Purpose Anatomical segmentation is one of the biggest sources of uncertainty in the online adaptive radiotherapy workflow. The aim of this study was to investigate the relation between the estimated uncertainty in deep learning (DL)-based segmentation and the correctness of the segmentations. In addition, the ability to capture out-of-distribution (OOD) data with uncertainty estimation was tested. Materials and methods The Monte Carlo dropout method was applied to estimate the uncertainty of a DL model for magnetic resonance (MR)-guided radiotherapy prostate cancer images, trained to segment the clinical target volume (CTV), bladder, and rectum. The training/validation set consisted of 151 T2 MR scans from 26 patients, while the test set consisted of 65 scans from 10 patients. Predictive entropy (PE) was used to capture predictive (model and data) uncertainty. The PE distributions for correct and incorrect predictions were used to find a threshold value. Predicted segmentations with PE values above this threshold value were allocated to the “uncertain group,” and those below to the “certain group.” Dice scores were computed for both groups, using manual segmentations as ground truth. Mutual information (MI) was additionally used to capture epistemic (model) uncertainty as a means to separate in-distribution (ID) from OOD data. Balanced steady-state free precession MRI scans of 10 healthy volunteers were used as OOD data. Results The segmentation model obtained Dice scores of 85.7% for the CTV, 94.8% for the bladder, and 86.6% for the rectum. The highest PE values were found at the segmentation borders. Higher PE threshold values resulted in better separation between the certain and uncertain groups. This shows the ability to detect incorrect predictions with uncertainty estimation. A 100% separation between ID and OOD data was achieved with MI. Conclusion Uncertainty estimation from a DL-based segmentation model was seen to correlate with Dice scores for segmentation of MR-guided radiotherapy prostate cancer images. This implies that uncertainty estimation could be used to label the quality of the segmentations in the online adaptive radiotherapy workflow. Preliminary results showed that uncertainty estimation could be used to distinguish between ID and OOD data.

IntroductionPaget’s bone disease is typically seen in elderly patients, and bisphosphonate (BP) therapy is the first-line treatment for achieving remission. However, in patients with end-stage renal disease (ESRD), it may be overlooked due to its radiologic and biochemical resemblance to secondary hyperparathyroidism-related bone lesions. This case underscores the diagnostic and therapeutic difficulties that may arise when Paget’s disease coexists with ESRD.Clinical CaseA 61-year-old male shuttle driver from Türkiye, with a medical history of ESRD attributed to hypertensive nephropathy since 2017, was referred to the endocrinology clinic in October 2024. A referral was initiated for incidentally identified expansile lesion in the right femoral head, observed on imaging during follow-up for prostate adenocarcinoma, first diagnosed in 2021.The patient has had peritoneal dialysis since 2018 and has been diagnosed with secondary hyperparathyroidism, treated with cinacalcet due to rising parathormone levels during follow-up. He had no familial history of bone disease. He experienced no discomfort, morning stiffness, or trouble walking, localized bone pain, hearing loss, or history of fractures for three years.The physical examination was unremarkable, with the exception of a peritoneal dialysis catheter. Blood Pressure:140/90 mmHg, height:168 cm, weight:80 kg BMI:28,3 kg/m2 Table-1 showed laboratory results at the time of presentation.In 2021, a surveillance MRI for prostate cancer identified heterogeneous intramedullary bone marrow in the trochanteric and subtrochanteric regions, accompanied by irregular cortical thickening. The patient did not undergo a biopsy and was treated conservatively with radiotherapy and hormonal therapy for prostate cancer.In 2023, the patient resumed follow-up, and further evaluation of the bone lesion was conducted using a PET-CT scan due to his prostate malignancy. The scan revealed FDG uptake in the femoral neck and trochanteric region, indicating an active bone lesion. Bone scintigraphy conducted in August 2024 demonstrated significant uptake localized to the proximal right femur, consistent with Paget’s disease. (Figure-1)The assessment of bone mineral density showed:L1–L4: 1.423 g/cm², T-score: +1.5; left femoral neck: 1.361 g/cm², T-score: +2.2The differential diagnosis comprised Paget’s disease, secondary hyperparathyroidism with bone lesions, and metastatic bone disease. The imaging findings, absence of systemic symptoms, and presence of a monostotic lesion corroborated the diagnosis of Paget’s disease. Table -2 showed time course of laboratory findings.BP therapy was avoided due to the elevated risk of vascular and soft tissue calcification. A multidisciplinary discussion with nephrology was held to optimize management.ConclusionThis case highlights the infrequency of Paget’s disease among dialysis patients and the diagnostic difficulties arising from its similarities to Brown tumors of hyperparathyroidismFigure 1:Radiography & Scintigraphy of Paget’s disease at right femur, shown at the arrow point Table 1:Laboratory results at the time of presentation Table 2:Time course of laboratory findings

