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Artificial Intelligence in Urological Oncology: Applications in Imaging, Prognostics, and Precision Disease Management

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2179

Special Issue Editor

Prof. Dr. Sabine D. Brookman-May

E-Mail Website
Guest Editor
Department of Urology, LMU University Hospital, Ludwig Maximilian University of Munich, 81377 Munich, Germany
Interests: urologic oncology; clinical research; artificial intelligence; men’s health/andrology; physical exercise in oncology

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming the field of urological oncology, offering novel opportunities to improve diagnosis, risk stratification, and disease management, including treatment selection and follow-up of patients. Advances in imaging, digital pathology, molecular profiling, and clinical decision support systems are driving more precise prognostication and individualized patient management. Beyond diagnostics and prognostics, AI applications are increasingly shaping therapeutic decision-making, surveillance strategies, and real-world patient care.

This Special Issue aims to bring together cutting-edge research and expert reviews highlighting the role of AI across the continuum of urological cancer care. We welcome contributions that showcase innovative methodologies, clinical applications, translational insights, and critical perspectives on the integration of AI into daily practice.

Prof. Dr. Sabine D. Brookman-May
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • urological oncology
  • imaging, precision medicine
  • digital pathology
  • therapeutic decision-making

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Published Papers (3 papers)

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Research

23 pages, 905 KB  
Article
Efficacy of a Modular App-Based Pelvic Floor Muscle Training Program for Postoperative Continence Recovery After Radical Prostatectomy: A Multi-Center Randomized Controlled Trial (PELVINTENSE Study)
by Bara Barakat, Mustapha Addali, Sameh Hijazi, Saed Alqaddi, Christian Rehme, Boris Hadaschik and Sabine D. Brookman-May
Cancers 2026, 18(9), 1333; https://doi.org/10.3390/cancers18091333 - 22 Apr 2026
Viewed by 194
Abstract
Background/Objectives: Stress urinary incontinence (SUI) remains a common and functionally relevant complication after radical prostatectomy (RP) and substantially impairs quality of life (QoL). Although pelvic floor muscle training (PFMT) is guideline-recommended, its real-world effectiveness is often limited by accessibility, standardization, and adherence. Digital [...] Read more.
Background/Objectives: Stress urinary incontinence (SUI) remains a common and functionally relevant complication after radical prostatectomy (RP) and substantially impairs quality of life (QoL). Although pelvic floor muscle training (PFMT) is guideline-recommended, its real-world effectiveness is often limited by accessibility, standardization, and adherence. Digital health interventions may improve adherence to PFMT, potentially influencing continence recovery. We conducted a multicenter randomized controlled trial to evaluate whether a structured, modular app-based PFMT program improves early continence recovery compared with conventional physiotherapist-guided training. Methods: Between September 2022 and September 2024, 62 preoperatively continent men undergoing radical prostatectomy were enrolled in this multicenter randomized controlled trial (Pelvintense). Both groups received perioperative PFMT: Patients were randomized 1:1 to either a modular app-based PFMT program (intervention group) or a standard physiotherapist-guided PFMT (control group). Both app-based PFMT and standard physiotherapist-guided PFMT started three weeks before surgery and continued for 90 days postoperatively. The primary endpoint was continence at 90 days, defined as ICIQ-SF Q1 = 0 (absence of involuntary SUI). Secondary endpoints included continence sub-scores, QoL, erectile function, adherence, and decision regret. Analyses were performed using a modified intention-to-treat approach applying logistic regression and non-parametric tests with sensitivity analyses. Results: A total of 62 patients were included in the study and randomized, with 31 allocated to the app-based PFMT arm and 31 to the standard physiotherapist-guided arm. Three patients in the control arm withdrew consent for data usage after randomization, resulting in a modified intention-to-treat population of 59 patients. At 90 days, continence rates were higher in the app-based group compared with the control group (74.2% versus 21.4%; p < 0.001), corresponding to an absolute risk reduction of 52.8% and a number needed to treat of two. In multivariable analysis, participation in the app-based program was independently associated with higher odds of continence recovery (odds ratio 13.80, 95%-confidence interval 3.22–59.12; p < 0.001). Continence at 30 days and continence-related QoL favored the intervention, whereas no significant differences were observed in erectile function at 90 days. Adherence to the PFMT was higher in the intervention group. Sensitivity analyses confirmed the robustness of the primary outcome. Conclusions: In this randomized controlled trial, a modular app-based PFMT program was associated with early continence recovery after prostatectomy compared with the standard-of-care physiotherapist-guided PFMT. Improved adherence, modular progression of exercises, and a more structured training delivery may have contributed to the effect. App-based PFMT might represent a scalable strategy to implement guideline-recommended supportive care. These findings warrant confirmation in studies with a longer follow-up. Full article
