E-pRSA: Embeddings Improve the Prediction of Residue Relative Solvent Accessibility in Protein Sequence.

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

Urinary PSA-ZINC biomarker outperforms standard of care in early detection of prostate cancer.

BACKGROUND: Urine is a promising biological fluid for prostate cancer (PCa) diagnostics due to its non-invasive collection and wide range of biomarkers. The aim of this study was to assess the role of urinary PSA (uPSA) and urinary Zinc (uZinc) as biomarkers for the diagnosis of PCa in combination with routine parameters of standard of care (SOC - blood PSA, abnormal DRE, age) and MRI in patients candidates for prostate biopsy. METHODS: Urine samples after prostatic massages were collected from men with suspected PCa scheduled for prostate biopsy. Quantification of uPSA was performed by ECLIA platform and confirmed by ELISA assay, while uZinc measurement was evaluated by ICP-MS and confirmed by colorimetric in vitro assay. Six multivariate logistic regression analysis were performed to assess diagnostic performance of uPSA and uZinc (urine), SOC and MRI alone, and combination of MRI+SOC, MRI+urine and SOC+MRI+urine. The discriminative power of the logistic models was assessed by calculating the area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: Two hundred thirty-eight patients were included in the analysis; 145 of them were diagnosed with PCa. Urine test showed a better discrimination of HS from CP, in respect of uPSA and uZinc alone, both for PCa of any grade and Gleason Score ≥7 (4+3) (AUC 0.804 and 0.823 respectively). ROC curve combining SOC+MRI+urine showed an AUC=0.882, that is statistically different from SOC or MRI alone, or MRI+SOC (P=0.0001, P=0.0001, and P=0.008 respectively). PCa risk algorithm designed considering SOC+MRI+urine results in potential reduction of 57% of unnecessary biopsies compared to the current standard parameters. CONCLUSIONS: The loss of uPSA and Zinc production and secretion during neoplastic transformation of the prostate could potentially represent a hallmark of PCa. Its combination with age, PSA and DRE, as well as with mpMRI could represent an interesting approach to improve the diagnostic accuracy of PCa.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

Alpha&ESMhFolds: A Web Server for Comparing AlphaFold2 and ESMFold Models of the Human Reference Proteome.

We develop a novel database Alpha&ESMhFolds which allows the direct comparison of AlphaFold2 and ESMFold predicted models for 42,942 proteins of the Reference Human Proteome, and when available, their comparison with 2,900 directly associated PDB structures with at least a structure to sequence coverage of 70%. Statistics indicate that good quality models tend to overlap with a TM-score >0.6 as long as some PDB structural information is available. As expected, a direct model superimposition to the PDB structure highlights that AlphaFold2 models are slightly superior to ESMFold ones. However, some 55% of the database is endowed with models overlapping with TM-score <0.6. This highlights the different outputs of the two methods. The database is freely available for usage at https://alpha-esmhfolds.biocomp.unibo.it/.

Ten-year functional and oncological outcomes of a prospective randomized controlled trial comparing laparoscopic versus robot-assisted radical prostatectomy.

BACKGROUND: Among prostate cancer (PCa) treatment options, mini-invasive surgical approaches have gained a wide diffusion in the last decades. The aim of this study was to present oncological, functional, and quality of life data after 10 years of follow-up of a prospective randomized controlled trial (RCT) (ISRCTN11552140) comparing robot-assisted radical prostatectomy (RARP) versus laparoscopic radical prostatectomy (LRP) for the treatment of PCa. METHODS: Patients with localized PCa were randomized to undergo LRP or RARP between January 2010 and January 2011. Functional (continence and potency) and oncological (prostate-specific antigen, biochemical recurrence [BCR] and BCR-free survival [BCRFS]) variables were evaluated. BCRFS curves were estimated by the Kaplan-Meier method and compared using the log-rank test. Machine learning partial least square-discriminant analysis (PLS-DA) was used to identify the variables characterizing more the patients who underwent RARP or LRP. RESULTS: Seventy-five of the originally enrolled 120 patients remained on follow-up for 10 years; 40 (53%) underwent RARP and 35 (47%) LRP. Continence and potency recovery rates did not show significant differences (p = 0.068 and p = 0.56, respectively), despite a Δ12% for continence and Δ8% for potency in favor of the robotic approach. However, the quality of continence (in terms of International Consultation on Incontinence Questionnaire-Short Form [ICIQ-SF] score) and erection (in terms of International Index of Erectile Function-5 [IIEF-5] score) was significantly better after 10 years in the robotic group (p = 0.02 and p < 0.001). PLS-DA revealed that LRP was characterized by the worst functional-related outcomes analyzing the entire follow-up period. Four (10%) and six (17%) patients experienced BCR in RARP and LRP groups, respectively (p = 0.36), with an overall 10-year BCR-free survival of 88% and 78% (p = 0.16). CONCLUSIONS: Comparable continence and potency rates were observed between RARP and LRP after a 10-year follow-up. However, the RARP group exhibited superior totally dry rate and erection quality. No difference in terms of oncological outcomes was found.

Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy.

Introduction: Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. Materials and Methods: In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. Results: A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. Conclusions: Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.

CoCoNat: A Deep Learning-Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences.

Coiled-coil domains (CCDs) are structural motifs observed in proteins in all organisms that perform several crucial functions. The computational identification of CCD segments over a protein sequence is of great importance for its functional characterization. This task can essentially be divided into three separate steps: the detection of segment boundaries, the annotation of the heptad repeat pattern along the segment, and the classification of its oligomerization state. Several methods have been proposed over the years addressing one or more of these predictive steps. In this protocol, we illustrate how to make use of CoCoNat, a novel approach based on protein language models, to characterize CCDs. CoCoNat is, at its release (August 2023), the state of the art for CCD detection. The web server allows users to submit input protein sequences and visualize the predicted domains after a few minutes. Optionally, precomputed segments can be provided to the model, which will predict the oligomerization state for each of them. CoCoNat can be easily integrated into biological pipelines by downloading the standalone version, which provides a single executable script to produce the output. Key features • Web server for the prediction of coiled-coil segments from a protein sequence. • Three different predictions from a single tool (segment position, heptad repeat annotation, oligomerization state). • Possibility to visualize the results online or to download the predictions in different formats for further processing. • Easy integration in automated pipelines with the local version of the tool.

Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer.

OBJECTIVES: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

Initial experience with robot-assisted ureteroscopy with Ily® Robot.

The aim of this study is to present the first Italian experience with robotic-assisted retrograde intrarenal surgery (rRIRS) using the Ily® platform. Procedures were performed for renal stones using the Ily® Robot (STERLAB, Vallauris, France), which is a ureteroscope holder with multiple degrees of freedom that can be controlled remotely through a wireless controller. In March 2023, consecutive patients with indications for rRIRS were included in the study. Demographic variables and stone characteristics were collected, and standard perioperative data were assessed. The one-month stone-free rate (SFR, i.e. no residual fragments) was evaluated using ultrasound. All participating surgeons filled out a Surgeons' Satisfaction Questionnaire (SSQ) based on a Likert-type scale. The questionnaire focused on: 1) ease of use; 2) ergonomics during renal cavity exploration; 3) stability during stone fragmentation. Among the patients, one had bilateral stones, while two had stones on the right side. The mean stone size was 13 mm. The average operative time was 70 minutes and the mean docking time was three minutes. No perioperative complications were recorded, and all patients were discharged on the first postoperative day. The one-month SFR was 100%. The SSQ scores were as follows: 1) ease of use: 4/5; 2) ergonomics: 5/5; 3) stability during stone fragmentation: 5/5. Based on the initial experience, the results indicate the feasibility, safety, and effectiveness of rRIRS. The ergonomic efficiency of the system was highly appreciated by the surgeons. While a cost-effectiveness analysis within clinical trials is necessary, rRIRS shows the potential for a more sustainable future for endoscopists and an improved workplace environment.

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.

CoCoNat: a novel method based on deep learning for coiled-coil prediction.

MOTIVATION: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. RESULTS: In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states. AVAILABILITY AND IMPLEMENTATION: CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat.

