dominant intraprostatic lesion (DIL) for patients with early-stage PCa". METHODS: Data from patients enrolled within AIRC IG-13218 (NCT01913717) trial were analyzed. Clinical and GU/GI toxicity assessment and PSA measurements were performed every 3 months for at least 2 years after RT end. QoL of enrolled patients was assessed by IPSS, EORTC QLQ-C30, EORTC QLQ-PR25, and IIEF-5. Patients' score changes were calculated at the end of RT and at 1, 12, and 60 months after RT. RESULTS: A total of 65 patients were included. At a median follow-up of 5 years, OS resulted 86%. Biochemical and clinical progression-free survival at 5 years were 95%. The median PSA at baseline was 6.07 ng/ml, while at last follow-up resulted 0.25 ng/ml. IPSS showed a statistically significant variation in urinary function from baseline (p = 0.002), with the most relevant deterioration 1 month after RT, with a recovery toward baseline at 12 months (p ≤ 0.0001). A numerical improvement in QoL according to the EORTC QLQ-C30 has been reported although not statistically significant. No change in sexual activity was recorded after RT. CONCLUSIONS: The study confirms that extreme hypofractionation with a DIL boost is safe and effective, with no severe effects on the QoL. The increasing dose to the DIL does not worsen the RT toxicity, thus opening the possibility of an even more escalated treatment.

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS: • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

Time to progression is the main predictor of survival in patients with high-risk nonmuscle invasive bladder cancer: Results from a machine learning-based analysis of a large multi-institutional database.

BACKGROUND: In patients affected by high-risk nonmuscle invasive bladder cancer (HR-NMIBC) progression to muscle invasive status is considered as the main indicator of local treatment failure. We aimed to investigate the effect of progression and time to progression on overall survival (OS) and to investigate their validity as surrogate endpoints. METHODS: A total of 1,510 patients from 18 different institutions treated for T1 high grade NMIBC, followed by a secondary transurethral resection and BCG intravesical instillation. We relied on random survival forest (RSF) to rank covariates based on OS prediction. Cox's regression models were used to quantify the effect of covariates on mortality. RESULTS: During a median follow-up of 49.0 months, 485 (32.1%) patients progressed to MIBC, while 163 (10.8%) patients died. The median time to progression was 82 (95%CI: 78.0-93.0) months. In RSF time-to-progression and age were the most predictive covariates of OS. The survival tree defined 5 groups of risk. In multivariable Cox's regression models accounting for progression status as time-dependent covariate, shorter time to progression (as continuous covariate) was associated with longer OS (HR: 9.0, 95%CI: 3.0-6.7; P < 0.001). Virtually same results after time to progression stratification (time to progression ≥10.5 months as reference). CONCLUSION: Time to progression is the main predictor of OS in patients with high risk NMIBC treated with BCG and might be considered a coprimary endpoint. In addition, models including time to progression could be considered for patients' stratification in clinical practice and at the time of clinical trials design.

Multivariable model versus AJCC staging system: cancer-specific survival predictions in adrenocortical carcinoma.

We developed a novel contemporary population-based model for predicting cancer-specific survival (CSS) in adrenocortical carcinoma (ACC) patients and compared it with the established 8th edition of the American Joint Committee on Cancer staging system (AJCC). Within the Surveillance, Epidemiology, and End Results database (2004-2020), we identified 1056 ACC patients. Univariable Cox regression model addressed CSS. Harrell's concordance index (C-index) quantified accuracy after 2000 bootstrap resamples for internal validation. The multivariable Cox regression model included the most informative, statistically significant predictors. Calibration and decision curve analyses (DCAs) tested the multivariable model as well as AJCC in head-to-head comparisons. Age at diagnosis (>60 vs ≤60 years), surgery, T, N, and M stages were included in the multivariable model. Multivariable model C-index for 3-year CSS prediction was 0.795 vs 0.757 for AJCC. Multivariable model outperformed AJCC in DCAs for the majority of possible CSS-predicted values. Both models exhibited similar calibration properties. Finally, the range of the multivariable model CSS predicted probabilities raged 0.02-75.3% versus only four single AJCC values, specifically 73.2% for stage I, 69.7% for stage II, 46.6% for stage III, and 15.5% for stage IV. The greatest benefit of the multivariable model-generated CSS probabilities applied to AJCC stage I and II patients. The multivariable model was more accurate than AJCC staging when CSS predictions represented the endpoint. Additionally, the multivariable model outperformed AJCC in DCAs. Finally, the AJCC appeared to lag behind the multivariable model when discrimination addressed AJCC stage I and II patients.

