Awake Da Vinci robotic partial nephrectomy: First case report ever in a situation of need

We report a unique case of a robotic partial nephrectomy performed under continuous spinal anesthesia (CSA). A 63-year-old woman, active smoker with mild obesity and previous right pneumonectomy, was diagnosed with a growing 5.5-cm renal right cystic tumor. Being at high risk for general anesthesia, a loco-regional approach was indicated. Therefore, after multidisciplinary discussion, a robotic-assisted partial nephrectomy under CSA was considered mandatory. After T4-T5 sensory and motor block, retroperitoneoscopic robot-assisted surgery was successfully performed. Postoperative period was uneventful, with optimal pain control. This unique case demonstrates the feasibility of robotic surgery under CSA, for imperative indications

Safety and Feasibility of Transperineal Targeted Microwave Ablation for Low- to Intermediate-risk Prostate Cancer

BACKGROUND: Focal therapy has emerged as an interesting option for localized low- to intermediate-risk prostate cancer (PCa). Targeted microwave ablation (TMA) is a novel FT modality involving targeted delivery of microwave energy under multiparametric magnetic resonance imaging (MRI)/ultrasound guidance. OBJECTIVE: To describe the step-by-step procedure for TMA and report early functional outcomes. DESIGN, SETTING, AND PARTICIPANTS: This was an experimental phase 1–2 trial in 11 patients diagnosed with a single, MRI-visible PCa lesion of up to 12 mm, scored as International Society of Urological Pathology grade group (GG) 1 or 2. SURGICAL PROCEDURE: Transperineal TMA under MRI/ultrasound image fusion guidance. MEASUREMENTS: We recorded patient and PCa features; intraoperative and postoperative parameters; pain (Visual Analog Scale [VAS]) and adverse events (Common Terminology Criteria for Adverse Events v5.0); and prostate-specific antigen (PSA), International Prostate Symptom Score (IPSS) and International Index of Erectile Function (IIEF-5) scores at 1 wk and 1, 3, and 6 mo. RESULTS AND LIMITATIONS: The median patient age was 67 yr (interquartile range [IQR] 18). Median PSA was 5.4 ng/ml (IQR 1.8), median prostate volume was 51 cm(3) (IQR 35), and median lesion size on MRI was 10 mm (IQR 4). Ten patients had GG 2 PCa and one had GG 1 disease. The median procedure time was 40 min (IQR 30). No intraoperative complications were reported. All treatments were performed on a day-case basis and no patients were discharged with a urinary catheter. Postoperatively, no grade ≥2 complications were reported. No significant changes in PSA (p = 0.46), IPSS (p = 0.39), or IIEF-5 scores (p = 0.18) scores were reported. The postoperative VAS score at 24 h was 0 for all patients. CONCLUSIONS: TMA is safe, feasible, and well tolerated in patients with low- to intermediate-risk PCa. Oncological outcomes are still awaited. PATIENT SUMMARY: Targeted microwave therapy is safe and feasible for selected patients with low- to intermediate-risk prostate cancer. The procedure is well tolerated and does not require a urinary catheter after the procedure. Cancer control outcomes are still awaited

Predictors of Prostate Cancer at Fusion Biopsy: The Role of Positive Family History, Hypertension, Diabetes, and Body Mass Index

Background: PSA density and an elevated PI-RADS score are among the strongest predictors of prostate cancer (PCa) in a fusion biopsy. Positive family history, hypertension, diabetes, and obesity have also been associated with the risk of developing PCa. We aim to identify predictors of the prostate cancer detection rate (CDR) in a series of patients undergoing a fusion biopsy. Methods: We retrospectively evaluated 736 consecutive patients who underwent an elastic fusion biopsy from 2020 to 2022. Targeted biopsies (2–4 cores per MRI target) were followed by systematic mapping (10–12 cores). Clinically significant PCa (csPCa) was defined as ISUP score ≥ 2. Uni- and multi-variable logistic regression analyses were performed to identify predictors of CDR among age, body mass index (BMI), hypertension, diabetes, positive family history, PSA, a positive digital rectal examination (DRE), PSA density ≥ 0.15, previous negative biopsy status, PI-RADS score, and size of MRI lesion. Results: The median patients’ age was 71 years, and median PSA was 6.6 ng/mL. A total of 20% of patients had a positive digital rectal examination. Suspicious lesions in mpMRI were scored as 3, 4, and 5 in 14.9%, 55.0%, and 17.5% of cases, respectively. The CDR was 63.2% for all cancers and 58.7% for csPCa. Only age (OR 1.04, p < 0.001), a positive DRE (OR 1.75, p = 0.04), PSA density (OR 2.68, p < 0.001), and elevated PI-RADS score (OR 4.02, p = 0.003) were significant predictors of the CDR in the multivariable analysis for overall PCa. The same associations were found for csPCa. The size of an MRI lesion was associated with the CDR only in uni-variable analysis (OR 1.07, p < 0.001). BMI, hypertension, diabetes, and a positive family history were not predictors of PCa. Conclusions: In a series of patients selected for a fusion biopsy, positive family history, hypertension, diabetes, or BMI are not predictors of PCa detection. PSA-density and PI-RADS score are confirmed to be strong predictors of the CDR

MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

Objectives: To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods: Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions' data and compared with a baseline reference and expert radiologist assessment of EPE. Results: In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81-83%, p = 0.39-1) and outperforming the baseline reference (p = 0.001-0.02). Conclusions: A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task