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Li Y, Xiao Y, Shen Z, Yang S, Li Z, Liao H, Zhou S. Recent Advances in Diagnosing and Treating Post-Prostatectomy Urinary Incontinence. Ann Surg Oncol 2024:10.1245/s10434-024-16110-1. [PMID: 39217264 DOI: 10.1245/s10434-024-16110-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
Radical prostatectomy and radiotherapy are common first-line treatments for clinically localized prostate cancer. Despite advances in surgical technology and multidisciplinary management, post-prostatectomy urinary incontinence (PPI) remains a common clinical complication. The incidence and duration of PPI are highly heterogeneous, varying considerably between individuals. Post-prostatectomy urinary incontinence may result from a combination of factors, including patient characteristics, lower urinary tract function, and surgical procedures. Physicians often rely on detailed medical history, physical examinations, voiding diaries, pad tests, and questionnaires-based symptoms to identify critical factors and select appropriate treatment options. Post-prostatectomy urinary incontinence treatment can be divided into conservative treatment and surgical interventions, depending on the severity and type of incontinence. Pelvic floor muscle training and lifestyle interventions are commonly conservative strategies. When conservative treatment fails, surgery is frequently recommended, and the artificial urethral sphincter remains the "gold standard" surgical intervention for PPI. This review focuses on the diagnosis and treatment of PPI, based on the most recent clinical research and recommendations of guidelines, including epidemiology and risk factors, diagnostic methods, and treatment strategies, aimed at presenting a comprehensive overview of the latest advances in this field and assisting doctors in providing personalized treatment options for patients with PPI.
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Affiliation(s)
- Yunlong Li
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - YingMing Xiao
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Zhengang Shen
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - ShengKe Yang
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Zeng Li
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Liao
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Shukui Zhou
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Amparore D, De Cillis S, Alladio E, Sica M, Piramide F, Verri P, Checcucci E, Piana A, Quarà A, Cisero E, Manfredi M, Di Dio M, Fiori C, Porpiglia F. Development of Machine Learning Algorithm to Predict the Risk of Incontinence After Robot-Assisted Radical Prostatectomy. J Endourol 2024; 38:871-878. [PMID: 38512711 DOI: 10.1089/end.2024.0057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Abstract
Introduction: Predicting postoperative incontinence beforehand is crucial for intensified and personalized rehabilitation after robot-assisted radical prostatectomy. Although nomograms exist, their retrospective limitations highlight artificial intelligence (AI)'s potential. This study seeks to develop a machine learning algorithm using robot-assisted radical prostatectomy (RARP) data to predict postoperative incontinence, advancing personalized care. Materials and Methods: In this propsective observational study, patients with localized prostate cancer undergoing RARP between April 2022 and January 2023 were assessed. Preoperative variables included age, body mass index, prostate-specific antigen (PSA) levels, digital rectal examination (DRE) results, Gleason score, International Society of Urological Pathology grade, and continence and potency questionnaires responses. Intraoperative factors, postoperative outcomes, and pathological variables were recorded. Urinary continence was evaluated using the Expanded Prostate cancer Index Composite questionnaire, and machine learning models (XGBoost, Random Forest, Logistic Regression) were explored to predict incontinence risk. The chosen model's SHAP values elucidated variables impacting predictions. Results: A dataset of 227 patients undergoing RARP was considered for the study. Post-RARP complications were predominantly low grade, and urinary continence rates were 74.2%, 80.7%, and 91.4% at 7, 13, and 90 days after catheter removal, respectively. Employing machine learning, XGBoost proved the most effective in predicting postoperative incontinence risk. Significant variables identified by the algorithm included nerve-sparing approach, age, DRE, and total PSA. The model's threshold of 0.67 categorized patients into high or low risk, offering personalized predictions about the risk of incontinence after surgery. Conclusions: Predicting postoperative incontinence is crucial for tailoring rehabilitation after RARP. Machine learning algorithm, particularly XGBoost, can effectively identify those variables more heavily, impacting the outcome of postoperative continence, allowing to build an AI-driven model addressing the current challenges in post-RARP rehabilitation.