A Phase 3 Prospective Randomized Trial to Evaluate the Impact of Augmented Reality During Robot-assisted Radical Prostatectomy on the Rates of Postoperative Surgical Margins: A Clinical Trial Protocol.

We designed a phase 3, prospective, randomized trial to evaluate the impact of augmented reality and augmented reality frozen section analysis in reducing the rates of positive surgical margins after robot-assisted radical prostatectomy.

First-in-human validation of a DROP-IN ß-probe for robotic radioguided surgery: defining optimal signal-to-background discrimination algorithm.

PURPOSE: In radioguided surgery (RGS), radiopharmaceuticals are used to generate preoperative roadmaps (e.g., PET/CT) and to facilitate intraoperative tracing of tracer avid lesions. Within RGS, there is a push toward the use of receptor-targeted radiopharmaceuticals, a trend that also has to align with the surgical move toward minimal invasive robotic surgery. Building on our initial ex vivo evaluation, this study investigates the clinical translation of a DROP-IN ß probe in robotic PSMA-guided prostate cancer surgery. METHODS: A clinical-grade DROP-IN ß probe was developed to support the detection of PET radioisotopes (e.g., 68 Ga). The prototype was evaluated in 7 primary prostate cancer patients, having at least 1 lymph node metastases visible on PSMA-PET. Patients were scheduled for radical prostatectomy combined with extended pelvic lymph node dissection. At the beginning of surgery, patients were injected with 1.1 MBq/kg of [68Ga]Ga-PSMA. The ß probe was used to trace PSMA-expressing lymph nodes in vivo. To support intraoperative decision-making, a statistical software algorithm was defined and optimized on this dataset to help the surgeon discriminate between probe signals coming from tumors and healthy tissue. RESULTS: The DROP-IN ß probe helped provide the surgeon with autonomous and highly maneuverable tracer detection. A total of 66 samples (i.e., lymph node specimens) were analyzed in vivo, of which 31 (47%) were found to be malignant. After optimization of the signal cutoff algorithm, we found a probe detection rate of 78% of the PSMA-PET-positive samples, a sensitivity of 76%, and a specificity of 93%, as compared to pathologic evaluation. CONCLUSION: This study shows the first-in-human use of a DROP-IN ß probe, supporting the integration of ß radio guidance and robotic surgery. The achieved competitive sensitivity and specificity help open the world of robotic RGS to a whole new range of radiopharmaceuticals.

The European Network for the Study of Adrenal Tumors staging system (2015): a United States validation.

OBJECTIVE: To test the ability of the 2015 modified version of the European Network for the Study of Adrenal Tumors-staging system (mENSAT) in predicting cancer specific-mortality (CSM), as well as overall mortality (OM) in adrenocortical carcinoma (ACC) patients of all stages, in a large scale, and contemporary United States cohort. METHODS: We relied on the Surveillance, Epidemiology, and End Results (SEER) database (2004-2020) to test the accuracy and calibration of the mENSAT and subsequently compared it to the 8th edition of the American Joint Committee on Cancer-staging system (AJCC). RESULTS: In 858 ACC patients, mENSAT accuracy was 74.7% for three-year CSM predictions and 73.8% for three-year OM predictions. The maximum departures from ideal predictions in mENSAT were +17.2% for CSM and +11.8% for OM. Conversely, AJCC accuracy was 74.5% for three-year CSM predictions and 73.5% for three-year OM predictions. The maximum departures from ideal predictions in AJCC were -6.7% for CSM and -7.1% for OM. CONCLUSION: The accuracy of mENSAT is virtually the same as that of AJCC in predicting CSM (74.7 vs. 74.5%) and OM (73.7 vs. 73.5%). However, calibration is lower for mENSAT than for AJCC. In consequence, no obvious benefit appears to be associated with the use of mENSAT relative to AJCC in United States ACC patients.

Oncological Outcomes of Thulium-Yttrium-Aluminum-Garnet (Tm:YAG) Laser Ablation for Penile Cancer.