First online real-time motion-including prostate and bladder dose reconstruction during prostate radiotherapy.

Primary tumor and metastasis-directed treatment for oligometastatic prostate cancer: An umbrella review of meta-analyses.

Automated Classification of Adverse Events After Hydrogel Perirectal Spacer Insertion for Prostate Cancer Using Large Language Models.

Background and purposeIn MR‐guided adaptive radiotherapy (MRgART), adaptive strategies are currently mainly determined through subjective review of anatomical changes. Machine learning (ML) models based on deformable image registration (DIR) have been developed to predict type of adaptive strategy: adapt to position (ATP) versus adapt to shape (ATS). However, subjective review may result in sub‐optimal plans and DIR processing for ML models can be time‐consuming. This study aims to develop a deep learning (DL) model that uses images as input data for fast and accurate adaptive strategy selection.MethodsData from 180 fractions of 36 prostate cancer patients were used retrospectively for this study. The optimal adaptive strategy was determined between ATP and ATS according to dosimetric evaluation. A multi‐stage network method was proposed and used for adaptive strategy prediction. A DL‐based image registration (DLIR) network was first trained to register the reference image to the daily image. Then, a DL‐based adaptive strategy prediction (DLSP) model was constructed and trained using the encoder section of the trained DLIR network. Data from 24 patients were used for training, while the data from the remaining 12 patients formed the independent test set.ResultsThe DLSP model demonstrated high performance with an area under the curve (AUC) value of 0.861, and corresponding accuracy (ACC), sensitivity (SEN), and specificity (SPC) were 0.867, 0.898, and 0.727, respectively. The DL prediction process required approximately 2.5 min, representing a 5‐fold improvement in efficiency over the existing ML method.ConclusionsThe DL‐based model could provide fast and accurate adaptive strategy selection, which further improves the efficiency of the MRgART process.

PurposeThis study evaluated the impact of a commercial AI‐assisted contouring tool on intra‐ and inter‐observer variability in prostate radiation therapy and assessed the dosimetric consequences of geometric contour differences.MethodsTwo experienced radiation oncologists independently delineated clinical target volume (CTV) and organs at risk (OARs) for prostate cancer patients. Manual contours (Cman) and AI‐generated contours (CAI) were compared with adjusted AI contours (CAI,adj). A consensus reference (Cref) served as the benchmark. To evaluate clinical impact, treatment plans were recalculated and replanned on each contour set under identical beam geometries to assess dose–volume histogram (DVH) parameters.ResultsAI‐assisted contouring significantly improved both intra‐ and inter‐observer agreement. Inter‐observer analysis revealed that the Dice similarity coefficient (DSCs) for CTV increased from 0.78 (± 0.11) for Cman to 0.89 (± 0.09) for CAI, adj. Similarly, intra‐observer analysis revealed that both oncologists showed significantly higher DSCs for CAI, adj compared to Cman. A thorough geometric comparison to the Cref revealed that while adjustments to CAI improved accuracy, they generally did not surpass Cman for CTV and rectum. Dosimetric analyses demonstrated that, under fixed plan geometry, both Cman and CAI,adj contours yielded lower planning target volume (PTV) D95% values compared with Cref, whereas after replanning, all plans met institutional criteria with no clinically significant differences among contour sets.ConclusionAI‐assisted contouring in prostate radiotherapy reduced intra‐ and inter‐observer variability and improved contouring consistency. However, CAI, adj did not consistently surpass Cman, especially for the CTV and rectum, where automation bias or selective clinical acceptance may have influenced edits. Fixed‐plan recalculations revealed dose differences from minor geometric deviations. These findings underscore the importance of structured quality assurance (QA) and human oversight to mitigate automation bias while leveraging AI's efficiency. The single‐institution design with two oncologists and one AI software limits generalizability, underscoring the need for multi‐observer validation.

