Comparison of Efficacy of Different Surgical Techniques for Benign Prostatic Obstruction

Prostate tissue ablation and drug delivery by an image-guided injectable ionic liquid in ex vivo and in vivo models

Benign prostatic hyperplasia and prostate cancer are often associated with lower urinary tract symptoms, which can severely affect patient quality of life. To address this challenge, we developed and optimized an injectable compound, prostate ablation and drug delivery agent (PADA), for percutaneous prostate tissue ablation and concurrently delivered therapeutic agents. PADA is an ionic liquid composed of choline and geranic acid mixed with anticancer therapeutics and a contrast agent. The PADA formulation was optimized for mechanical properties compatible with hand injection, diffusion capability, cytotoxicity against prostate cells, and visibility of an x-ray contrast agent. PADA also exhibited antibacterial properties against highly resistant clinically isolated bacteria in vitro. Ultrasound-guided injection, dispersion of PADA in the tissue, and tissue ablation were tested ex vivo in healthy porcine, canine, and human prostates and in freshly resected human tumors. In vivo testing was conducted in a murine subcutaneous tumor model and in the canine prostate. In all models, PADA decreased the number of viable cells in the region of dispersion and supported the delivery of nivolumab throughout a portion of the tissue. In canine survival experiments, there were no adverse events and no impact on urination. The injection approach was easy to perform under ultrasound guidance and produced a localized effect with a favorable safety profile. These findings suggest that PADA is a promising therapeutic prostate ablation strategy to treat lower urinary tract symptoms.

Peripheral zone thickness in preoperative MRI is predictive of Trifecta achievement after Holmium laser enucleation of the prostate (HoLEP)

PURPOSE

To investigate various anatomical features of the prostate using preoperative MRI and patients' clinical factors to identify predictors of successful Holmium:YAG laser enucleation of the prostate (HoLEP).

METHODS

71 patients who had received HoLEP and undergone a 3.0-T prostate MRI scan within 6 months before surgery were retrospectively enrolled. MRI features (e.g., total prostate and transitional zone volume, peripheral zone thickness [PZT], BPH patterns, prostatic urethral angle, intravesical prostatic protrusion, etc.) and clinical data (e.g., age, body mass index, surgical technique, etc.) were analyzed using univariable and multivariable logistic regression to identify predictors of successful HoLEP. Successful HoLEP was defined as achieving the Trifecta, characterized by the contemporary absence of postoperative complications within 3 months, a 3-month postoperative maximum flow rate (Qmax) > 15 mL/s, and no urinary incontinence at 3 months postoperatively.

RESULTS

Trifecta achievement at 3 months post-surgery was observed in 37 (52%) patients. Patients with Trifecta achievement exhibited a lower preoperative IPSS-quality of life score (QoL) (4.1 vs. 4.5, P = 0.016) and a thinner preoperative peripheral zone thickness (PZT) on MRI (7.9 vs.10.3 mm, P < 0.001). In the multivariable regression analysis, a preoperative IPSS-QoL score < 5 (OR 3.98; 95% CI, 1.21-13.07; P = 0.017) and PZT < 9 mm (OR 11.51; 95% CI, 3.51-37.74; P < 0.001) were significant predictors of Trifecta achievement after HoLEP.

CONCLUSIONS

Alongside the preoperative QoL score, PZT measurement in prostate MRI can serve as an objective predictor of successful HoLEP. Our results underscore an additional utility of prostate MRI beyond its role in excluding concurrent prostate cancer.

Salvage holmium laser enucleation of the prostate after previous interventions: a systematic review

OBJECTIVE

To investigate the feasibility, safety and efficacy of holmium laser enucleation of the prostate (HoLEP) in the re-treatment setting (salvage HoLEP) and compare it to the primary HoLEP procedure that is commonly used for the treatment of benign prostate hyperplasia (BPH).

MATERIALS AND METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, PubMed, Scopus, and Cochrane databases were systematically screened, from inception to 8 August 2022. Other potentially eligible studies were retrieved using the reference lists of the included studies. Retrospective and prospective studies, both comparative and non-comparative, were included.

RESULTS

A total of 12 studies met the inclusion criteria and were included in the final qualitative synthesis. One study was prospective comparative (non-randomised), seven studies were retrospective comparative, and four studies were retrospective non-comparative or case series. In total, 831 patients were treated with salvage HoLEP in the above studies. Previous intervention before salvage HoLEP ranged among studies. The most commonly performed was transurethral resection of the prostate. Intraoperative parameters of salvage HoLEP were comparable with those reported during primary HoLEP, while all postoperative outcomes were significantly improved after salvage HoLEP and were similar with those observed after primary HoLEP. No major complications were noted after salvage HoLEP according to Clavien-Dindo classification.

