Computer-Generated R.E.N.A.L. Nephrometry Scores Yield Comparable Predictive Results to Those of Human-Expert Scores in Predicting Oncologic and Perioperative Outcomes.

PURPOSE: We sought to automate R.E.N.A.L. (for radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry scoring of preoperative computerized tomography scans and create an artificial intelligence-generated score (AI-score). Subsequently, we aimed to evaluate its ability to predict meaningful oncologic and perioperative outcomes as compared to expert human-generated nephrometry scores (H-scores). MATERIALS AND METHODS: A total of 300 patients with preoperative computerized tomography were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer at a single institution. A deep neural network approach was used to automatically segment kidneys and tumors, and geometric algorithms were developed to estimate components of R.E.N.A.L. nephrometry score. Tumors were independently scored by medical personnel blinded to AI-scores. AI- and H-score agreement was assessed using Lin's concordance correlation and their predictive abilities for both oncologic and perioperative outcomes were assessed using areas under the curve. RESULTS: Median age was 60 years (IQE 51-68), and 40% were female. Median tumor size was 4.2 cm and 91.3% had malignant tumors, including 27%, 37% and 24% with high stage, grade and necrosis, respectively. There was significant agreement between H-scores and AI-scores (Lin's ⍴=0.59). Both AI- and H-scores similarly predicted meaningful oncologic outcomes (p <0.001) including presence of malignancy, necrosis, and high-grade and -stage disease (p <0.003). They also predicted surgical approach (p <0.004) and specific perioperative outcomes (p <0.05). CONCLUSIONS: Fully automated AI-generated R.E.N.A.L. scores are comparable to human-generated R.E.N.A.L. scores and predict a wide variety of meaningful patient-centered outcomes. This unambiguous artificial intelligence-based scoring is intended to facilitate wider adoption of the R.E.N.A.L. score.

Navigating systemic therapy for metastatic castration-naïve prostate cancer.

INTRODUCTION: The last decade has seen a remarkable shift in the treatment landscape of advanced prostate cancer, none more so than in the management of metastatic castration-naïve disease. METHODS: This narrative review will examine existing and emerging evidence supporting systemic therapy use for metastatic castration-naïve prostate cancer (mCNPC) and provide guidance on the selection of these agents with respect to optimising patient outcomes. RESULTS: The addition of either docetaxel (chemohormonal approach) or an AR pathway inhibitor (abiraterone, enzalutamide or apalutamide) is a reasonable standard of care option for men commencing long-term ADT for mCNPC. While the issue of disease volume as a predictive biomarker for docetaxel benefit has previously been debated, recent data support consideration of upfront docetaxel in all patients, regardless of metastatic burden. Decisions regarding systemic treatment for men with mCNPC should be based on comprehensive consideration of disease, patient and logistical factors. Multiple novel therapeutics for mCNPC are currently under active investigation. CONCLUSION: The introduction of potent systemic therapy earlier in the mCNPC disease course has resulted in dramatic improvements in clinical outcomes for patients. As the management of mCNPC continues to evolve, the future remains promising, with the expectation of ongoing improvements to patient outcomes and quality of life.

Robotic prostatectomy after abandoned open radical prostatectomy-Technical aspects and outcomes.

Objective: To describe the technical aspects and outcomes of robotic-assisted radical prostatectomy (RARP) following abandoned open radical prostatectomy (ORP). Patients and Methods: A retrospective review was performed of patients who underwent RARP following abandonment of ORP between 2016 and 2020. RARP was undertaken by two highly experienced robotic surgeons. Analysis of patient and operative characteristics, outcomes, and reasons for abandonment of ORP were described. Results: Six patients were included for analysis with a median age of 63.5 years [50.3-67.5]. The median body mass index (BMI) was 34.7 [27.8-36.2]. All patients had intermediate-risk prostate cancer. Small prostate and deep pelvis were given as reasons for abandoning ORP in five cases (83.3%), with four of these also attributing increased BMI as a factor. Extensive mesh from previous bilateral inguinal hernia repair was cited as the reason for abandonment in the remaining patient. One patient had commenced androgen deprivation therapy following abandoned ORP. Extensive retropubic adhesions were noted at the time of RARP in five of six patients, with intraoperative complication of small bladder lacerations encountered in the patient with prior mesh hernia repair. The median time from abandoned ORP to RARP was 128 days [40-216]. Median operating time was 160 minutes [139-190] and estimated blood loss was 225 mL [138-375]. Negative margins were obtained in four of six cases, with further salvage treatment being required in one case at a median follow-up duration of 10.5 months [6.5-25.3]. Conclusion: Abandonment of ORP is an uncommonly reported event, however, in this small case series, we demonstrate that, in the hands of experienced surgeons, RARP is a safe and technically feasible alternative in such cases. Increased BMI, small prostate size and pelvic anatomical constraints appear to be common catalysts for abandonment of open surgery in this cohort. Identifying these high-risk patients early and considering referral to robotic centers may be preferred.