The purpose of this study was to design a radiogenomics machine learning-DeepSurv model for biochemical recurrence-free (BCR-free) survival and treatment response (TR) prediction for radiotherapy (RT) of prostate cancer (PCa). In this study, radiomic features were extracted from pre and post treatment multiparametric MRI (mpMRI), including T2-weighted (T2W), diffusion-weighted MR imaging (DWI) and apparent diffusion coefficient (ADC). Also, genomic biomarkers such as Ki-67 (a cell proliferation marker reflecting tumor growth activity and also prognostic information in cancer progression), PTEN (tumor suppressor gene regulating cell growth and survival, have a prominent role for TR and cancer progression) and Decipher (a genomic signature predicting cancer recurrence risk and TR based on gene expression patterns) were collected. Radiomics feature selection and dimensionality reduction methods were employed, followed by training machine learning (ML) models. Moreover, time to event data and survival models including Random Survival Forest (RSF) and DeepSurv neural networks were used. For model performance, the concordance index (C-index) and integrated Brier score (IBS), and for improving interpretability, the SHapley Additive exPlanations (SHAP) were applied. Radiomic feature of MRI including Kurtosis demonstrated a near-perfect positive correlation with Ki-67 expression (r = 0.64), however skewness showed a strong negative correlation with PTEN status (r = -0.88). Entropy and kurtosis of MRI were also highly correlated with the Decipher genomic risk score (r = 0.90 and r = -0.96, respectively). The integrated ML-DeepSurve model performance overall F1-score was 0.93 for TR. The model also offered robust stratification for patients based on BCR-free survival probability. Our findings underscore the potential of radiogenomic signatures as non-invasive biomarkers to personalized PCa RT decisions and provide a novel clinically explainable predictive model based on radiomic and molecular biomarkers for BCR-free survival and TR of mentioned cancer.

IntroductionThe proximity of the rectum to the prostate in radiation therapy for prostate cancer presents a significant dosimetric challenge, leading to high rectal doses and resulting in detrimental side effects. Perirectal tissue spacing reduces rectal dose and gastrointestinal toxicities by mechanically separating these organs. We retrospectively compared balloon and PEG hydrogel spacers, focusing on spacer geometry, symmetry, rectal dosimetry, and GI/GU toxicity.MethodsSixty-seven men with localized prostate cancer treated with EBRT were analysed (balloon = 33; PEG hydrogel = 34). Symmetry was graded on axial CT at apex, mid-gland, and base with a five-tier midline scale (SYM-1 = optimal). Anteroposterior, laterolateral, and craniocaudal separations were measured. Rectal V60%–V100% were taken from dose–volume histograms. Acute (≤90 d) and late (>90 d) GI/GU toxicities were scored (CTCAE v4.0). Two-sided p ≤ 0.05 was significant.ResultsOptimal symmetry occurred in 33% (balloon) vs 14% (PEG hydrogel); asymmetry SYM-4/5 in 27% vs 24% (p = 0.21). At the apex, balloon spacers consistently created measurable separation, whereas 3 patients (9%) with PEG hydrogel demonstrated complete absence of spacing. Mean anteroposterior separation was larger with balloon at all levels (p < 0.001). Laterolateral differed inferiorly (2.4 cm vs 1.9 cm; p = 0.01). Craniocaudal length averaged 4.8 cm vs 4.3 cm (p < 0.001). Rectal V60–V100% showed no significant differences. Acute toxicity was low: GI grade 1 in 6% (balloon) vs 0%, with one grade 3 GI in PEG hydrogel; GU grade 1 in 13% vs 29%, grade 2 in 10% vs 7%. Late events: GI grade 2 in 0% vs 7%; GU grade 3 in one patient per cohort (~3%); other late toxicities mild and similar.ConclusionThe balloon spacer achieved greater, more uniform separation including improved apical symmetry, and showed fewer early GI events and lower mild acute GU rates, while rectal doses remained comparable. Prospective studies with longer follow-up are needed to confirm long-term benefit.