(This article belongs to the Special Issue Artificial Intelligence in Urological Oncology: Applications in Imaging, Prognostics, and Precision Disease Management)
13 pages, 233 KB  
Article
Quality and Usability of Prostate Cancer Information Generated by Artificial Intelligence Chatbots: A Comparative Analysis
by Abdullah Al-Khanaty, Jordan Santucci, David Hennes, Niranjan Sathianathen, Carlos Delgado, Karan Sharma, Eoin Dinneen, Kieran Sandhu, David Chen, Renu Eapen, Daniel Moon, Gregory Jack, Jeremy Goad, Shankar Siva, Muhammad Ali, Damien Bolton, Nathan Lawrentschuk, Declan G. Murphy and Marlon Perera
Cancers 2026, 18(6), 906; https://doi.org/10.3390/cancers18060906 - 11 Mar 2026
Viewed by 555
Abstract
Background: Artificial intelligence chatbots are increasingly used by patients to obtain health information, including for prostate cancer. While these platforms offer accessible and conversational responses, concerns remain regarding the quality, usability, and clinical relevance of AI-generated content. This study comparatively evaluated patient-directed prostate [...] Read more.
Background: Artificial intelligence chatbots are increasingly used by patients to obtain health information, including for prostate cancer. While these platforms offer accessible and conversational responses, concerns remain regarding the quality, usability, and clinical relevance of AI-generated content. This study comparatively evaluated patient-directed prostate cancer information generated by commonly used AI chatbots. Methods: Standardised prostate cancer-related prompts were developed using Google Trends and authoritative healthcare resources. Identical queries were submitted to five publicly accessible AI chatbots: ChatGPT 5.2, Google Gemini, Claude AI, Microsoft Copilot, and Perplexity. Responses were independently assessed by two blinded reviewers using the DISCERN instrument for information quality and the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) for understandability and actionability. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Readability was evaluated using the Flesch–Kincaid Reading Ease score. Descriptive statistics were used for comparative and pooled analyses. Results: Overall information quality was moderate, with a pooled median (interquartile range [IQR]) DISCERN score of 56.5 (53.0–61.0). Higher mean DISCERN scores were observed for ChatGPT 5.2 and Microsoft Copilot, whereas lower scores were observed for Claude and Perplexity. PEMAT-P understandability was consistently high across platforms, with a pooled median (IQR) score of 91.7% (83.3–91.7%). In contrast, PEMAT-P actionability was uniformly poor, with a pooled median (IQR) score of 0% (0–0%). Readability analysis demonstrated moderate complexity, with a pooled median (IQR) Flesch–Kincaid Reading Ease score of 50.4 (49.2–52.5) and a median word count of 666 (657–1022). Inter-rater reliability was good for PEMAT understandability (ICC 0.841) and moderate for DISCERN (ICC 0.712). Conclusions: AI chatbots provide highly understandable but only moderately high-quality patient-directed prostate cancer information, with a consistent lack of actionable guidance. Although variation in content quality was observed across platforms, significant limitations remain in evidence transparency and practical patient support. Future development should prioritise integration of evidence-based resources and actionable decision-support tools to enhance the role of AI chatbots in prostate cancer education. Full article
(This article belongs to the Special Issue Artificial Intelligence in Urological Oncology: Applications in Imaging, Prognostics, and Precision Disease Management)
12 pages, 644 KB  
Article
Impact of Computational Histology AI Biomarkers on Clinical Management Decisions in Non-Muscle Invasive Bladder Cancer: A Multi-Center Real-World Study
by Vignesh T. Packiam, Saum Ghodoussipour, Badrinath R. Konety, Hamed Ahmadi, Gautum Agarwal, Lesli A. Kiedrowski, Viswesh Krishna, Anirudh Joshi, Stephen B. Williams and Armine K. Smith
Cancers 2026, 18(2), 249; https://doi.org/10.3390/cancers18020249 - 14 Jan 2026
Viewed by 1086
Abstract
Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG [...] Read more.
Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management. Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness. Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors. Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Urological Oncology: Applications in Imaging, Prognostics, and Precision Disease Management)
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