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Summary Background Partial nephrectomy (PN) is recommended as first-line treatment for cT1 stage kidney tumors because of a better renal function and probably a better overall survival than radical nephrectomy (RN). For larger tumors, PN has a controversial position due to lack of evidence showing good cancer control. The aim of this study was to compare the results of PN and RN in cT2a stage on overall survival and oncological results. Method A retrospective international multicenter study was conducted in the frame of the French kidney cancer research network (UroCCR). We considered all patients aged ≥ 18 years who underwent surgical treatment for localized renal cell carcinoma (RCC) stage cT2a (7.1–10 cm) between 2000 and 2014. Cox and Fine-Gray models were performed to analyze overall survival (OS), cancer specific survival (CSS) and cancer-free survival (CFS). Comparison between PN and RN was realized after an adjustment by propensity score considering predefined confounding factors: age, sex, tumor size, pT stage of the TNM classification, histological type, ISUP grade, ASA score. Results A total of 267 patients were included. OS at 3 and 5 years was 93.6% and 78.7% after PN and 88.0% and 76.2% after RN, respectively. CSS at 3 and 5 years was 95.4% and 80.2% after PN and 91.0% and 85.0% after RN. No significant difference between groups was found after propensity score adjustment for OS (HR 0.87, 95% CI: 0.37–2.05, P = 0.75), CSS (HR 0.52, 95% CI: 0.18–1.54, P = 0.24) and CFS (HR 1.02, 95% CI: 0.50–2.09, P = 0.96). Conclusion PN seems equivalent to RN for OS, CSS and CFS in cT2a stage kidney tumors. The risk of recurrence is probably more related to prognostic factors than the surgical technique. The decision to perform a PN should depend on technical feasibility rather than tumor size, both to imperative and elective situation. Level of evidence 4.

Background Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone. Objective We aimed at developing and validating a novel classification integrating clinical and radiological parameters. Design, setting, and participants A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified. Outcome measurements and statistical analysis Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel’s concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR. Results and limitations Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively). Conclusions We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools. Patient summary Our novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.

BackgroundUse of predictive models for the prediction of biochemical recurrence (BCR) is gaining attention for prostate cancer (PCa). Specifically, BCR occurs in approximately 20–40% of patients five years after radical prostatectomy (RP) and the ability to predict BCR may help clinicians to make better treatment decisions. We aim to investigate the accuracy of CAPRA score compared to others models in predicting the 3-year BCR of PCa patients.Material and MethodsA total of 5043 men who underwent RP were analyzed retrospectively. The accuracy of CAPRA score, Cox regression analysis, logistic regression, K-nearest neighbor (KNN), random forest (RF) and a densely connected feed-forward neural network (DNN) classifier were compared in terms of 3-year BCR predictive value. The area under the receiver operating characteristic curve was mainly used to assess the performance of the predictive models in predicting the 3 years BCR of PCa patients. Pre-operative data such as PSA level, Gleason grade, and T stage were included in the multivariate analysis. To measure potential improvements to the model performance due to additional data, each model was trained once more with an additional set of post-operative surgical data from definitive pathology.ResultsUsing the CAPRA score variables, DNN predictive model showed the highest AUC value of 0.7 comparing to the CAPRA score, logistic regression, KNN, RF, and cox regression with 0.63, 0.63, 0.55, 0.64, and 0.64, respectively. After including the post-operative variables to the model, the AUC values based on KNN, RF, and cox regression and DNN were improved to 0.77, 0.74, 0.75, and 0.84, respectively.ConclusionsOur results showed that the DNN has the potential to predict the 3-year BCR and outperformed the CAPRA score and other predictive models.

Background The combined role of multiparametric magnetic resonance imaging (mp-MRI), and magnetic resonance imaging (MRI)-targeted and concomitant systematic biopsies in the identification of prostate cancer (PCa) patients at a higher risk of adverse pathology at radical prostatectomy (RP) is still unclear. Objective To develop novel models to predict extracapsular extension (ECE), seminal vesicle invasion (SVI), or upgrading in patients diagnosed with MRI-targeted and concomitant systematic biopsies. Design, setting, and participants We included 614 men with clinical stage≤T2 at digital rectal examination who underwent MRI-targeted biopsy with concomitant systematic biopsy. Outcome measurements and statistical analyses Logistic regression analyses predicting ECE, SVI, and upgrading (ie, a shift from biopsy International Society of Urological Pathology grade group to any higher grade at RP) based on clinical variables with or without mp-MRI features and systematic biopsy information (the percentage of cores with grade group ≥2 PCa) were developed and internally validated. The area under the curve (AUC) was used to identify the models with the highest discrimination. Decision-curve analyses (DCAs) determined the net benefit associated with their use. Results and limitations Overall, 333 (54%), 88 (14%), and 169 (27%) patients had ECE, SVI, and upgrading at RP, respectively. The inclusion of mp-MRI data improved the discrimination of clinical models for ECE (67% vs 70%) and SVI (74% vs 76%). Models including mp-MRI, and MRI-targeted and concomitant systematic biopsy information achieved the highest AUC at internal validation for ECE (73%), SVI (81%), and upgrading (73%) and represented the basis for three risk calculators that yield the highest net benefit at DCA. Conclusions Not only mp-MRI and MRI-targeted sampling, but also concomitant systematic biopsies provide significant information to identify patients at a higher risk of adverse pathology. Although omitting systematic prostate sampling at the time of MRI-targeted biopsy might be associated with a reduced risk of detecting insignificant PCa and lower patient discomfort, it reduces the ability to accurately predict pathological features. Patient summary The combination of multiparametric magnetic resonance imaging (mp-MRI) with accurate biopsy information on MRI-targeted and systematic biopsies improves the accuracy of multivariable models based on clinical and mp-MRI data alone. Correct mp-MRI interpretation and proper extensive prostate sampling are both needed to predict adverse pathology accurately at radical prostatectomy.