Deep learning (DL)-based auto-segmentation has rapidly become the state-of-the-art in radiotherapy planning, significantly reducing contouring time while achieving geometric accuracy comparable to expert-derived contours [1-3]. While AI contouring on CTp is now widely established, its application to cone-beam CT (CBCT) is less well explored, despite CBCT's critical role in daily image guidance for prostate radiotherapy. Current adaptive workflows rely on manual contouring or deformable image registration (DIR), both of which are resource-intensive and subject to limitations in accuracy and consistency. Recent advances in AI-based CBCT segmentation have shown promise in reducing manual workload, improving contour consistency, and supporting adaptive radiotherapy (ART) workflows [4]. To assess the clinical implications of these developments, this study retrospectively analyzed CBCT images from 20 prostate cancer patients, comparing AI- and DIR-generated contours to evaluate systematic differences and their potential impact on dosimetry and ART decision-making. Twenty prostate radiotherapy patients were retrospectively selected, treated with either 42.7 Gy in 7 fractions or 60 Gy in 20 fractions, and imaged on Halcyon linear accelerators using Hypersight CBCT ([Formula: see text]). AI-generated contours were produced with Limbus AI v1.8.0, while deformable image registration (DIR) contours were propagated from planning CTs in Velocity v4.2. Contour accuracy was assessed by two senior medical officers using a four-point Likert scale across 140 CBCTs. Prostate, bladder, and rectum were analyzed using Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), mean surface distance (MSD), center-of-mass (COM) displacement, and volumetric change relative to the planning CT. Dosimetric evaluation included [Formula: see text], [Formula: see text], [Formula: see text], and clinically defined organ-at-risk metrics to assess potential implications for adaptive radiotherapy. Statistical significance was tested using paired Student's t-tests and Wilcoxon signed-rank tests with a threshold of [Formula: see text]. AI-generated contours achieved acceptable clinical accuracy in >80% of cases, with fewer severe or medium errors compared to DIR-derived contours, which required minimal changes of 49%. Quantitative analysis demonstrated broadly comparable Dice Similarity Coefficients (DSC), Hausdorff Distance (HD), and mean surface distance (MSD) across prostate, bladder, and rectum. Organ variation on CBCT revealed larger mean centre of mass shifts and volume differences for AI, particularly in bladder contours, whereas DIR showed smaller systematic deviations. Dosimetric comparisons highlighted that prostate dose metrics were significantly different between methods, while bladder differences were mostly non-significant except at high-dose volumes, and rectum analysis revealed consistent statistically significant variations. Overall, although both methods captured daily anatomical changes, suggesting complementary strengths depending on adaptive radiotherapy application. AI-generated contours for prostate radiotherapy on CBCT images demonstrate high geometric accuracy and clinical usability, requiring minimal expert correction, while DIR contours, although generally usable, show greater variability, particularly for organs subject to large anatomical changes such as the bladder and rectum. Despite similar geometric comparisons, statistically significant dosimetric differences highlight the importance of careful expert verification, especially for sensitive structures like the rectum. These findings support the integration of AI-based contouring into adaptive radiotherapy workflows to streamline clinical processes, reduce workload, and maintain treatment accuracy, while emphasizing that automated contours, whether AI- or DIR-derived, should always undergo expert review to ensure safe and effective patient care.

