Faculty Bibliography

PURPOSE/OBJECTIVE:Multiparametric magnetic resonance imaging (mpMRI) with informed targeted biopsies (TGBX) has changed the paradigm of prostate cancer (PCa) diagnosis. Randomized studies have demonstrated a diagnostic benefit of Clinically significant (CS) for TGBX compared to standard systematic biopsies (SBX). We aimed to evaluate whether mpMRI-informed TGBX has superior diagnosis rates of any-, CS-, high-grade (HG)-, and clinically insignificant (CI)-PCa compared to SBX in biopsy-naive men. METHODS:Data was searched in Medline, Embase, Web of Science, and Evidence-based medicine reviews-Cochrane Database of systematic reviews from database inception until 2019. Studies were selected by two authors independently, with disagreements resolved by consensus with a third author. Overall 1951 unique references were identified, and 100 manuscripts underwent full-text review. Data were pooled using random-effects models. The meta-analysis is reported according to the PRISMA statement. The study protocol is registered with PROSPERO (CRD42019128468). RESULTS:Overall 29 studies (13,845 patients) were analyzed. Compared to SBX, use of mpMRI-informed TGBX was associated with a 15% higher rate of any PCa diagnosis (95% CI 10-20%, p<0.00001). This relationship was not affected by the study methodology (p=0.11). Diagnosis of CS and HG PCa were more common in the mpMRI-informed TGBX group (risk difference of 11%, 95% CI 0-20%, p=0.05, and 2%, 95% CI 1-4%; p=0.005, respectively) while there was no difference in diagnosis of CI PCa (risk difference of 0, 95% CI -3 to 3%, p=0.96). Notably, the exclusion of SBX in the mpMRI-informed TGBX arm significantly modified the association between a mpMRI strategy and lower rates of CI PCa diagnosis (p=0.01) without affecting the diagnosis rates of CS- or HG-PCa. CONCLUSIONS:In comparison to SBX, a mpMRI-informed TGBX strategy results in a significantly higher diagnosis rate of any-, CS-, and HG-PCa. Excluding SBX from mpMRI-informed TGBX was associated with decreased rates of CI-PCa diagnosis without affecting diagnosis of CS- or HG-PCa.

Background/UNASSIGNED:Radical cystectomy for bladder cancer has one of the highest rates of morbidity among urologic surgery, but the ability to predict postoperative complications remains poor. Our study objective was to create machine learning models to predict complications and factors leading to extended length of hospital stay and discharge to a higher level of care after radical cystectomy. Methods/UNASSIGNED:Using the American College of Surgeons National Surgical Quality Improvement Program, peri-operative adverse outcome variables for patients undergoing elective radical cystectomy for bladder cancer from 2005 to 2016 were extracted. Variables assessed include occurrence of minor, infectious, serious, or any adverse events, extended length of hospital stay, and discharge to higher-level care. To develop predictive models of radical cystectomy complications, we fit generalized additive model (GAM), least absolute shrinkage and selection operator (LASSO) logistic, neural network, and random forest models to training data using various candidate predictor variables. Each model was evaluated on the test data using receiver operating characteristic curves. Results/UNASSIGNED:A total of 7557 patients were identified who met the inclusion criteria, and 2221 complications occurred. LASSO logistic models demonstrated the highest area under curve for predicting any complications (0.63), discharge to a higher level of care (0.75), extended length of stay (0.68), and infectious (0.62) adverse events. This was comparable with random forest in predicting minor (0.60) and serious (0.63) adverse events. Conclusions/UNASSIGNED:Our models perform modestly in predicting radical cystectomy complications, highlighting both the complex cystectomy process and the limitations of large healthcare datasets. Identifying the most important variable leading to each type of adverse event may allow for further strategies to model cystectomy complications and target optimization of modifiable variables pre-operative to reduce postoperative adverse events.