Faculty Bibliography

OBJECTIVES/OBJECTIVE:Recent advances have led to the use of magnetic resonance imaging (MRI) alone or with fusion to transrectal ultrasound (TRUS) images for guiding biopsy of the prostate. Our group sought to develop consensus recommendations regarding MRI-guided prostate biopsy based on currently available literature and expert opinion. METHODS:The published literature on the subject of MRI-guided prostate biopsy was reviewed using standard search terms and synthesized and analyzed by four different subgroups from among the authors. The literature was grouped into four categories-MRI-guided biopsy platforms, robotic MRI-TRUS fusion biopsy, template mapping biopsy and transrectal MRI-TRUS fusion biopsy. Consensus recommendations were developed using the Oxford Center for Evidence Based Medicine criteria. RESULTS:There is limited high level evidence available on the subject of MRI-guided prostate biopsy. MRI guidance with or without TRUS fusion can lead to fewer unnecessary biopsies, help identify high-risk (Gleason ≥ 3 + 4) cancers that might have been missed on standard TRUS biopsy and identify cancers in the anterior prostate. There is no apparent significant difference between MRI biopsy platforms. Template mapping biopsy is perhaps the most accurate method of assessing volume and grade of tumor but is accompanied by higher incidence of side effects compared to TRUS biopsy. CONCLUSIONS:Magnetic resonance imaging-guided biopsies are feasible and better than traditional ultrasound-guided biopsies for detecting high-risk prostate cancer and anterior lesions. Judicious use of MRI-guided biopsy could enhance diagnosis of clinically significant prostate cancer while limiting diagnosis of insignificant cancer.

BACKGROUND:Patient-specific 3D models are being used increasingly in medicine for many applications including surgical planning, procedure rehearsal, trainee education, and patient education. To date, experiences on the use of 3D models to facilitate patient understanding of their disease and surgical plan are limited. The purpose of this study was to investigate in the context of renal and prostate cancer the impact of using 3D printed and augmented reality models for patient education. METHODS:Patients with MRI-visible prostate cancer undergoing either robotic assisted radical prostatectomy or focal ablative therapy or patients with renal masses undergoing partial nephrectomy were prospectively enrolled in this IRB approved study (n = 200). Patients underwent routine clinical imaging protocols and were randomized to receive pre-operative planning with imaging alone or imaging plus a patient-specific 3D model which was either 3D printed, visualized in AR, or viewed in 3D on a 2D computer monitor. 3D uro-oncologic models were created from the medical imaging data. A 5-point Likert scale survey was administered to patients prior to the surgical procedure to determine understanding of the cancer and treatment plan. If randomized to receive a pre-operative 3D model, the survey was completed twice, before and after viewing the 3D model. In addition, the cohort that received 3D models completed additional questions to compare usefulness of the different forms of visualization of the 3D models. Survey responses for each of the 3D model groups were compared using the Mann-Whitney and Wilcoxan rank-sum tests. RESULTS:All 200 patients completed the survey after reviewing their cases with their surgeons using imaging only. 127 patients completed the 5-point Likert scale survey regarding understanding of disease and surgical procedure twice, once with imaging and again after reviewing imaging plus a 3D model. Patients had a greater understanding using 3D printed models versus imaging for all measures including comprehension of disease, cancer size, cancer location, treatment plan, and the comfort level regarding the treatment plan (range 4.60-4.78/5 vs. 4.06-4.49/5, p < 0.05). CONCLUSIONS:All types of patient-specific 3D models were reported to be valuable for patient education. Out of the three advanced imaging methods, the 3D printed models helped patients to have the greatest understanding of their anatomy, disease, tumor characteristics, and surgical procedure.

INTRODUCTION/BACKGROUND:Penectomy for PC is useful in staging, disease prognosis, and treatment. Limited studies have evaluated its surgical complications. We sought to assess these complications and determine predictive models to create a novel risk score for penectomy complications. PATIENTS AND METHODS/METHODS:A retrospective review of patients undergoing PC surgical management from the 2005-2016 American College of Surgeons National Surgical Quality Improvement Program was performed. Data were queried for partial and total penectomy among those with PC. To develop predictive models of complications, we fit LASSO logistic, random forest, and stepwise logistic models to training data using cross-validation, demographic, comorbidity, laboratory, and wound characteristics as candidate predictors. Each model was evaluated on the test data using receiver operating characteristic curves. A novel risk score was created by rounding coefficients from the LASSO logistic model. RESULTS:A total of 304 cases met the inclusion criteria. Overall incidence of penectomy complications was 19.7%, where urinary tract infection (3.0%), superficial surgical site infection (3.0%), and bleeding requiring transfusion (3.9%) were most common. LASSO logistic, random forest, and stepwise logistic models for predicting complications had area under the curve (AUC) [95% confidence interval] values of 0.66 [0.52-0.81], 0.73 [0.63-0.83], and 0.59 [0.45-0.74], respectively. Eleven variables were included in the risk score. The LASSO model-derived risk score had moderately good performance (area under the curve [95% confidence interval] 0.74 [0.66-0.82]). Using a cutoff point of 6, the score attains sensitivity 0.58, specificity 0.74, and kappa 0.26. CONCLUSION/CONCLUSIONS:PC management through penectomy is associated with appreciable complications rates. Predictive models of penectomy complications performed moderately well. Our novel prognostic risk score may allow for improved preoperative counseling and risk stratification of men undergoing surgical management of PC.

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.