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Affiliation(s)
- Daniele Amparore
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Sabrina De Cillis
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Eugenio Alladio
- Department of Chemistry, University of Turin, Turin, Italy
- Centro Regionale Antidoping "A. Bertinaria" of Orbassano (Turin), Turin, Italy
| | - Michele Sica
- Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Federico Piramide
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Paolo Verri
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Enrico Checcucci
- Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy
| | - Alberto Piana
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Alberto Quarà
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Edoardo Cisero
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Matteo Manfredi
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Michele Di Dio
- Division of Urology, Department of Surgery, SS Annunziata Hospital, Cosenza, Italy
| | - Cristian Fiori
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
| | - Francesco Porpiglia
- Division of Urology, Department of Oncology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
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Ramos R, Shankar PR, Soputro NA, Bullen J, Pedraza AM, Chavali JS, Mikesell CD, Ward R, Purysko A, Kaouk J. Preoperative Prostate Magnetic Resonance Imaging-based Anatomical Predictors of Early Urinary Continence Following Single-port Transvesical Robot-assisted Radical Prostatectomy. Eur Urol Focus 2024:S2405-4569(24)00089-0. [PMID: 38866663 DOI: 10.1016/j.euf.2024.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/10/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND OBJECTIVE The introduction of the single-port (SP) robotic system has led to new approaches in robot-assisted radical prostatectomy (RARP), such as the transvesical (TV) approach, offering high rates of early urinary continence. While previous studies of SP TV RARP have identified perioperative factors influencing continence outcomes, the impact of anatomical factors remains unexplored. This study aims to assess magnetic resonance imaging (MRI)-based anatomical predictors of urinary continence after SP TV RARP. METHODS A retrospective analysis of consecutive SP TV RARP cases (November 2020 to June 2023) with preoperative prostate MRI was performed. Two urogenital radiologists independently evaluated ten anatomical parameters to distinguish patients achieving urinary continence within 1 wk and 3 mo. Nonparametric methods estimated receiver operating characteristic curves (area under the curve [AUC]) and inter-reader agreement. KEY FINDINGS AND LIMITATIONS In 120 cases, 40% achieved continence within 1 wk, rising to 71.7% by 3 mo. Membranous urethra length (MUL) alone was significantly associated with continence at 3 mo (AUC: 0.67, p = 0.003). At 1 wk, several parameters, including anteroposterior diameter of the prostate, coronal membranous urethra length, prostate volume, and transverse diameter of the prostate, showed promise in predicting continence. CONCLUSIONS AND CLINICAL IMPLICATIONS A longer preoperative MUL was significantly associated with better odds of an early return to urinary continence after SP TV RARP. Each 1-mm increase in coronal MUL was associated with a 27% increase in the odds of continence at 3 mo. This information can aid in patient counseling and expectations preoperatively. PATIENT SUMMARY Urinary incontinence is a common outcome after prostate cancer surgery, particularly in the early months. Recently, the single-port (SP) robotic system has emerged, localizing surgery to the diseased area. With the SP robot, accessing the prostate via the bladder leads to high rates of early continence. Our study reveals that the longer the urethral portion beneath the prostate, the higher the likelihood of regaining continence within 3 mo after surgery.
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Affiliation(s)
- Roxana Ramos
- Urology Department, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Jennifer Bullen
- Quantitative Health Sciences Department, Lerner Research Institute, Cleveland, OH, USA
| | | | - Jaya S Chavali
- Urology Department, Cleveland Clinic, Cleveland, OH, USA
| | | | - Ryan Ward
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Jihad Kaouk
- Urology Department, Cleveland Clinic, Cleveland, OH, USA.