Radical cystectomy in bladder cancer patients previously treated for prostate cancer: Insights from a large European multicentric series.

INTRODUCTION: Previous radical prostatectomy (RP) for prostate cancer (PCa) might impair feasibility of radical cystectomy (RC) for bladder cancer (BCa). The current study addressed morbidity, operative time (OT), and length of stay (LOS) of RC, within the largest available series of patients with history of previous RP. MATERIALS AND METHODS: All patients previously submitted to RP for PCa and subsequently submitted to RC for BCa, at six high-volume European institutions between 2010 and 2019, were identified. Presence of either PCa or BCa metastases, RT as primary treatment for PCa, and palliative RC represented exclusion criteria. The quality criteria for accurate and comprehensive reporting of intra- and post-operative surgical outcomes, recommended by the European Association of Urology guidelines, were fulfilled. Multivariable logistic and Poisson regression analyses were performed. RESULTS: Overall, 140 RC patients with history of RP were identified. After RP, 69 (49%) patients received radiotherapy (RT) for PCa, either in adjuvant (n = 50, 36%) or salvage setting (n = 19, 13%). Median age-adjusted Charlson comorbidity index was 6 (IQR 5, 7). Median OT, estimated blood loss and LOS were, respectively, 300 min, 500 ml, and 16 days. Intra-operative transfusions rate was 47% (n = 65). One intra-operative complication occurred (EAUiaiC grade 2, perforation of the rectum managed with immediate repair). Eighty-two (59%) patients experienced a total of 107 post-operative complications during the hospital stay, and seven (5%) patients required hospital readmission. In multivariable regression analyses, RT for PCa was associated with higher risk of post-operative complications (odds ratio 1.82, p = 0.039), longer OT (incidence rate ratio 1.09, p < 0.001), and longer LOS (incidence rate ratio 1.24, p < 0.001). CONCLUSIONS: RC in patients with history of RP is feasible, albeit burdened by remarkable morbidity, even in centers of excellence. RT after RP for PCa portends worse surgical outcomes.

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study.

The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.

ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction Sites in Protein Sequences.

The knowledge of protein-protein interaction sites (PPIs) is crucial for protein functional annotation. Here we address the problem focusing on the prediction of putative PPIs considering as input protein sequences. The issue is important given the huge volume of protein sequences compared to experimental and/or computed structures. Taking advantage of protein language models, recently developed, and Deep Neural networks, here we describe ISPRED-SEQ, which overpasses state-of-the-art predictors addressing the same problem. ISPRED-SEQ is freely available for testing at https://ispredws.biocomp.unibo.it.

A prospective randomized controlled trial comparing target prostate biopsy alone approach vs. target plus standard in naïve patients with positive mpMRI.