CONCLUSIONS

Salvage HoLEP after previous interventions for treating recurrent or residual BPH is a feasible, safe, and efficient procedure. Data presented in selected studies, along with the holmium laser's physical properties to resect more tissue and to dissect along the true anatomical plane of BPH, render HoLEP an ideal salvage treatment modality for recurrent or residual BPH symptoms.

The Current State of Artificial Intelligence Application in Urology

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research.

Transfer Learning for Effective Urolithiasis Detection

PURPOSE

Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology.

METHODS

The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model's performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics.

RESULTS

The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process.

CONCLUSION

This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.

Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm

Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach.Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed).Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.

New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases

Artificial intelligence (AI) is used in various fields of medicine, with applications encompassing all areas of medical services, such as the development of medical robots, the diagnosis and personalized treatment of diseases, and personalized healthcare. Medical AI research and development have been largely focused on diagnosis, prediction, treatment, and management as an auxiliary means of patient care. AI is mainly used in the fields of personal healthcare and diagnostic imaging. In urology, substantial investments are being made in the development of urination monitoring systems in the personal healthcare field and diagnostic solutions for ureteral stricture and urolithiasis in the diagnostic imaging field. This paper describes AI applications for urinary diseases and discusses current trends and future perspectives in AI research.

A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis

Purpose

This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them.

Methods

This paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones.

Results

The final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.

Conclusions

The general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.

Laser enucleation of the prostate in men with very large glands ≥175 ml: A systematic review

Background

Surgical treatment options for lower urinary tract symptoms can differ according to prostate size. There are few studies on the efficacy and safety of endoscopic enucleation of prostate (EEP) in patients with very large prostates focusing on laser as energy source. In this systematic review, we aimed to examine the efficacy and safety of laser-based EEP on prostate glands ≥150 ml.

Methods

A systematic search was conducted using Web of Science, PubMed-MEDLINE, Wiley Online Library and Cochrane Library databases with the following search terms solely or in combination: "large prostate", "laser enucleation", "laser prostatectomy"by combining PICO (population, intervention, comparison, and outcome) terms. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed.

Results

We retrieved 6 studies included 375 patients with prostate sizes ≥175 ml treated with laser-based EEP for symptomatic benign prostatic obstruction. Three studies examined Holmium laser enucleation of prostate (HoLEP) outcomes with a prostate volume (PV) >200 ml, one evaluated HoLEP outcomes with a PV of 200–299 and ≥ 300 ml, two studies evaluated HoLEP outcomes with a PV > 175 ml. We observed improvement in postoperative functional outcomes in patients with a PV > 175, >200 and >300 ml. The retreatment rate was 0–1.3% in all studies involving prostate size ≥175 ml. Most of the complications were Clavien-Dindo I (%0–9) and II (%12.7–16.6).

Conclusions

Laser-based EEP is an efficient, safe and feasible procedure even in very large prostates with good functional outcomes, low perioperative complication and retreatment rates.

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We observed better postoperative functional outcomes in prostates with a volume of ≥175, >200 and >300 ml in the present study.

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The retreatment rate was 0–1.3% in all studies involving prostate size ≥175 ml.

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Laser-based- endoscopic enucleation of the prostate is an efficient, safe and feasible procedure even in very large glands.

Development of an Artificial Intelligence-Based Support Technology for Urethral and Ureteral Stricture Surgery

Purpose

This paper proposes a technological system that uses artificial intelligence to recognize and guide the operator to the exact stenosis area during endoscopic surgery in patients with urethral or ureteral strictures. The aim of this technological solution was to increase surgical efficiency.

Methods

The proposed system utilizes the ResNet-50 algorithm, an artificial intelligence technology, and analyzes images entering the endoscope during surgery to detect the stenosis location accurately and provide intraoperative clinical assistance. The ResNet-50 algorithm was chosen to facilitate accurate detection of the stenosis site.

Results

The high recognition accuracy of the system was confirmed by an average final sensitivity value of 0.96. Since sensitivity is a measure of the probability of a true-positive test, this finding confirms that the system provided accurate guidance to the stenosis area when used for support in actual surgery.

Conclusions

The proposed method supports surgery for patients with urethral or ureteral strictures by applying the ResNet-50 algorithm. The system analyzes images entering the endoscope during surgery and accurately detects stenosis, thereby assisting in surgery. In future research, we intend to provide both conservative and flexible boundaries of the strictures.