Objective:The objective of this study is to validate if ex-vivo multispectral photoacoustic (PA) imaging can differentiate between malignant prostate tissue, benign prostatic hyperplasia (BPH), and normal human prostate tissue.Materials and Methods:Institutional Review Board's approval was obtained for this study. A total of 30 patients undergoing prostatectomy for biopsy-confirmed prostate cancer were included in this study with informed consent. Multispectral PA imaging was performed on surgically excised prostate tissue and chromophore images that represent optical absorption of deoxyhemoglobin (dHb), oxyhemoglobin (HbO2), lipid, and water were reconstructed. After the imaging procedure is completed, malignant prostate, BPH and normal prostate regions were marked by the genitourinary pathologist on histopathology slides and digital images of marked histopathology slides were obtained. The histopathology images were co-registered with chromophore images. Region of interest (ROI) corresponding to malignant prostate, BPH and normal prostate were defined on the chromophore images. Pixel values within each ROI were then averaged to determine mean intensities of dHb, HbO2, lipid, and water.Results:Our preliminary results show that there is statistically significant difference in mean intensity of dHb (P < 0.0001) and lipid (P = 0.0251) between malignant prostate and normal prostate tissue. There was difference in mean intensity of dHb (P < 0.0001) between malignant prostate and BPH. Sensitivity, specificity, positive predictive value, and negative predictive value of our imaging system were found to be 81.3%, 96.2%, 92.9% and 89.3% respectively.Conclusion:Our preliminary results of ex-vivo human prostate study suggest that multispectral PA imaging can differentiate between malignant prostate, BPH and normal prostate tissue.
ObjectivesTo incorporate and validate clinically relevant performance metrics of simulation (CRPMS) into a hydrogel model for nerve-sparing robot-assisted radical prostatectomy (NS-RARP). Materials and MethodsAnatomically accurate models of the human pelvis, bladder, prostate, urethra, neurovascular bundle (NVB) and relevant adjacent structures were created from patient MRI by injecting polyvinyl alcohol (PVA) hydrogels into threedimensionally printed injection molds. The following steps of NS-RARP were simulated: bladder neck dissection; seminal vesicle mobilization; NVB dissection; and urethrovesical anastomosis (UVA). Five experts (caseload >500) and nine novices (caseload <50) completed the simulation. Force applied to the NVB during the dissection was quantified by a novel tension wire sensor system fabricated into the NVB. Post-simulation margin status (assessed by induction of chemiluminescent reaction with fluorescent dye mixed into the prostate PVA) and UVA weathertightness (via a standard 180-mL leak test) were also assessed. Objective scoring, using Global Evaluative Assessment of Robotic Skills (GEARS) and Robotic Anastomosis Competency Evaluation (RACE), was performed by two blinded surgeons. GEARS scores were correlated with forces applied to the NVB, and RACE scores were correlated with UVA leak rates. Incorporating clinical metrics in a RARP model sparing radical prostatectomy and fruit for simple prostatectomy. Korean J Urol 2011; 52: 130-5 34 Clarebrough E, Christidis D, Lindner U, Fernandes K, Fleshner N, Lawrentschuk N. Analysis of a practical surgical skills laboratory for nerve sparing radical prostatectomy. World J Urol 2019; 37: 799-804
ObjectiveTo validate robot‐assisted surgery skills acquisition using an augmented reality (AR)‐based module for urethrovesical anastomosis (UVA).MethodsParticipants at three institutions were randomised to a Hands‐on Surgical Training (HoST) technology group or a control group. The HoST group was given procedure‐based training for UVA within the haptic‐enabled AR‐based HoST environment. The control group did not receive any training. After completing the task, the control group was offered to cross over to the HoST group (cross‐over group). A questionnaire administered after HoST determined the feasibility and acceptability of the technology. Performance of UVA using an inanimate model on the daVinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) was assessed using a UVA evaluation score and a Global Evaluative Assessment of Robotic Skills (GEARS) score. Participants completed the National Aeronautics and Space Administration Task Load Index (NASA TLX) questionnaire for cognitive assessment, as outcome measures. A Wilcoxon rank‐sum test was used to compare outcomes among the groups (HoST group vs control group and control group vs cross‐over group).ResultsA total of 52 individuals participated in the study. UVA evaluation scores showed significant differences in needle driving (3.0 vs 2.3; P = 0.042), needle positioning (3.0 vs 2.4; P = 0.033) and suture placement (3.4 vs 2.6; P = 0.014) in the HoST vs the control group. The HoST group obtained significantly higher scores (14.4 vs 11.9; P 0.012) on the GEARS. The NASA TLX indicated lower temporal demand and effort in the HoST group (5.9 vs 9.3; P = 0.001 and 5.8 vs 11.9; P = 0.035, respectively). In all, 70% of participants found that HoST was similar to the real surgical procedure, and 75% believed that HoST could improve confidence for carrying out the real intervention.ConclusionTraining in UVA in an AR environment improves technical skill acquisition with minimal cognitive demand.
During the past 5 years, the body of literature surrounding the utilization of three-dimensional (3D) printing in the field of urology has grown exponentially. Incentivized by work hour restrictions, patient safety initiatives, and inspired by technical advances in biomaterials and rapid printing strategies, this emerging, and fascinating area of research has begun to make headway into clinical practice. However, concerns about cost, limited understanding of the technical processes involved, and lack of its potential uses remain barriers to its widespread adoption. We examined existing published literature on how 3D printing technologies have been utilized in the field of Urology to enhance pre-operative planning, revitalize surgical training, and modernize patient education, with particular focus on, robotic surgery. To date, 3D-printed models have been used and studied most commonly in the preoperative planning for nephron-sparing surgeries during the treatment of renal masses, where the challenges of complex renal anatomy and benefits of reducing renal ischemic injury create the most intuitive value. Prostate models are the second most common, particularly in the planning of nerve-sparing procedures. Early studies have demonstrated sufficient realism and educational effectiveness. Subsequent studies demonstrated improved surgeon confidence, operative performance, and optimized patient outcomes including high levels of patient satisfaction.Realistic, accurate, and reasonably priced models can currently be generated within hours using standard desktop 3D printers. While primarily utilized as anatomic replicas of diseased organs that restore a sense of haptic feedback lost in robotic procedures, innovations in polymers, improvements in 3D printer host and modeling software, and upgrades in printer hardware allow this technology to serve as a comprehensive, interactive, simulation platform that can be a critical surgical decision making as well as an effective teaching tool. As Urologists continue to rapidly diversify and iterate upon this adaptive modality, the benefits in patient outcomes will likely outpace the diminishing drawbacks, and we may well see the next revolution in surgical education, robotic techniques, and personalized medicine concurrently.
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