BACKGROUND: In the era of mpMRI guided target fusion biopsy (FB), the role of concomitant standard biopsy (SB) in naïve patients still remains under scrutiny. The aim of this study was to compare the detection rate (DR) of clinically significant prostate cancer (csPCa) in biopsy naïve patients with positive mpMRI who underwent FB alone (Arm A) vs FB+SB (Arm B). Secondary objectives were to compare the incidence of complications, the overall PCa DR and the biopsy results with final pathological findings after robotic prostatectomy (RARP). METHODS: This is a single center prospective non-inferiority parallel two arms (1:1) randomized control trial (ISRCTN registry number ISRCTN60263108) which took place at San Luigi Gonzaga University Hospital, Orbassano (Turin, Italy) from 4/2019 to 10/2021. Eligible participants were all adults aged<75 years old, biopsy naïve, with serum PSA<15 ng/mL and positive mpMRI (Pi-Rads V.2>3). FB was performed under ultrasound guidance using the BioJet fusion system; four to six target samples were obtained for each index lesion. SB was performed in accordance with the protocol by Rodríguez-Covarrubias. RARP with total anatomical reconstruction was carried out when indicated. DR of PCa and csPCA (Gleason Score >7) were evaluated. Post-biopsy complications according to Clavien-Dindo were recorded. Concordance between biopsy and RARP pathological findings was evaluated. Fisher's Exact test and Mann-Whitney test were applied; furthermore, Logistic Principal Component Analysis (LogPCA) and Pearson's correlation method, in terms of correlation funnel plots, were performed to explore data in a multivariate way. RESULTS: 201 and 193 patients were enrolled in Arm A and B, respectively. csPCa DR was 60.2% vs. 60.6% in Arm A and B respectively (Δ 0.4%; P=0.93); whilst overall PCa DR was 63.7% vs. 71.0% (Δ 7.3%; P=0.12). However, in a target only setting, the addition of SB homolaterally to the index lesion reaching a non-inferior performance compared to the combined sampling (Δ PCa DR 3%). Although the differences of 7.3% in PCa DR, during RARP were registered similar nerve sparing rate (P=0.89), positive surgical margins (P=0.67) and rate of significant upgrading (P=0.12). LogPCA model showed no distinction between the two cohorts; and Pearson's correlation values turned to be between -0.5 and +0.5. In Arm B, the lesion diameter <10 mm is the only predictive variable of positive SB only for PCa (P=0.04), with an additional value +3% for PCa DR. CONCLUSIONS: In biopsy naïve patients, FB alone is not inferior to FB+SB in detecting csPCa (Δ csPCa DR 0.4%). Δ 7.3% in overall PCa DR was registered between the two Arms, however the addition of further standard samples homolaterally to mp-MRI index lesion improved the overall PCa DR of FB only sampling (Δ PCa DR 3%). The omission of SB did not influence the post-surgical outcomes in terms of NS approach, PSMr and upgrading/downgrading.

Development of a novel nomogram to identify the candidate to extended pelvic lymph node dissection in patients who underwent mpMRI and target biopsy only.

BACKGROUND: Nowadays a tool able to predict the risk of lymph-node invasion (LNI) in patients underwent target biopsy (TB) only before radical prostatectomy (RP) is still lacking. Our aim is to develop a model based on mp-MRI and target biopsy (TB) alone able to predict the risk of LNI. METHODS: We retrospectively extracted data of patients with preoperative positive mp-MRI and TB only who underwent RARP with ePLND from April 2014 to March 2020. A logistic regression model was performed to evaluate the impact of pre- and intra-operative factors on the risk of LNI. Model discrimination was assessed using an area under (AUC) the ROC curve. A nomogram, and its calibration plot, to predict the risk of LNI were generated based on the logistic model. A validation of the model was done using a similar cohort. RESULTS: 461 patients were included, of which 52 (11.27) had LNI. After logistic regression analysis and multivariable model DRE, PI-RADS, seminal vesicle invasion, PSA and worst GS at I and II target lesions were significant predictors of LNI. The AUC was 0.74 [0.67-0.81] 95% CI. The calibration plot shows that our model is very close to the ideal one which is in the 95% CI. After the creation of a visual nomogram, the cut-off to discriminate between the risk or not of LNI was set with Youden index at 60 points that correspond to a risk of LNI of 7%. The model applied on a similar cohort shown a LH+ of 2.58 [2.17-2.98] 95% CI. CONCLUSIONS: Our nomogram for patients undergoing MRI-TB only takes into account clinical stage, SVI at MRI, biopsy Gleason pattern and PSA and it is able to identify patients with risk of LNI when a score higher than 7% is achieved.

Treatment of Ureteral Stent-Related Symptoms.