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Fonseca J, Moraes-Fontes MF, Sousa I, Oliveira F, Froes G, Gaivão A, Palmas A, Rebola J, Muresan C, Santos T, Dias D, Varandas M, Lopez-Beltran A, Ribeiro R, Fraga A. Membranous urethral length is the single independent predictor of urinary continence recovery at 12 months following Retzius-sparing robot-assisted radical prostatectomy. J Robot Surg 2024; 18:230. [PMID: 38809307 PMCID: PMC11136784 DOI: 10.1007/s11701-024-01986-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 05/19/2024] [Indexed: 05/30/2024]
Abstract
The influence of anatomical parameters on urinary continence (UC) after Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains uncharted. Our objective was to evaluate their association with UC at 3, 6 and 12 months post-operatively. Data from patients who underwent RS-RARP were prospectively collected. Continence was defined as no pad use. Anatomic variables were measured on preoperative magnetic resonance imaging (MRI). Regression analyses were performed to identify predictors of UC at each time point. We included 158 patients with a median age of 60 years, most of whom had a localized tumor (≤ cT2). On multivariate analyses, at 3 months post-surgery, urinary incontinence (UI) rises with age, odds ratio (OR) 1.07 [95% confidence interval (CI) 1.004-1.142] and with prostate volume (PV), OR 1.029 (95% CI 1.006-1.052); it reduces with longer membranous urethral length (MUL), OR 0.875 (95% CI 0.780-0.983) and with higher membranous urethral volume (MUV), OR 0.299 (95% CI 0.121-0.737). At 6 months, UI rises with PV, OR 1.033 (95% CI 1.011-1.056) and decreases with MUV, OR 0.1504 (95% CI 0.050-0.444). Significantly, at 12 months post-surgery, the only predictor of UI is MUL, OR 0.830 (95% CI 0.706-0.975), establishing a threshold associated with a risk of UI of 5% (MUL > 15 mm) in opposition to a risk of 25% (MUL < 10 mm). This single institutional study requires external validation. To our knowledge, this is the first prospective cohort study supporting MUL as the single independent predictor of UC at 12 months post-surgery. By establishing MUL thresholds, we enable precise patient counseling.
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Affiliation(s)
- Jorge Fonseca
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal.
- Instituto de Ciências Biomédicas Abel Salazar, Universidade Do Porto, Porto, Portugal.
| | | | - Inês Sousa
- Unidade de Investigação Clínica, Centro Clínico Champalimaud, Champalimaud Foundation, Lisbon, Portugal
| | - Francisco Oliveira
- Serviço de Medicina Nuclear, Centro Clínico Champalimaud, Champalimaud Foundation, Lisbon, Portugal
| | - Gonçalo Froes
- Faculté de Médecine Et Médecine Dentaire, Université Catholique de Louvain, Brussels, Belgium
| | - Ana Gaivão
- Serviço de Imagiologia, Centro Clínico Champalimaud, Champalimaud Foundation, Lisbon, Portugal
| | - Artur Palmas
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Jorge Rebola
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Ciprian Muresan
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Tiago Santos
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Daniela Dias
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Mário Varandas
- Unidade de Próstata, Centro Clínico Champalimaud, Champalimaud Foundation, Av. Brasília, 1400-038, Lisboa, Portugal
| | - Antonio Lopez-Beltran
- Department of Morphological Sciences, Córdoba University Medical School, Córdoba, Spain
| | - Ricardo Ribeiro
- Instituto de Ciências Biomédicas Abel Salazar, Universidade Do Porto, Porto, Portugal
- Instituto de Investigação E Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
| | - Avelino Fraga
- Instituto de Ciências Biomédicas Abel Salazar, Universidade Do Porto, Porto, Portugal
- Instituto de Investigação E Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
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van den Berg I, Spaans RN, Wessels FJ, van der Hoeven EJRJ, Nolthenius CJT, van den Bergh RCN, van der Voort van Zyp JRN, van den Berg CAT, van Melick HHE. Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy. Eur Radiol Exp 2024; 8:1. [PMID: 38165522 PMCID: PMC10761662 DOI: 10.1186/s41747-023-00402-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans. METHODS Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. RESULTS The DL models achieved median Dice similarity coefficient (DSC) values of 0.85-0.97 for coronal structures and 0.87-0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28-0.83) to 0.91 (95% CI 0.84-0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42-0.87) to 0.86 (95% CI 0.75-0.92) and for the levator ani muscle from 0.40 (95% CI 0.05-0.66) to 0.61 (95% CI 0.31-0.78). CONCLUSIONS DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans. RELEVANCE STATEMENT The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models. KEY POINTS • DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models.
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Affiliation(s)
- Ingeborg van den Berg
- Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
- Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands.
| | - Robert N Spaans
- Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Frank J Wessels
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | | | | | - Cornelis A T van den Berg
- Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Harm H E van Melick
- Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands
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