OBJECTIVE: To report oncological outcomes after thulium-yttrium-aluminum-garnet (Tm:YAG) laser ablation for penile cancer patients. MATERIALS AND METHODS: We retrospectively analyzed 71 patients with ≤cT1 penile cancer (2013-2022). All patients underwent Tm:YAG ablation with a RevoLix 200W continuous-wave laser. First, Kaplan-Meier plots and multivariable Cox regression models tested local tumor recurrence rates. Second, Kaplan-Meier plots tested progression-free survival (≥T3 and/or N1-3 and/or M1). RESULTS: Median (interquartile range) follow-up time was 38 (22-58) months. Overall, 33 (50.5%) patients experienced local tumor recurrence. Specifically, 19 (29%) vs 9 (14%) vs 5 (7.5%) patients had 1 vs 2 vs 3 recurrences over time. In multivariable Cox regression models, a trend for higher recurrence rates was observed for G3 tumors (hazard ratio:6.1; P = .05), relative to G1. During follow-up, 12 (18.5%) vs 4 (6.0%) vs 2 (3.0%) men were retreated with 1 vs 2 vs 3 Tm:YAG laser ablations. Moreover, 11 (17.0%) and 3 (4.5%) patients underwent glansectomy and partial/total penile amputation. Last, 5 (7.5%) patients experienced disease progression. Specifically, TNM stage at the time of disease progression was: (1) pT3N0; (2) pT2N2; (3) pTxN3; (4) pT1N1 and (5) pT3N3, respectively. CONCLUSION: Tm:YAG laser ablation provides similar oncological results as those observed by other penile-sparing surgery procedures. In consequence, Tm:YAG laser ablation should be considered a valid alternative for treating selected penile cancer patients.

Advanced Age Impacts Survival After Radical Nephroureterectomy for Upper Tract Urothelial Carcinoma.

INTRODUCTION: Upper tract urothelial carcinoma is rare but has a poor prognosis. Prognostic factors have been extensively studied in order to provide the best possible management for patients. We have aimed to investigate commonly available factors predictive of recurrence and survival in this patient population at high risk of death and recurrence, with an emphasis on the effects of age (using a cutoff of 70 years) on survival outcomes. PATIENTS AND METHODS: From 1387 patients with clinically nonmetastatic upper tract urothelial carcinoma treated with radical nephroureterectomy at 21 academic hospital centers between 2005 and 2021, 776 patients were eligible and included in the study. Univariable and multivariable Cox regression models were built to evaluate the independent prognosticators for intravesical and extravesical recurrence, overall survival, and cancer-specific survival according to age groups. A P value of <.05 was considered statistically significant. RESULTS: We did not find an association between groups aged <70 and >70 years old and preoperatively clinical or histopathological characteristics. Kaplan-Meier analysis was found no statistical significance between the 2 age groups in terms of intravesical or extravesical recurrence (P = .09 and P = .57). Overall survival (P = .0001) and cancer-specific survival (P = .0001) have been found to be statistically significantly associated with age as independent predictors (confounding factors: gender, tumor size, tumor side, clinical T stage, localization, preoperative hydronephrosis, tumor localization, type of surgery, multifocality of the tumor, pathological grade, lymphovascular invasion, concomitant CIS, lymph node status, necrosis, or history of previous bladder cancer). CONCLUSION: This research confirms that patients aged 70 and above who undergo radical nephroureterectomy may have worse outcomes compared to younger patients, older patients needing an improved care and management of UTUC to improve their outcomes in the setting of an increase in this aged population group.

Investigating Nutritional and Inflammatory Status as Predictive Biomarkers in Oligoreccurent Prostate Cancer-A RADIOSA Trial Preliminary Analysis.