BACKGROUND: The aim of the study was to assess the effectiveness of the main classes of drugs used at reducing morbidity related to ureteric stents. SUMMARY: After establishing a priori protocol, a systematic electronic literature search was conducted in July 2019. The randomized clinical trials (RCTs) selection proceeded in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and was registered (PROSPERO ID 178130). The risk of bias and the quality assessment of the included RCTs were performed. Ureteral Stent Symptom Questionnaire (USSQ), International Prostate Symptom Score (IPSS), and quality of life (QoL) were pooled for meta-analysis. Mean difference and risk difference were calculated as appropriate for each outcome to determine the cumulative effect size. Fourteen RCTs were included in the analysis accounting for 2,842 patients. Alpha antagonist, antimuscarinic, and phosphodiesterase (PDE) inhibitors significatively reduced all indexes of the USSQ, the IPSS and QoL scores relative to placebo. Conversely, combination therapy (alpha antagonist plus antimuscarinic) showed in all indexes of the USSQ, IPSS, and QoL over alpha antagonist or antimuscarinic alone. On comparison with alpha blockers, PDE inhibitors were found to be equally effective for urinary symptoms, general health, and body pain parameters, but sexual health parameters improved significantly with PDE inhibitors. Finally, antimuscarinic resulted in higher decrease in all indexes of the USSQ, the IPSS, and QoL relative to alpha antagonist. KEY MESSAGE: Relative to placebo, alpha antagonist alone, antimuscarinics alone, and PDE inhibitors alone have beneficial effect in reducing stent-related symptoms. Furthermore, there are significant advantages of combination therapy compared with monotherapy. Finally, PDE inhibitors are comparable to alpha antagonist, and antimuscarinic seems to be more effective than alpha antagonist alone.

MRI/real-time ultrasound image fusion guided high-intensity focused ultrasound: a prospective comparative and functional analysis of different ablative techniques.

BACKGROUND: The aim of this paper was to compare safety and functional outcomes of total, hemi and focal ablation by the latest focal high-intensity focused ultrasound (HIFU) device. METHODS: This is a prospective study including patients with low to intermediate-risk PCa treated with HIFU by Focal One® device from 11/2018 to 3/2020. Before the treatment all patients underwent mp-magnetic resonance imaging (MRI) and subsequent MRI/transrectal ultrasound (TRUS) fusion and standard biopsy. Patients were stratified according to the type of ablation: total, hemi- or focal ablation. Functional data (IPSS, Quality of Life [QoL], IIEF-5, maximum flow [Qmax] and post void residual [PVR] at flowmetry) were assessed preoperatively and at 1, 3, 6 and 12 months after treatment. Moreover, the urinary symptoms reported by patients at IPSS questionnaire were divided in "irritative" and "obstructive" and compared. RESULTS: One hundred patients were enrolled. Median prostate volume and lesion diameter were 46 (IQR 25-75) mL and 10 (IQR 6-13) mm. 15, 50 and 35 patients underwent total, hemi- and focal ablation, respectively. No differences were found between them except for operative time (lower in the focal group, P<0.01). Significant lower incidence of irritative symptoms was identified in the focal group compared to the others (P<0.05 at 1 and 3 months of follow-up). No differences were found among the baseline status and the postoperative assessment in terms of obstructive IPSS items, IIEF-5, QoL, Qmax and PVR (all P value>0.05). CONCLUSIONS: Our study suggests that patients' specific HIFU tailoring with the MRI/real-time TRUS Guidance by Focal One® device is able to minimize the side effects of treatment.

Clinical value of offering multiple chemotherapy lines to a luminal-like metastatic breast cancer: A case report with eribulin.

INTRODUCTION: The achievement of complete response with chemotherapy after multiple treatment lines in metastatic breast cancer and the chemosensitivity in a luminal-like breast cancer are two important issues as it is often asked whether there is a potential limit to the number of therapeutic lines offered and what clinical value they may have. In this setting, eribulin mesylate is a chemotherapy option available. Several randomized and observational studies demonstrated eribulin's meaningful improvement on prolongation of survival, chronicling the disease and preventing the onset of new metastases, although the rate of complete responses is rather limited. CASE DESCRIPTION: We report the five-year history of a luminal A breast cancer, stage IV at diagnosis, metastasized to bone and brain. After undergoing four chemotherapy lines and several radiotherapy sessions with partial response as the best response on bone and with a complete response on brain, our patient finally achieved a metabolic complete response also on bone after about a year of fifth-line treatment with eribulin. Currently the patient is in close clinical and radiological follow-up. CONCLUSIONS: This case report aims to emphasize the clinical value of a chronic chemotherapy treatment also in heavily pretreated and luminal-like metastatic breast cancer, supporting eribulin as a good choice to consider.