ABSTRACTBackgroundRectal spacers have been shown in clinical studies to reduce side effects of radiotherapy (RT) in prostate cancer (PCa) patients. In addition, secondary analyses also showed reduced erectile dysfunction following PCa RT with the use of a rectal spacer. However, this association with erectile dysfunction (ED) in large‐scale real‐world settings remains unexplored. This study evaluated the association between rectal spacer use and the prevalence of ED diagnosis among PCa patients receiving prostate RT at the US county level.MethodsThis study utilized Medicare 5% and 100% Standard Analytic Files to analyze county‐level data. The analytical sample included adult PCa patients receiving RT—comprising stereotactic body radiation therapy (SBRT), intensity‐modulated radiation therapy (IMRT), proton beam radiation therapy, and brachytherapy—between January 2015 and March 2023. The primary outcome was the county‐level proportion of RT patients diagnosed with ED between January 2015 and March 2023. The primary explanatory variable was the proportion of patients receiving prostate RT utilizing a rectal spacer from 1 to 5 years prior to an ED diagnosis. Zero‐inflated Poisson regression models were used to assess the association between rectal spacer use and ED prevalence at the county level, controlling for county‐level PCa patient characteristics (median age and racial composition) and general population characteristics (median age, racial composition, and median household income). State‐level fixed effects accounted for regional variation. Data for general population characteristics were obtained from the 2020 Agency for Healthcare Research and Quality Social Determinants of Health Database.ResultsThe study included 247,250 PCa patients who underwent RT across 3132 US counties between January 2015 and March 2023. The average annual prevalence of ED among PCa patients receiving RT at the county level was 1.3%. During the study period, the proportion of patients receiving rectal spacers increased from 2.9% to 18.9%. After adjusting for confounders, counties with higher rectal spacer use 4–5 years prior had a significantly lower prevalence of ED: a 10‐percentage point increase in rectal spacer use at the county level was associated with a 7.7% relative reduction in ED prevalence after 4 years (p < 0.001) and an 8.4% reduction after 5 years (p = 0.006).ConclusionThis is the first large‐scale real‐world analysis to demonstrate an association between rectal spacer use and ED prevalence among PCa patients undergoing RT. County‐level analysis suggests that increased use of rectal spacing among PCa patients receiving RT is associated with a significantly lower prevalence of ED, with benefits emerging after a 4–5‐year time lag. These findings support the long‐term benefit of rectal spacer use in preserving sexual function in PCa patients undergoing prostate RT. Future research should evaluate the etiology of the delayed benefit observed in this study.

For de-novo oligometastatic prostate cancer (omHSPC) treated with standard of care androgen suppression and prostate radiotherapy, the patterns of progression vis-a-vis index metastatic sites are not well understood. This single-centre study included patients with de-novo omHSPC (CHAARTED criteria) staged with a PSMA-PET/CT scan at diagnosis, treated with systemic therapy and prostate radiotherapy, and re-staged with PSMA-PET/CT at biochemical progression. Disease status at index oligometastases was noted at progression. From 2015 to 2024, 79 patients with omHSPC were found eligible (M1a = 22, M1b = 57). Over a median follow-up of 39 months (IQR 28-69), 15 patients (19%) had disease progression. Restaging PSMA-PET/CT revealed progression of the index oligometastases for 11/15 patients (73%), with additional metastases in 7 of these. The high proportion of progression at the index oligometastases supports the potential benefit of metastasis-directed therapy for local ablation.