(1) Background: In the RADIOSA phase II randomized clinical trial (NCT03940235), the biology task entails the identification of predictive and prognostic biomarkers in the context of oligorecurrent, castration-sensitive prostate cancer in order to distinguish polymetastatic from oligometastatic disease. This may lay the groundwork for personalized treatments for those patients who could really benefit from metastasis-directed therapies. (2) Methods: Oligorecurrent PCa pts with three or fewer bone or lymph nodal localizations were randomized 1:1 to receive SBRT alone (arm A) or SBRT + 6 months of ADT (arm B). Common serum-derived biomarkers were collected at baseline, and at 3 months after RT. The prognostic nutritional index, an immune and nutrition-based prognostic score, and the controlling nutritional status (CONUT) score, a scoring system for evaluating patient's nutritional status, were calculated in accordance with the body of available literature. As inflammatory indicators, neutrophil-lymphocyte ratio (NLR) and the NLR-albumin ratio (NLRAR) were assessed. Changes in these parameters between baseline and the 3-month timepoint were evaluated both in absolute and relative values. Changes in these parameters between baseline and the 3-month timepoint were evaluated. Significant differences in the trend of these parameters were assessed using the non-parametric Wilcoxon rank-sum test. A network analysis to analyze the relationships between different features stratifying patients according to the arm of study and site of metastases was performed. (3) Results: The current analysis comprised 88 patients (45 arm A, SBRT only, and 43 arm B, SBRT + ADT). When patients were stratified by ADT administration, cholesterol values showed an increasing trend in the group receiving ADT (p = 0.005) which was no longer significant at 1 year. When patients were stratified by site of metastases (52 lymph nodal, 29 bone localizations), the value of NLR was found to be increased in patients with bone localizations (p < 0.05). In addition, the network analysis showed that BMI and NRI are strongly and directly linked for patients at baseline and that this correlation is no longer found at three months. Finally, when patients were divided according to time from surgery to oligorecurrence (enrollment) the patients with a longer time (>6.7 years) showed an increase in CONUT score from baseline. All the other nutritional and inflammatory scores or parameters investigated in the present analysis showed no statistically significant differences at baseline, three months, 1 year, and in absolute change. (4) Conclusions: The nutritional and inflammatory parameters do not seem to represent valuable candidates for possible use in clinical decision making in our cohort of patients and a reliable biological characterization of the oligometastatic state in prostate cancer still seems far from being achieved. Ongoing molecular analysis will show if there is a role of mutational landscape in the definition of the oligometastatic state.

Multiparametric MRI-based 5-year Risk Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy.

Background Current predictive tools to estimate the risk of biochemical recurrence (BCR) after treatment of prostate cancer do not consider multiparametric MRI (mpMRI) information. Purpose To develop a risk prediction tool that considers mpMRI findings to assess the risk of 5-year BCR after radical prostatectomy. Materials and Methods In this retrospective single-center analysis in 1459 patients with prostate cancer who underwent mpMRI before radical prostatectomy (in 2012-2015), the outcome of interest was 5-year BCR (two consecutive prostate-specific antigen [PSA] levels > 0.2 ng/mL [0.2 µg/L]). Patients were randomly divided into training (70%) and test (30%) sets. Kaplan-Meier plots were applied to the training set to estimate survival probabilities. Multivariable Cox regression models were used to test the relationship between BCR and different sets of exploratory variables. The C-index of the final model was calculated for the training and test sets and was compared with European Association of Urology, University of California San Francisco Cancer of the Prostate Risk Assessment, Memorial Sloan-Kettering Cancer Center, and Partin risk tools using the partial likelihood ratio test. Five risk categories were created. Results The median duration of follow-up in the whole cohort was 59 months (IQR, 32-81 months); 376 of 1459 (25.8%) patients had BCR. A multivariable Cox regression model (referred to as PIPEN, and composed of PSA density, International Society of Urological Pathology grade group, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extraprostatic extension score, nodes) fitted to the training data yielded a C-index of 0.74, superior to that of other predictive tools (C-index 0.70 for all models; P ≤ .01) and a median higher C-index on 500 test set replications (C-index, 0.73). Five PIPEN risk categories were identified with 5-year BCR-free survival rates of 92%, 84%, 71%, 56%, and 26% in very low-, low-, intermediate-, high-, and very high-risk patients, respectively (all P < .001). Conclusion A five-item model for predicting the risk of 5-year BCR after radical prostatectomy for prostate cancer was developed and internally verified, and five risk categories were identified. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Aguirre and Ortegón in this issue.

Diagnosis of prostate cancer with magnetic resonance imaging in men treated with 5-alpha-reductase inhibitors.

