Untargeted metabolomic profile for the detection of prostate carcinoma-preliminary results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares–discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach

Decision Support System for target prostate biopsy outcome prediction: Clustering and FP-growth algorithm for fuzzy rules extraction

An automated and data-driven rules extraction is crucial for the construction of Fuzzy Inference Systems (FIS). This work presents a method for extracting fuzzy rules based on clustering and association mining through the FP-growth algorithm. First, Self Organizing Maps are used to identify subsets of elements with similar characteristics, separately for each class. Then, the FP-Growth algorithm is applied to each cluster. Elements matching each rule are subdivided in the corresponding classes and only rules showing a predominance of elements belonging to one class are used as fuzzy rules. The method was applied to the construction of a Decision Support System based on FIS for the target prostate biopsy outcome prediction based on six pre-bioptic variables. A dataset containing 1447 patients (824 with positive outcome, 623 with negative outcome) was used. Four and six clusters were identified for the positive and the negative class, respectively. A total of 151 rules were extracted with FP-Growth algorithm and 29 were included in the FIS. The system was able to classify 927 patients out of 1447. On the classi-fied subjects, it reached a sensitivity of 87.5% and a specificity of 58.8%

Segmental Ureterectomy for Upper Tract Urothelial Carcinoma: A Systematic Review and Meta-analysis of Comparative Studies

Radical nephroureterectomy (RNU) represents the standard of care for high-risk upper tract urothelial carcinoma (UTUC). In selected patients with ureteral UTUC, a conservative approach such as segmental ureterectomy (SU) can be considered. However, this therapeutic option remains controversial. The aim of this study was to perform a systematic review and meta-analysis of studies assessing the outcomes of SU versus RNU in patients with UTUC. Three search engines (Scopus, Embase, and Web of Science) were queried up to May 2019. The Preferred Reporting Items for Systematic Review and Meta-analysis Statement (PRISMA Statement) was used as a guideline for study selection. The clinical question was established as stated in the PICO (Population, Intervention, Comparator, Outcome) process. Patients in the SU group were more likely to have history of bladder cancer (odds ratio [OR], 1.99; 95% confidence interval [CI], 1.12-3.51; P = .02), but less likely to present with preoperative hydronephrosis (OR, 0.52; 95% CI: 0.31-0.88; P = .02). A higher rate of ureteral tumor location was found in the SU group (OR, 7.54; 95% CI, 4.15-13.68; P < .00001). The SU group presented with a lower rate of higher (pT ≥ 2) stage (OR, 0.66; 95% CI, 0.53-0.82; P = .0002), and high-grade tumors (OR, 0.62; 95% CI, 0.50-0.78; P < .0001). The SU group was found to have shorter 5-year relapse-free survival (OR, 0.64; 95% CI, 0.43-0.95; P = .03), but higher postoperative estimated glomular filtration rate (weighted mean difference, 10.97 mL/min; 95% CI, 2.97-18.98; P = .007). Selected patients might benefit from SU as a therapeutic option for UTUC. In advanced high-risk disease, RNU still remains the standard of care

Indication to pelvic lymph nodes dissection for prostate cancer: the role of multiparametric magnetic resonance imaging when the risk of lymph nodes invasion according to Briganti updated nomogram is &lt;5

Background: The Briganti updated nomogram (BN) is the most popular predictive model aiming to predict the presence of lymph node invasion (LNI) in patients with prostate cancer (PCa), but it lacks information obtained by preoperative imaging. The primary aim of the study was to evaluate the role of multiparametric prostate magnetic resonance imaging (mp-MRI) in the indication to perform pelvic lymph nodes dissection (PLND) or not in patients with risk of LNI according to BN below 5%. Methods: Since March 2012 and September 2016, 310 patients who underwent a preoperative mp-MRI for staging purpose and subsequent robot-assisted extended PLND (RAEPLND) were retrospectively evaluated. Mp-MRIs were prospectively analyzed by two experienced radiologists. The imaging parameters analyzed were the presence of extracapsular extension (ECE), seminal vesicles invasion (SVI) and predominant Gleason pattern 4 (pG4). All patients underwent RAEPLND by two experienced surgeons with a standardized technique. A dedicated uropathologist performed all pathological analysis. Univariate analysis and multivariate logistic regression analysis were used in order to identify the predictors of LNI in patients with PCa. Results: In the overall population, 57 (18.4%) patients had histologically proven pN1 disease. 48/250 patients (19.2%) with a risk of LNI â¥5% as calculated by the BN were staged pN1 at final histopathological analysis. 9/60 patients (15.0%) with a risk of LNI <5% as calculated by BN, who underwent RAEPLND anyway according to the findings at mp-MRI, were staged pN1 at final histopathological analysis. At multivariate logistic regression analysis, all the three mp-MRI parameters were significant independent predictors of LNI after RAEPLND. Conclusions: The role of mp-MRI seemed to be crucial in patients with a risk of LNI <5% as calculated by the BN. The presence of ECE, SVI, or pG4 at mp-MRI was found to be an independent predictor of LNI by itself