Abstract Introduction Erectile dysfunction (ED) and urinary incontinence are debilitating complications following treatment for genitourinary malignancies. Following radical prostatectomy or cystectomy, ED affects 62% to 89% of patients.[1-3] Even with nerve-sparing techniques, recovery of erectile function is highly variable, with reported rates ranging from under 50% to 79%.[4] Multiple evidence-based ED therapies (e.g., PDE5 inhibitors, medicated urethral system for erection intraurethral suppositories, intracavernosal injections (ICI), and penile implants) are included in AUA guidelines [5],and have demonstrated efficacy in clinical trials and meta-analyses.[6-8] Despite this, their use and timing in routine clinical practice remain poorly understood. Objective This study investigates how prior prostate or bladder cancer treatments influence (1) the likelihood of receiving first-, second-, and third-line ED therapies and (2) the time to initiation of each therapy. Methods We conducted a retrospective cohort analysis of 1,125,295 men newly diagnosed with ED in the AUA Quality (AQUA) registry from 2014 to 2023. Patients were required to have at least one year of documented follow-up. Subgroups included those with prior radical prostatectomy (n=55 594), radiation (n=7 808), brachytherapy (n=675), radical cystectomy (n=1 037), or ablation (n=504). We compared treatment-type frequencies and median days to initiation (interquartile range) using chi-square, two-tailed t-tests, and z-tests. Odds ratios for receipt of ED therapies were estimated via unadjusted logistic regression. Significance was set at p&amp;lt;0.05. Results Of the 1,125,295 men with erectile dysfunction, 4.9% had previously undergone prostatectomy, 0.69% prostate radiation, 0.06% brachytherapy, 0.04% ablation, and 0.1% radical cystectomy. Those with prostatectomies or ablations were significantly more likely to use PDE5i therapy compared to those without (43.0% vs. 40.8%, p&amp;lt;0.001). In contrast, PDE5i use was significantly lower among patients with prior brachytherapy (34.8% vs. 40.9%, p=0.0013) and radical cystectomy (29.1% vs. 40.9%, p&amp;lt;0.001) compared to those without. Use of ED therapy varied significantly by cancer treatment history. Patients with prior prostatectomy (OR 2.37, p&amp;lt;0.001), prostate radiation (OR 1.67, p&amp;lt;0.01), and radical cystectomy (OR 2.09, p=0.022) had higher odds of receiving intraurethral suppositories, or intracavernosal injections compared to the overall cohort seeking ED care. Progression to advanced ED therapies (e.g., penile prosthesis) were particularly noticeable in those with prior prostatectomy (OR 2.81, p&amp;lt;0.001) and radical cystectomy (OR 4.57, p&amp;lt;0.001). However, all groups with a history of cancer treatment were significantly more likely to have undergone penile prosthesis. Median time to treatment was significantly delayed across all modalities in those with a prior history of prostatectomy vs without. Median time to ED therapy by cancer treatment history are provided in Figure 1. Conclusions Although outcomes for prostate and bladder cancer continue to improve, many survivors experience erectile dysfunction that is refractory to oral therapy. Men with a history of prostatectomy or radiation are significantly more likely to require treatment escalation to second- or third-line interventions. Despite their high need, patients often encounter delays in initiating appropriate treatment. It is unclear whether earlier education on post-treatment erectile dysfunction and pre-treatment counseling on sexual side effects can help reduce delays in starting recommended treatments for those with more severe dysfunction. Disclosure No

Association between prostate radiotherapy and survival among patients with metastatic prostate cancer by extent of disease burden.