PURPOSE: The primary aim of this study was to evaluate if exposure to 5-alpha-reductase inhibitors (5-ARIs) modifies the effect of MRI for the diagnosis of clinically significant Prostate Cancer (csPCa) (ISUP Gleason grade ≥ 2). METHODS: This study is a multicenter cohort study including patients undergoing prostate biopsy and MRI at 24 institutions between 2013 and 2022. Multivariable analysis predicting csPCa with an interaction term between 5-ARIs and PIRADS score was performed. Sensitivity, specificity, and negative (NPV) and positive (PPV) predictive values of MRI were compared in treated and untreated patients. RESULTS: 705 patients (9%) were treated with 5-ARIs [median age 69 years, Interquartile range (IQR): 65, 73; median PSA 6.3 ng/ml, IQR 4.0, 9.0; median prostate volume 53 ml, IQR 40, 72] and 6913 were 5-ARIs naïve (age 66 years, IQR 60, 71; PSA 6.5 ng/ml, IQR 4.8, 9.0; prostate volume 50 ml, IQR 37, 65). MRI showed PIRADS 1-2, 3, 4, and 5 lesions in 141 (20%), 158 (22%), 258 (37%), and 148 (21%) patients treated with 5-ARIs, and 878 (13%), 1764 (25%), 2948 (43%), and 1323 (19%) of untreated patients (p < 0.0001). No difference was found in csPCa detection rates, but diagnosis of high-grade PCa (ISUP GG ≥ 3) was higher in treated patients (23% vs 19%, p = 0.013). We did not find any evidence of interaction between PIRADS score and 5-ARIs exposure in predicting csPCa. Sensitivity, specificity, PPV, and NPV of PIRADS ≥ 3 were 94%, 29%, 46%, and 88% in treated patients and 96%, 18%, 43%, and 88% in untreated patients, respectively. CONCLUSIONS: Exposure to 5-ARIs does not affect the association of PIRADS score with csPCa. Higher rates of high-grade PCa were detected in treated patients, but most were clearly visible on MRI as PIRADS 4 and 5 lesions. TRIAL REGISTRATION: The present study was registered at ClinicalTrials.gov number: NCT05078359.

Penile-Sparing Surgery for Tumour Recurrence after Previous Glansectomy/Partial Penectomy: Treatment Feasibility and Oncological Outcomes.

We tested the feasibility and oncological outcomes after penile-sparing surgery (PSS) for local recurrent penile cancer after a previous glansectomy/partial penectomy. We retrospectively analysed 13 patients (1997-2022) with local recurrence of penile cancer after a previous glansectomy or partial penectomy. All patients underwent PSS: circumcision, excision, or laser ablation. First, technical feasibility, treatment setting, and complications (Clavien-Dindo) were recorded. Second, Kaplan-Meier plots depicted overall and local recurrences over time. Overall, 11 (84.5%) vs. 2 (15.5%) patients were previously treated with glansectomy vs. partial penectomy. The median (IQR) time to disease recurrence was 56 (13-88) months. Six (46%) vs. two (15.5%) vs. five (38.5%) patients were treated with, respectively, local excision vs. local excision + circumcision vs. laser ablation. All procedures, except one, were performed in an outpatient setting. Only one Clavien-Dindo 2 complication was recorded. The median follow-up time was 41 months. Overall, three (23%) vs. four (30.5%) patients experienced local vs. overall recurrence, respectively. All local recurrences were safely treated with salvage surgery. In conclusion, we reported the results of a preliminary analysis testing safety, feasibility, and early oncological outcomes of PSS procedures for patients with local recurrence after previous glansectomy or partial penectomy. Stronger oncological outcomes should be tested in other series to optimise patient selection.

Predicting Peri-Operative Outcomes in Patients Treated with Percutaneous Thermal Ablation for Small Renal Masses: The SuNS Nephrometry Score.

Our objective was to develop a new, simple, and ablation-specific nephrometry score to predict peri-operative outcomes and to compare its predictive accuracy to PADUA and RENAL scores. Overall, 418 patients were treated with percutaneous thermal ablation (microwave and radiofrequency) between 2008 and 2021. The outcome of interest was trifecta status (achieved vs. not achieved): incomplete ablation or Clavien-Dindo ≥ 3 complications or postoperative estimated glomerular filtration rate decrease ≥ 30%. First, we validated the discrimination ability of the PADUA and RENAL scoring systems. Second, we created and internally validated a novel scoring (SuNS) system, according to multivariable logistic regression models. The predictive accuracy of the model was tested in terms of discrimination and calibration. Overall, 89 (21%) patients did not achieve trifecta. PADUA and RENAL scores showed poor ability to predict trifecta status (c-indexes 0.60 [0.53-0.67] and 0.62 [0.55-0.69], respectively). We, therefore, developed the SuNS model (c-index: 0.74 [0.67-0.79]) based on: (1) contact surface area; (2) nearness to renal sinus or urinary collecting system; (3) tumour diameter. Three complexity classes were created: low (3-4 points; 11% of no trifecta) vs. moderate (5-6 points; 30% of no trifecta) vs. high (7-8 points; 65% of no trifecta) complexity. Limitations include the retrospective and single-institution nature of the study. In conclusion, we developed an immediate, simple, and reproducible ablation-specific nephrometry score (SuNS) that outperformed PADUA and RENAL nephrometry scores in predicting peri-operative outcomes. External validation is required before daily practice implementation.

What is the best time for postoperative radiation therapy in pN1 prostate cancer?

We retrospectively compared long-term biochemical recurrence rates (BCR) in pN1 PCa patients that underwent adjuvant radiotherapy (aRT) vs. no aRT/early salvage (esRT) after robot-assisted radical prostatectomy and extended pelvic lymphadenectomy. All PCa pN1 M0 patients treated at a single high-volume center between 2010 and 2020 were analyzed. Patients with <10 LNs yield, or >10 positive LNs, or persistently detectable PSA after RARP were excluded. Kaplan-Meier (KM) plots depicted BCR rates. Multivariable Cox regression models (MCRMs) focused on predictors of BCR. The cumulative incidence plot depicted BCR rates after propensity score (PS) matching (ratio 1:1). 220 pN1 patients were enrolled, 133 (60.4%) treated with aRT and 87 (39.6%) with no-aRT/esRT. aRT patients were older, with higher rates of postoperative ISUP grade group 4-5, and higher rates of pT3b stage. The actuarial BCR was similar (aRT 39.8% vs. no-aRT/esRT 40.2%; p=1). Median time to BCR was 62 vs. 38 months in aRT vs. no-aRT/esRT patients (p=0.001). In MCRMs, patients managed with no-aRT/esRT were associated with higher rates of BCR over time (hazard ratio [HR]: 3.27, p<0.001). ISUP grade group 5 (HR: 2.18, p<0.01) was an independent predictor of BCR. In PS-matched cumulative incidence plots, the BCR rate was significantly higher in the aRT group (76.4 vs. 40.4%; p<0.01). Patients managed with no-aRT/esRT experienced BCR approximately two years before the aRT group. Despite, the important BCR benefit after aRT, this treatment strategy is underused in daily practice.

Assessment of the VENUSS and GRANT Models for Individual Prediction of Cancer-specific Survival in Surgically Treated Nonmetastatic Papillary Renal Cell Carcinoma.

Background: Guidelines recommend VENUSS and GRANT models for the prediction of cancer control outcomes after nephrectomy for nonmetastatic papillary renal cell carcinoma (pRCC). Objective: To test the ability of VENUSS and GRANT models to predict 5-yr cancer-specific survival in a North American population. Design setting and participants: For this retrospective study, we identified 4184 patients with unilateral surgically treated nonmetastatic pRCC in the Surveillance, Epidemiology, and End Results database (2004-2019). Outcome measurements and statistical analysis: The original VENUSS and GRANT risk categories were applied to predict 5-yr cancer-specific survival. A cross-validation method was used to test the accuracy and calibration of the models and to conduct decision curve analyses for the study cohort. Results and limitations: The VENUSS and GRANT categories represented independent predictors of cancer-specific mortality. On cross-validation, the accuracy of the VENUSS and GRANT risk categories was 0.73 and 0.65, respectively. Both models showed good calibration and performed better than random predictions in decision curve analysis. Limitations include the retrospective nature of the study and the absence of a central pathological review. Conclusion: VENUSS risk categories fulfilled prognostic model criteria for predicting cancer-specific survival 5 yr after surgery in North American patients with nonmetastatic pRCC as recommended by guidelines. Conversely, GRANT risk categories did not. Thus, VENUSS risk categories represent an important tool for counseling, follow-up planning, and patient selection for appropriate adjuvant trials in pRCC. Patient summary: We tested the ability of two validated methods (VENUSS and GRANT) to predict death due to papillary kidney cancer in a North American population. The VENUSS risk categories showed good performance in predicting 5-year cancer-specific survival.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.

Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.