Cone-beam computed tomography (CBCT) is commonly utilized in radiation therapy to visualize soft tissues and bone structures. This study aims to develop a machine learning model that predicts optimal, patient-specific CBCT doses that minimize radiation exposure while maintaining soft tissue image quality in prostate radiation therapy. Phantom studies evaluated the relationship between dose and two image quality metrics: image standard deviation (SD) and contrast-to-noise ratio (CNR). In a prostate-simulating phantom, CNR did not significantly decrease at doses above 40% compared to the 100% dose. Based on low-contrast resolution, this value was selected as the minimum clinical dose level. In clinical image analysis, both SD and CNR degraded with decreasing dose, consistent with the phantom findings. The structural similarity index between CBCT and planning computed tomography (CT) significantly decreased at doses below 60%, with a mean value of 0.69 at 40%. Previous studies suggest that this level may correspond to acceptable registration accuracy within the typical planning target volume margins applied in image-guided radiotherapy. A machine learning model was developed to predict CBCT doses using patient-specific metrics from planning CT scans and CBCT image quality parameters. Among the tested models, support vector regression achieved the highest accuracy, with an R2 value of 0.833 and a root mean squared error of 0.0876, and was therefore adopted for dose prediction. These results support the feasibility of patient-specific CBCT imaging protocols that reduce radiation dose while maintaining clinically acceptable image quality for soft tissue registration.

Establishing BMI-specific diagnostic reference levels for radiotherapy planning CT in breast and prostate cancer: A retrospective Portuguese study.

To Space or Not to Space: The EPIC Question for Prostate Stereotactic Radiotherapy (SBRT) with or without Hydrogel Rectal Spacer (RS).

We report a 72-year-old man who developed secondary rectal cancer 17 years after receiving brachytherapy for prostate cancer. The patient had undergone gastrectomy for gastric cancer 4 years earlier. Elevated tumor markers during follow-up prompted further investigation, revealing a rectal tumor invading the prostate. Robot-assisted total pelvic exenteration was performed after neoadjuvant chemotherapy. Histopathological examination confirmed advanced rectal adenocarcinoma invading the bladde(r ypT4bN3M0, ypStage Ⅲc). Although secondary malignancies following prostate radiotherapy are rare, our case met the established criteria for radiation-induced cancer;history of radiation exposure, latency period >4 years, histological differences from the primary tumor, and origin in previously normal tissue. Literature review indicates that, although uncommon, the incidence of secondary pelvic malignancies increases 15-20 years after radiotherapy, with a higher frequency reported following brachytherapy compared to radical prostatectomy. Our case highlights the importance of long- term follow-up in patients treated with pelvic radiotherapy because secondary cancers may emerge after decades. Ongoing surveillance may help detect such malignancies at earlier and more treatable stages, thereby improving patient outcomes.

This study aimed to evaluate the cost-effectiveness of the Prostate Cancer Patient Empowerment Program (PC-PEP), a six-month comprehensive intervention designed to enhance psychological well-being and reduce healthcare expenditures among prostate cancer patients. In a crossover randomized clinical trial of 128 men aged 50-82 years scheduled for curative prostate cancer surgery or radiotherapy (± hormone treatment), 66 men received the PC-PEP intervention immediately, while 62 were randomized to a waitlist control arm and received standard care for six months before receiving PC-PEP. The intervention included daily activities targeting physical fitness, pelvic floor training, stress management, intimacy, social support, and dietary guidance. Cost-effectiveness was assessed from a healthcare payer perspective using billing data from Nova Scotia's Medical Services Insurance (MSI) and self-reported outcomes. Incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves (CEACs) were calculated using bootstrapped samples. Psychological distress was assessed with the Kessler Psychological Distress Scale (K10), while quality-adjusted life years (QALYs) were estimated from SF-6D utility scores. PC-PEP resulted in cost savings of $411.53 CAD per patient at six months, with a 30% reduction in clinically significant psychological distress and a QALY gain of 0.013. At 12 months, savings increased to $660.89 CAD per patient, preventing 31% of distress cases and yielding a QALY gain of 0.034. These outcomes demonstrate that PC-PEP is a dominant intervention, achieving both improved clinical outcomes and reduced healthcare expenditures. PC-PEP is a dominant, cost-effective strategy that significantly improves psychological well-being while lowering healthcare costs. Early implementation following prostate cancer diagnosis is strongly recommended to maximize both clinical and economic benefits.

Cystitis After Prostate Surgery and Radiotherapy: a Narrative Review